Pub Date : 2021-06-07DOI: 10.1109/DTS52014.2021.9498108
Boutana Ilhem, Bahloul Abdenour, Boukendir Souhil
Electromagnetic pulse welding is a new and very innovative production process. The working principle of the magnetic pulse welding (MPW) process is based on the use of electromagnetic forces to deform and to weld workpieces. Since this sophisticated welding process doesn't use heat to realise the weld, it offers important advantages with regard to the conventional welding techniques. The present study investigates the weldability parameters of joining cylindrical or flat workpieces using MPW process, particularly in industrial applications. The present study deals with numerical modelling of the MPW process applied to plates and tubes. The electromagnetic and mechanical models were coupled using FEM software COMSOL Multiphysics.
{"title":"Numerical investigation on weldability of workpieces using magnetic pulse welding process","authors":"Boutana Ilhem, Bahloul Abdenour, Boukendir Souhil","doi":"10.1109/DTS52014.2021.9498108","DOIUrl":"https://doi.org/10.1109/DTS52014.2021.9498108","url":null,"abstract":"Electromagnetic pulse welding is a new and very innovative production process. The working principle of the magnetic pulse welding (MPW) process is based on the use of electromagnetic forces to deform and to weld workpieces. Since this sophisticated welding process doesn't use heat to realise the weld, it offers important advantages with regard to the conventional welding techniques. The present study investigates the weldability parameters of joining cylindrical or flat workpieces using MPW process, particularly in industrial applications. The present study deals with numerical modelling of the MPW process applied to plates and tubes. The electromagnetic and mechanical models were coupled using FEM software COMSOL Multiphysics.","PeriodicalId":158426,"journal":{"name":"2021 IEEE International Conference on Design & Test of Integrated Micro & Nano-Systems (DTS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131247894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-07DOI: 10.1109/DTS52014.2021.9498080
Seifeddine Messaoud, Abbas Bradai, Samir Dawaliby, Mohamed Atri
Industry 4.0 is considered as a very promising paradigm for Industrial Internet of Things (IIoT) that will significantly impact current industries and the construction of upcoming ones due to its various use cases. The latter have heterogeneous quality of service (QoS) requirements which imposes important challenges in enabling these applications over a single IIoT infrastructure. In this paper, we propose an SDN-based architecture for Industry 4.0 as well as a dynamic slicing admission and resource reservation method based on online machine learning tools to provide flexibility in managing network resources while avoiding performance degradation of urgent IIoT traffic with network slicing. Simulation results, implemented over NS3 network simulator, highlights the efficiency of our proposed method in avoiding resources starvation and providing QoS for devices by respecting the defined delay thresholds and decreasing energy consumption.
{"title":"Slicing Optimization based on Machine Learning Tool for Industrial IoT 4.0","authors":"Seifeddine Messaoud, Abbas Bradai, Samir Dawaliby, Mohamed Atri","doi":"10.1109/DTS52014.2021.9498080","DOIUrl":"https://doi.org/10.1109/DTS52014.2021.9498080","url":null,"abstract":"Industry 4.0 is considered as a very promising paradigm for Industrial Internet of Things (IIoT) that will significantly impact current industries and the construction of upcoming ones due to its various use cases. The latter have heterogeneous quality of service (QoS) requirements which imposes important challenges in enabling these applications over a single IIoT infrastructure. In this paper, we propose an SDN-based architecture for Industry 4.0 as well as a dynamic slicing admission and resource reservation method based on online machine learning tools to provide flexibility in managing network resources while avoiding performance degradation of urgent IIoT traffic with network slicing. Simulation results, implemented over NS3 network simulator, highlights the efficiency of our proposed method in avoiding resources starvation and providing QoS for devices by respecting the defined delay thresholds and decreasing energy consumption.","PeriodicalId":158426,"journal":{"name":"2021 IEEE International Conference on Design & Test of Integrated Micro & Nano-Systems (DTS)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115760699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-07DOI: 10.1109/DTS52014.2021.9497988
Meriam Dhouibi, A. K. Ben Salem, S. Ben Saoud
Convolutional Neural Networks (CNNs) are particularly precise in several fields, especially computer vision where image classification is one of the most researched and commercialized application. Deploying these models on embedded devices requires high throughput and low latency even with limited resources and energy budgets. The complexity of the architecture of CNN models implies a very high computation cost. We are looking in this paper for determining the optimal topology (the number of layers and the number of neurons per layer) that allows us to reduce the model and deploy it in embedded platforms. We have proposed a small CNN architecture that achieves high level accuracy 81.50% on CIFAR-10 with fewer parameters based on the growing approach. For more optimization we used the pruning technique and the results showed that with more optimal architecture we obtained 82.43% accuracy and 15% reduction of the number of parameters.
{"title":"Optimization of CNN model for image classification","authors":"Meriam Dhouibi, A. K. Ben Salem, S. Ben Saoud","doi":"10.1109/DTS52014.2021.9497988","DOIUrl":"https://doi.org/10.1109/DTS52014.2021.9497988","url":null,"abstract":"Convolutional Neural Networks (CNNs) are particularly precise in several fields, especially computer vision where image classification is one of the most researched and commercialized application. Deploying these models on embedded devices requires high throughput and low latency even with limited resources and energy budgets. The complexity of the architecture of CNN models implies a very high computation cost. We are looking in this paper for determining the optimal topology (the number of layers and the number of neurons per layer) that allows us to reduce the model and deploy it in embedded platforms. We have proposed a small CNN architecture that achieves high level accuracy 81.50% on CIFAR-10 with fewer parameters based on the growing approach. For more optimization we used the pruning technique and the results showed that with more optimal architecture we obtained 82.43% accuracy and 15% reduction of the number of parameters.","PeriodicalId":158426,"journal":{"name":"2021 IEEE International Conference on Design & Test of Integrated Micro & Nano-Systems (DTS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125978723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-07DOI: 10.1109/DTS52014.2021.9498219
Paula Angarita Rivera, Mark D. Woollam, Amanda P. Siegel, Mangilal Agarwal
Utilizing chemiresistive gas sensors for volatile organic compound (VOC) detection has been a growing area of investigation in the last decade. VOCs have been extensively studied as potential biomarkers for biomedical applications as they are byproducts of metabolic pathways which are dysregulated by disease. Therefore, sensor arrays have been fabricated in previous studies to detect VOC biomarkers. In the process of testing these sensors, it is highly advantageous to quantify the concentration of the VOC biomarkers with high accuracy to diagnose the disease with high sensitivity and specificity. To investigate, analyze, and understand the relation between the concentrations of the VOC to the sensor resistance response, Gaussian Process (GP) models were implemented to predict the behavior of the data with respect to the resistance when the sensor is exposed to a range of concentrations of VOCs. Additionally, the relation between the concentration and resistance of the sensor was studied to predict the concentration of the VOC when a resistance is obtained. Monte Carlo Simulation Sampling from the GP model was utilized to generate data to further understand the trend. The results demonstrated that the relation between the concentration and resistance is linear. The model was tested with sampling data and its accuracy was evaluated.
{"title":"Gaussian Process Regression and Monte Carlo Simulation to Determine VOC Biomarker Concentrations Via Chemiresistive Gas Nanosensors","authors":"Paula Angarita Rivera, Mark D. Woollam, Amanda P. Siegel, Mangilal Agarwal","doi":"10.1109/DTS52014.2021.9498219","DOIUrl":"https://doi.org/10.1109/DTS52014.2021.9498219","url":null,"abstract":"Utilizing chemiresistive gas sensors for volatile organic compound (VOC) detection has been a growing area of investigation in the last decade. VOCs have been extensively studied as potential biomarkers for biomedical applications as they are byproducts of metabolic pathways which are dysregulated by disease. Therefore, sensor arrays have been fabricated in previous studies to detect VOC biomarkers. In the process of testing these sensors, it is highly advantageous to quantify the concentration of the VOC biomarkers with high accuracy to diagnose the disease with high sensitivity and specificity. To investigate, analyze, and understand the relation between the concentrations of the VOC to the sensor resistance response, Gaussian Process (GP) models were implemented to predict the behavior of the data with respect to the resistance when the sensor is exposed to a range of concentrations of VOCs. Additionally, the relation between the concentration and resistance of the sensor was studied to predict the concentration of the VOC when a resistance is obtained. Monte Carlo Simulation Sampling from the GP model was utilized to generate data to further understand the trend. The results demonstrated that the relation between the concentration and resistance is linear. The model was tested with sampling data and its accuracy was evaluated.","PeriodicalId":158426,"journal":{"name":"2021 IEEE International Conference on Design & Test of Integrated Micro & Nano-Systems (DTS)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133937626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-07DOI: 10.1109/DTS52014.2021.9498187
M. H. Raouadi, O. Touayar
Pyroelectric sensors are designed as multi-layer structure consisting essentially of an absorbent layer, a pyroelectric film and a radiator substrate; we modify this structure by varying the thickness of the absorbent layer, measured by ellipsometer technique, and the nature of the substrate. Initially the pyroelectric signal is measured using a look-in amplifier type SR 830, and thereafter the sensor is placed in a conventional circuit; then we study the evolution of the current -voltage response, collected on an oscilloscope and saved in a excel file, with the nature of the substrate and for a given thickness of the absorbent layer. The results are discussed in terms of the evolution of charging time, the discharging time and the amplitude of the pyroelectric sensor response as function of the thermal conductivity of the substrate.
{"title":"Substrate effect on the evolution of the current-voltage response of a pyroelectric sensor","authors":"M. H. Raouadi, O. Touayar","doi":"10.1109/DTS52014.2021.9498187","DOIUrl":"https://doi.org/10.1109/DTS52014.2021.9498187","url":null,"abstract":"Pyroelectric sensors are designed as multi-layer structure consisting essentially of an absorbent layer, a pyroelectric film and a radiator substrate; we modify this structure by varying the thickness of the absorbent layer, measured by ellipsometer technique, and the nature of the substrate. Initially the pyroelectric signal is measured using a look-in amplifier type SR 830, and thereafter the sensor is placed in a conventional circuit; then we study the evolution of the current -voltage response, collected on an oscilloscope and saved in a excel file, with the nature of the substrate and for a given thickness of the absorbent layer. The results are discussed in terms of the evolution of charging time, the discharging time and the amplitude of the pyroelectric sensor response as function of the thermal conductivity of the substrate.","PeriodicalId":158426,"journal":{"name":"2021 IEEE International Conference on Design & Test of Integrated Micro & Nano-Systems (DTS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132699627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-07DOI: 10.1109/DTS52014.2021.9497976
Aurelien T. Mozipo, J. Acken
The ability to attack Advanced Encryption Standard (AES) algorithms in the last round has been shown to be possible by enumerating the key guesses one byte at a time. However, attacking the first round does not lend itself to such a technique because of the presence of the mix-column layer. We demonstrate an attack on the 1st round of AES encryption by defining a leakage function based on the full key, and then we apply correlation power analysis to successfully uncover the encryption key. The success rate, defined by the Euclidian distance fluctuation is 0.788, which is higher than similar applications in the current literature. We also introduce the concept of Kullback-Leibler entropy as a distinguisher for discriminating between the power measurements and the estimated power of the key guesses. We demonstrate this as a new way of reducing the key search space via key ranking with applications for power side-channel attacks on the implementations of AES algorithms in an FPGA.
{"title":"Power Side Channel Attack of AES FPGA Implementation with Experimental Results using Full Keys","authors":"Aurelien T. Mozipo, J. Acken","doi":"10.1109/DTS52014.2021.9497976","DOIUrl":"https://doi.org/10.1109/DTS52014.2021.9497976","url":null,"abstract":"The ability to attack Advanced Encryption Standard (AES) algorithms in the last round has been shown to be possible by enumerating the key guesses one byte at a time. However, attacking the first round does not lend itself to such a technique because of the presence of the mix-column layer. We demonstrate an attack on the 1st round of AES encryption by defining a leakage function based on the full key, and then we apply correlation power analysis to successfully uncover the encryption key. The success rate, defined by the Euclidian distance fluctuation is 0.788, which is higher than similar applications in the current literature. We also introduce the concept of Kullback-Leibler entropy as a distinguisher for discriminating between the power measurements and the estimated power of the key guesses. We demonstrate this as a new way of reducing the key search space via key ranking with applications for power side-channel attacks on the implementations of AES algorithms in an FPGA.","PeriodicalId":158426,"journal":{"name":"2021 IEEE International Conference on Design & Test of Integrated Micro & Nano-Systems (DTS)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133468875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-07DOI: 10.1109/DTS52014.2021.9497993
Belgacem Nassima, Belgacem Wahiba, Abri Mehadji
In This paper, we propose a new broadband monopole antenna design for satellite applications operating at S and C band covering the frequency range from [2.32 to 5.70 GHz ,it can be also used for 4G mobile bands, some WiFi bands, as well as IoT & ISM 2.4 and 5GHz bands especially for video transmission. The monopole antenna is designed with FR-4 substrate overall size of 34 x 40x1.5 mm3, a dielectric constant of 4.3 and loss tangent of 0.025. In study parameter, a new miniaturization technics have been used in the middle of a patch is a rectangular slot to create the broadband resonance at S and C bands, in the next step we used two circular slot symmetry to rectangular slot inserted in the middle of patch to improve the return loss adaptation and to adjust the desired frequencies with optimization of the parameters of slots; The monopole antenna proposed exhibits a good return loss (S11) values of –29.54 dB at 2.7 GHz and −45 dB at 5,1GHz, the best efficiency of 84 %, a good VSWR less than 2, and the positive gain is between 1.8 to 4 dBi on the bandwidth of operation, a best simulations results are obtained and improved with CST software.
{"title":"New S / C band Monopole antenna design for satellite application","authors":"Belgacem Nassima, Belgacem Wahiba, Abri Mehadji","doi":"10.1109/DTS52014.2021.9497993","DOIUrl":"https://doi.org/10.1109/DTS52014.2021.9497993","url":null,"abstract":"In This paper, we propose a new broadband monopole antenna design for satellite applications operating at S and C band covering the frequency range from [2.32 to 5.70 GHz ,it can be also used for 4G mobile bands, some WiFi bands, as well as IoT & ISM 2.4 and 5GHz bands especially for video transmission. The monopole antenna is designed with FR-4 substrate overall size of 34 x 40x1.5 mm3, a dielectric constant of 4.3 and loss tangent of 0.025. In study parameter, a new miniaturization technics have been used in the middle of a patch is a rectangular slot to create the broadband resonance at S and C bands, in the next step we used two circular slot symmetry to rectangular slot inserted in the middle of patch to improve the return loss adaptation and to adjust the desired frequencies with optimization of the parameters of slots; The monopole antenna proposed exhibits a good return loss (S11) values of –29.54 dB at 2.7 GHz and −45 dB at 5,1GHz, the best efficiency of 84 %, a good VSWR less than 2, and the positive gain is between 1.8 to 4 dBi on the bandwidth of operation, a best simulations results are obtained and improved with CST software.","PeriodicalId":158426,"journal":{"name":"2021 IEEE International Conference on Design & Test of Integrated Micro & Nano-Systems (DTS)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128203984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-07DOI: 10.1109/DTS52014.2021.9497897
Mark D. Woollam, Paul Grocki, Paula Angarita Rivera, Amanda P. Siegel, F. Deiss, Mangilal Agarwal
Volatile organic compounds (VOCs) have been shown to be biomarkers of disease and are typically identified by gas chromatography-mass spectrometry (GC-MS) or gas sensor arrays (electronic nose, e-nose). GC-MS has the advantages of VOC structure elucidation, while the e-nose is easy to use and offers point of care applications. Current e-nose designs utilize a sensor array to adsorb a diverse set of VOCs, some of which are biomarkers. Because the sensor elements are not tuned for biomarkers, these devices must rely on machine learning to identify disease. Therefore, tuning the selectivity of the sensing layers to detect specific biomarkers may increase diagnostic accuracy. Solid phase microextraction (SPME) coupled to GC-MS may be a rapid and facile process to evaluate different sensing layers. To demonstrate this concept, SPME fibers of different compositions were fabricated (custom fibers) and compared to commercially available polyacrylate (PAA) fibers. Coatings were characterized by Fourier Transform Infrared spectroscopy and scanning electron microscopy. Custom, PAA and uncoated (negative controls) SPME fibers were utilized to extract a standard VOC mixture which was analyzed by GC-MS. PAA and custom fibers significantly outperformed the negative control, but PAA fibers were more sensitive. However, the custom fiber was nearly twice as selective to some VOCs and the PAA fiber was nearly twice as selective toward other VOCs. The results show that SPME GC-MS is an efficient method for testing/comparing the selectivity of sensing layers toward multiple VOCs in a single analytical run.
{"title":"Comparing the Selectivity of Solid Phase Microextraction Fibers to Detect Volatile Organic Compounds","authors":"Mark D. Woollam, Paul Grocki, Paula Angarita Rivera, Amanda P. Siegel, F. Deiss, Mangilal Agarwal","doi":"10.1109/DTS52014.2021.9497897","DOIUrl":"https://doi.org/10.1109/DTS52014.2021.9497897","url":null,"abstract":"Volatile organic compounds (VOCs) have been shown to be biomarkers of disease and are typically identified by gas chromatography-mass spectrometry (GC-MS) or gas sensor arrays (electronic nose, e-nose). GC-MS has the advantages of VOC structure elucidation, while the e-nose is easy to use and offers point of care applications. Current e-nose designs utilize a sensor array to adsorb a diverse set of VOCs, some of which are biomarkers. Because the sensor elements are not tuned for biomarkers, these devices must rely on machine learning to identify disease. Therefore, tuning the selectivity of the sensing layers to detect specific biomarkers may increase diagnostic accuracy. Solid phase microextraction (SPME) coupled to GC-MS may be a rapid and facile process to evaluate different sensing layers. To demonstrate this concept, SPME fibers of different compositions were fabricated (custom fibers) and compared to commercially available polyacrylate (PAA) fibers. Coatings were characterized by Fourier Transform Infrared spectroscopy and scanning electron microscopy. Custom, PAA and uncoated (negative controls) SPME fibers were utilized to extract a standard VOC mixture which was analyzed by GC-MS. PAA and custom fibers significantly outperformed the negative control, but PAA fibers were more sensitive. However, the custom fiber was nearly twice as selective to some VOCs and the PAA fiber was nearly twice as selective toward other VOCs. The results show that SPME GC-MS is an efficient method for testing/comparing the selectivity of sensing layers toward multiple VOCs in a single analytical run.","PeriodicalId":158426,"journal":{"name":"2021 IEEE International Conference on Design & Test of Integrated Micro & Nano-Systems (DTS)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125891522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1109/dts48731.2020.9196101
{"title":"Welcome to the IEEE DTS’21 Conference","authors":"","doi":"10.1109/dts48731.2020.9196101","DOIUrl":"https://doi.org/10.1109/dts48731.2020.9196101","url":null,"abstract":"","PeriodicalId":158426,"journal":{"name":"2021 IEEE International Conference on Design & Test of Integrated Micro & Nano-Systems (DTS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127646961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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