Quantification and analysis of global oil trade networks reveals deep insights into a nation's development and influence at a global scale. Further, it allows us to predict future trends and changes to adapt state policy as the crude oil market influences the balance of power among the developed and emerging economies alike as it is central for energy needs as well for industrial progress. In this paper we analyzed the crude oil export data from 2016 to 2022 from Organization of Petroleum Exporting Countries and their allies (OPEC+) using complex networks in order to quantify and create a comparison metric between the exporting and importing nations to identify changes in the trade network. Our analysis revealed that that subsequent to the COVID-19 pandemic, even after sizable economic recovery, Organization of Petroleum Exporting Countries (OPEC) is unable to maintain pre-pandemic levels of trade due to multiple factors such as the rise of the USA as the leading oil exporter [14] [15], global financial sanctions on Russian federation resulting in influx of discounted oil in the markets [16], shift of world towards renewable energy [17] etc. As entering 2022 with the recovery of global oil trade the increase was 8.09% as compared to the loss of 12.35% on the onset of 2020. Our study also opens up possible research work in this direction about other major non-OPEC oil exporters and quantify how their control is increasing over the market.

Reference Architectures (RAs) play an important role in the integration of 6G terrestrial and satellite networks. In this paper, we present the essence of the reference architectural roadmap as per the Edition-3 document of the Satellite Working Group of IEEE Future Networks Initiative (FNI).

The paper studies the method for online teaching using Multi-Track Modular Teaching (MT2). MT2 is an advanced teaching-learning method for better training of students with deep skills.  Covid19 pandemic and the associated lockdown periods, coupled with the requirements of social distancing, have played a major disruption in the academic processes across all the world’s universities. Due to the inability to conduct physical classes and lectures, most universities have opted for online classes using video conference tools. One way to make the computer networking classes, during lock-down periods, more effective is to use the Multi-Track Modular Teaching (MT2) technique which organizes a course into multiple tracks and modules. This paper presents the mod?ifications carried out to MT2-based computer networking course and experiences from teaching it online during the lock-down period. While the online classes offered an alter?native to the completely disrupted teaching and learning process, observations on their effectiveness reveal certain important considerations in order to make them more useful. Results from a feedback survey of the students who under?took the course is also provided in this paper, giving insights to certain difficulties and issues in the conduct of online classes in developing countries.

Due to the rapid development of satellite imaging sensors, high-resolution images are being generated for use. Various image processing algorithms, such as deep learning models, require images of reduced sizes given the computational constraints. Hence, preprocessing the images to reduce their size is crucial for any deep learning model. This paper proposes a novel approach to compress satellite images using quantum computing. A comparative study on different standard data embedding techniques used in quantum computing is undertaken. We propose four quantum compression techniques (𝑄𝐶𝑇s) by extending the unitary operations of amplitude encoding for compressing satellite images. The proposed methods provide exponential scaling as amplitude encoding is used, where 2𝑛 classical data values are encoded into 𝑛 qubits. Compression performance, visual evaluation, and quality metric evaluation were carried out to assess the proposed compression techniques. Our experimental results showed that the crucial patterns in images are retained in the compressed images without quality loss even after 75% compression. The compressed images can be used for post-processing tasks such as classification using classical or quantum computing algorithms.

Quantum Processed SAR and Optical Image Data for Deep Learning Applications

Deep learning techniques are very prominent in processing remotely sensed synthetic aperture radar (SAR) images for real-time, high-impact applications, such as image classification, object detection, and semantic segmentation. The accuracy of deep learning models, such as convolutional neural networks (CNNs), depends on the quality of the input data. Compared to the model-centric approach, where the model parameters are optimized during training, the data-centric approach can enhance the performance accuracy as data quality is improved before training the models. Improving the data quality of SAR images is challenging as SAR image properties are different from optical (OPT) images. Image fusion techniques proved to enhance the quality of SAR images when combined with OPT images. Many fusion techniques exist for combining SAR and OPT images in the classical domain. This paper proposes a novel approach to using quantum computing for the image fusion of SAR and OPT images. Eight different quantum processing techniques are used for the fusion of the images. We designed and created a dataset for land-use classification by collecting data using the Google Earth Engine. The quality metric measurements show that the quality of SAR images has improved by using the proposed quantum processing techniques. In addition, performance evaluation of the deep learning CNNs on the dataset was carried out for all quantum processing techniques. Our approach improved the classification accuracy from 82.64%, with only SAR images for training, to 95.36% using the proposed image fusion techniques.