We analyze a narrow-beam low earth orbit (NB LEO) terrestrial-satellite uplink from the perspective of the typical satellite base station. The user equipment (UEs) are located on the earth's surface according to a homogeneous Poisson point process (PPP). We present a simplified planar system model and focus on the point process of the approximate antenna gains of each UE in the satellite base station's antenna pattern and analyze the signal-to-interference ratio (SIR), the SIR meta distribution (MD), the signal-to-interference-plus-noise ratio (SINR) and the throughput. We present simple analytical results, some in closed form, for the performance metrics. We validate the approximate system model by comparing the results to the Monte Carlo simulated performance metrics using the more accurate system model incorporating the earth's curvature. The paper includes insightful results that help to understand the quantitative and qualitative nature of the NB LEO satellite uplink.

This paper explore the Off-on Cycle ADC (OOC ADC), an innovative analog-to-digital conversion (ADC) technique that significantly simplifies the analog-to-digital conversion process by accomplishing it through just three steps: inputting the analog signal, triggering the probe’s dip-and-reset motion (PDR), and outputting the binary digital signal. Initially, the paper establishes a theoretical foundation for subsequent ADC design by introducing the Off-on Cycle principle (OOC principle) in binary encoding rules. Subsequently, it illustrates the design concepts and operational principles of the OOC ADC through the illustrative example of a 4-bit precision OOC ADC design. Finally, the paper compares the superior performance of the OOC ADC over existing ADCs in terms of conversion rate, resolution, process complexity, and power consumption. The research findings in this paper have the potential to drastically reduce the technical difficulties involved in analog-to-digital conversion, facilitate technological advancements in the realm of digital signal processing.

The Scenario-Based Model Predictive Control (SB-MPC) (SB-MPC) is an autonomous collision avoidance algorithm primarily designed for open and coastal waters. Over the years, the algorithm has shown significant promise in both simulations and real-world experiments. However, one Over the years, the algorithm has shown significant promise in both simulations and real-world experiments. However, one Over the years, the algorithm has shown significant promise in both simulations and real-world experiments. However, one One of the challenges in adapting SB-MPC for autonomous inland waterway collision avoidance is the inherent resolution insufficiency due inherent resolution insufficiency due inherent resolution insufficiency due inability to deriving solutions from deriving solutions from deriving solutions from use a finite set of discretized solutions using exhaustive search. To increase the resolution of the solution space, a derivative-based finite set of discretized solutions using exhaustive search. To increase the resolution of the solution space, a derivative-based finite set of discretized solutions using exhaustive search. To increase the resolution of the solution space, a derivative-based derivative based optimization strategy would be required. However, the would be required. However, the would be required. However, the due to non-smooth components of the SB-MPC of the SB-MPC of the SB-MPC in its cost function prohibit this approach. Therefore, function prohibit this approach. Therefore, function prohibit this approach. Therefore, function. Hence, we propose a newer variant of the newer variant of the newer variant of the novel algorithm, Smooth Scenario-Based Model Predictive Control (Smooth-SBMPC), (Smooth-SBMPC), (Smooth-SBMPC), (Smooth-SBMPC) specifically designed for the the the highly constrained and complex navigational environments inherent to inland waterways. It utilizes different techniques, such as using Fuzzy Logic, to convert the non-smooth components of the original SB-MPC cost function into smooth, derivable ones. It utilizes different techniques, such as using Fuzzy Logic, to convert the non-smooth components of the original SB-MPC cost function into smooth, derivable ones. It utilizes different techniques, such as using Fuzzy Logic, to convert the non-smooth components of the original SB-MPC cost function into smooth, derivable ones. The effectiveness of Smooth-SBMPC is validated through a comprehensive simulation study, offering offering offering providing insights into its performance in complex navigational environments.

High-Performance Computing (HPC) systems are designed for large-scale processing and complex dataset analysis leveraging scalability, efficiency, and parallelism, often integrating specialized hardware structures such as Vector Processing Units (VPUs). As these systems have grown in complexity and scale, their vulnerability to errors and failures has become an important and complex issue in the HPC world. Our research addresses this challenge by exploring and implementing advanced fault tolerance techniques inside the Vitruvius+ architecture, a partial out-of-order Vector Processing Unit. To the best of our knowledge, this is the first full RTL-level implementation of instruction replication in an HPC-class vector processor for reliability. Specifically, we investigate the integration and interaction of redundancy mechanisms inside the most sensitive architectural units, obtaining a reduction of 75% in non-silent faults causing system failure, proven by an extensive fault injection simulation campaign, with a hardware overhead of only 7.5% and a negligible variation in clock frequency.

A document by Hanan Abdulkarim. Click on the document to view its contents.

A document by Adham Sakhnini. Click on the document to view its contents.

A document by Astrid Barreiro. Click on the document to view its contents.

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.

This paper describes integration of a software defined antenna (SDA) and a software defined radio (SDR) to enable dynamic and automatic selection of physical propagation channels. The SDA, an agile aperture antenna (A3), provides monolithic microdiversity, i.e. multiple antenna states within a single structure and within a space having maximum dimension of approximately one wavelength. In this way physical channel selection occurs outside, and therefore augmenting, the front-end electronics. The independent controllable parameters for channel access include: frequency, polarization (full Poincaré sphere), and pattern (in two dimensions). Several different tests were conducted to maximize a quality figure of merit calculated by the radio. Both library-based and evolutionary search techniques were used, resulting in short term channel improvements on the order of 10 dB, and long term fully optimized improvements on the order of 20 dB.

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. This document is a dataset descriptor for the dataset of crude oil exports from OPEC nations to importing nations/regions from a period of (2016-2022) structured for easy formation of nodes and edges sourced from various sources referenced below also it contains the average closing price per barrel and the global demand of crude oil during a fiscal year to note and understand complex relations between the global oil trade.The data-set is available at https://dx.doi.org/10.21227/m8ds-nd06.The authors can be contacted for the access to dataset as well.

(This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.)In this work, we combine microwave dielectric spectroscopy and machine learning techniques to assess the authenticity of extra virgin olive oil (EVOO). We investigate four common adulterants of EVOO, namely soybean oil, corn oil, sunflower oil, and canola oil. We use a low-cost spiral shaped microwave resonator-based sensor operating at 546.8 MHz to detect changes in the complex permittivity of oil samples. A vector network analyzer (VNA) is used to extract complex scattering parameters S11 and S21 that serves as inputs for two artificial neural network models. The first model, using only the real and imaginary parts of S21, achieves an overall accuracy of 95.8% in predicting the applied adulterant in test samples. In contrast, the second model, incorporating the real and imaginary parts of both S11 and S21, attains a 100% accuracy for test samples. Additionally, we investigate the relationship between the measured |S21| (in dB) and the adulteration level, expressed as the percentile value of the volume of adulterant per volume of the sample (mL/mL). For each adulterant, a calibration equation is developed using partial least squares regression (PLSR) to predict adulteration levels up to 50%. The maximum root mean square error (RMSE) is 2.1% for canola oil adulteration prediction. PLSR yields RMSE values of 0.9% for soybean oil, 1.1% for corn oil, and 1.0% for sunflower oil adulteration. This methodology offers both qualitative and quantitative analyses of EVOO, capable of identifying adulterations as low as 5% with a simple, portable, and practical system.

In this paper, we propose a Passive Actuator-Less Gripper (PALGRIP) for picking a piece of fabric from a stack of fabric parts and placing the picked fabric part. The picking of a piece of fabric from a stack is a simple but difficult process to automate. The proposed gripper can pick a piece of fabric from the stack by simply pressing the fingertips of the gripper against the stack. The fingers are closed and opened by the relative motion between the fingers and the housing of the gripper. The grasping motion of the gripper is generated by two mechanisms: a passive pinching mechanism and a selflocking mechanism. These mechanisms allow the fingers to perform open and close movements and to maintain the fingers in either open or closed state. The kinematics of the mechanisms are analyzed to design the gripper. The relation between the movement of the fingers and the force required to operate the gripper is also investigated through static force analysis and the experiment. Finally, experiments using PALGRIP are conducted, and the experimental results illustrate how the pick-and-place operations are carried out using the prototype of PALGRIP. The proposed gripper allows the robot to automate fabric pick-andplace operations easily by attaching it to the robot's endpoint.

Soft optical transducers have the potential to fulfill the need for advanced tactile sensing in robotics. We present a fingertipshaped soft sensor with optically transparent channels that relies on soft materials and sensor morphology to measure an applied triaxial force. The proposed 3D-channeled sensor has a volume of 2.5 cm3 , and experimental results reveal a fifteen-fold increase in voltage compared to its bulk analogous, showcasing a sensitivity of 0.34 N/mV and 0.09 N/mV to tangential and normal forces. A prototype with a diameter of 2 mm (0.4x) indicates the feasibility of scaling down the sensor. Force magnitude is estimated with a linear model and then decomposed into its Fxy and Fz with an R2 of 0.93 and 0.98 within a sensing range of 4.05 N and 8.50 N, respectively. A coordinate transformation from a covariant to a cartesian reference frame is used to retrieve the direction of the tangential component of the force. The sensor was integrated into a compliant robotic hand as a proof-of-concept to demonstrate its real-time operation and suitability for grasping, paving the way for advancements in soft tactile sensors that can be embedded in soft robots.

A Distributed Nonlinear Model Predictive Control (DNMPC) approach is proposed to control the simplified decoupled dynamics of a quadrotor UAV. The performance of DNMPC is compared, in terms of tracking and execution time, to that of standard control configurations based on centralized MPC and PID control aiming to show the suitability of each configuration in terms of performance and the practicality of using a particular configuration in real-time applications. The results show the advantage of using DN-MPC in terms of ease of tuning and computational cost over more centralized feedback control approaches.

A document by Kolja Thormann . Click on the document to view its contents.

Symbolic planning algorithms and large language models have different strengths and weaknesses for story generation, suggesting hybrid models might leverage advantages from both. Others have proposed using a language model in combination with a partial order planning style algorithm to avoid the need for a handwritten symbolic domain of actions. This paper offers a complementary approach. We use a state space planning algorithm to plan coherent multi-agent stories in symbolic domains, with a language model acting as a guide to estimate which events are worth exploring first. We evaluate an initial implementation of this method on a set of benchmark problems and find that the LLM's guidance is helpful to the planner in most domains.

In this paper, we develop a method for automatically selecting groups of loops to fuse in an image processing data flow graph, here referred to as a "fusing configuration". The method is designed for use on Digital Signal Processors (DSP), many of which rely on statically scheduled Very Long Instruction Word architecture. Selection is guided by a heuristic instruction scheduler that serves as a performance model for a candidate configuration. We show that for synthetically generated graphs of size 2 to 10 nodes, this approach is capable of selecting the optimal fusing configuration in 80% of graphs and selects a configuration that achieves within 10% of the performance of the optimal configuration for 90% of graphs.

A tuning-parameter-free and matrix-inversionfree solution of nonlinear programming (NLP) problems is proposed. The key idea is to design an update law based on Lyapunov analysis to satisfy the first-order necessary conditions for optimality. To this aim, first, the Lyapunov function is defined as the summation of the norms of these conditions. Then, the desired optimization variables and Lagrange multipliers, which minimize the Lyapunov function the most, are found analytically, thereby rapidly approaching the necessary conditions. The proposed method neither requires tuning parameters nor matrix inversions; thus, it can be implemented easily with less iterations and computational load than conventional methods, such as sequential quadratic programming (SQP) and augmented Lagrangian method (ALM). The effectiveness of the proposed method is applied to and validated by using it to solve a nonlinear model predictive torque control (NMPTC) problem in electrical drives. The results are compared with those of SQP and ALM.