Miracle-dz/UniFAR-FTUs huggingface dataset:
Oral Absorption of Middle-to-Large Molecules and Its Improvement, with a Focus on New Modality Drugs

To meet unmet medical needs, middle-to-large molecules, including peptides and oligonucleotides, have emerged as new therapeutic modalities. Owing to their middle-to-large molecular sizes, middle-to-large molecules are not suitable for oral absorption, but there are high expectations around orally bioavailable macromolecular drugs, since oral administration is the most convenient dosing route. Therefore, extensive efforts have been made to create bioavailable middle-to-large molecules or develop absorption enhancement technology, from which some successes have recently been reported. For example, Rybelsus® tablets and Mycapssa® capsules, both of which contain absorption enhancers, were approved as oral medications for type 2 diabetes and acromegaly, respectively. The oral administration of Rybelsus and Mycapssa exposes their pharmacologically active peptides with molecular weights greater than 1000, namely, semaglutide and octreotide, respectively, into systemic circulation. Although these two medications represent major achievements in the development of orally absorbable peptide formulations, the oral bioavailability of peptides after taking Rybelsus and Mycapssa is still only around 1%. In this article, we review the approaches and recent advances of orally bioavailable middle-to-large molecules and discuss challenges for improving their oral absorption.
Research on Qubit Mapping Technique Based on Batch SWAP Optimization

—The conventional approach for initial qubit mapping in the Noisy Intermediate-Scale Quantum (NISQ) era typically uses a static heuristic strategy, overlooking insufficient qubit neighborhood in subsequent operations, resulting in excess additional SWAP gates. To address this, we introduce a multifactor interaction cost function considering qubit distance, interaction time, and gate operation error rates, enhancing SWAP gate selection in the traditional strategy. Considering quantum hardware constraints, we propose Batch SWAP Optimization Strategy (BSOS). BSOS tackles qubit mapping challenges by leveraging optimal SWAP gate selection and a SWAP-based batch update technique, effectively minimizing SWAP gates throughout circuit execution. Experimental results show that BSOS significantly reduces additional gates by intelligently selecting SWAP gates and using batch updating, with a 38.1% average decrease in inserted SWAP gates, leading to a 12% reduction in hardware gate counting overhead.
Design of a Circularly-Polarized Miniaturized Microstrip Annular Ring Antenna

A design approach to obtain a miniaturization, semi-circular, slotted annular ring antenna with circularly-polarized (CP) radiation is presented in this paper. Firstly, the antenna is miniaturized on the basis of a traditional annular ring antenna, which can reduce the overall transverse size by nearly 50%. After that, good CP radiation characteristics is well achieved under a single-point feed by simply loading a horizontal sector slot on the annular ring. Simulation results show that the proposed antenna has a −10dB impedance bandwidth of 11.3% and an axial ratio bandwidth of 3.8%.
An Efficient Method to Evaluate the Influence of Additional Air Gaps on the Cogging Torque for the PM Machines with Modular Stator

Due to the uncertainties in the manufacturing process, additional air gaps (AAGs) between different stator segments in the modular stator motors are inevitable, which might deteriorate the cogging torque performance. To evaluate the influences of the uncertainties, a mass of finite element analysis (FEA) on designs featuring different uncertain combinations is always required. The correspondingly high computational cost is the biggest obstacle to uncertainties analysis and robust design. To solve this problem, an approach based on the worst–uncertain–combination–analysis (WUCA) method is proposed to theoretically find the possible worst-case under uncertainties. With the help of the WUCA approach, the influence of AAG uncertainties on the cogging torque could be estimated by only at most two specific combinations, while hundreds of uncertain comparisons are usually required if the conventional approach is adopted. The efficacy of the proposed method is verified by FEA results for different pole/slot configurations, indicating its high potential to reduce the high computational burden for robust optimization.
ACCLAIM: An End-to-End SystemC-AMS Simulation Framework for Analog In-Memory-Computing

This paper presents ACCLAIM, a novel framework based on SystemC-AMS for evaluating nonidealities in crossbar-based Analog In-Memory-Computing, offering a comprehensive and efficient alternative to SPICE-level simulations and analytical behavior-level simulators. ACCLAIM covers the end-to-end computing process. The dataflow of ACCLAIM is investigated and the accumulative effect of nonidealities is analyzed. Accuracy evaluation of LeNet is discussed based on the dataflow simulation.
Predefined- Time Robust Synchronization for Competitive Neural Networks with Fast-Slow Time Scales: Sliding-Mode Control Approach

In this paper, the predefined-time (PdT) synchro-nization for competitive neural networks (CNNs) exhibiting multiple time scales with parametric and external disturbances is investigated on the basis of the sliding-mode control. The new PdT stability theorems for the singular perturbation system are derived and the appropriate autonomous controllers and suitable integral sliding-mode surfaces are designed. Consequently, the PdT drive-response synchronization for the CNNs is established, independent of other additional algebraic conditions. The error states of drive-response CNNs can reach and keep on the designed surfaces within a PdT, causing the states to approach zero. Moreover, the PdT, as an arbitrary positive constant, is treated as a controller parameter, offering good flexibility in advance. Finally, the numerical example is provided to illustrate the effectiveness of the obtained main results.
Discontinuous PWM Modulation for Active Power Filters Operating in Disturbed Environments

Active power filters (APFs) are power converters able to compensate for the distorted current components injected into the mains by non-linear loads. To reduce the APF switching losses and thus to allow using higher switching frequencies, discontinuous pulse-width modulation (DPWM) techniques have been developed in the literature. However, the DPWM techniques applied to APFs are highly sensitive to the high frequency disturbances on the power lines. Therefore, this paper proposes a dedicated discontinuous modulation technique for APFs installed in disturbed environments, able to minimize the switching losses for any operating condition. The proposed solution has been experimentally validated on a 100 kVA 2-level APF inverter compensating a regenerative system used for the final functional test of industrial inverters at the end of the production line.
Quiet Zone Diagnostics in a Microwave Anechoic Chamber Using Imaging

A quiet zone (QZ) diagnostic technique for a microwave anechoic chamber has been demonstrated. It provides a comprehensive imaging view of the QZ's performance. The imaging for reflected signals from different angles and direct signals within the QZ provides the location of strong reflected signals accurately. This technology can perform a specific diagnosis of the QZ after it is found that the performance of the quiet zone does not meet the design requirements.
Optimal Scheduling of Multipath Multicast with In-network Cache for One-to-many Transfer

The need for fast and efficient one-to-many file transfers is growing with the rapid increase in traffic among distributed servers for replicating and distributing large files. Assuming centrally-managed Software Defined Networking (SDN) environments, the authors have been developing a framework of one-to-many file transfers for networks with full-duplex links by which every receiver can fully utilize its own max-flow from the sender throughout the file transfer duration. In our framework, a file is divided into multiple blocks that are transmitted to receivers on a set of multicast trees according to a block transmission schedule designed for a given network topology with the locations of the sender and the receivers (called MPMC). Our previous study proposed an extension of MPMC in which the sender generates additional coded blocks from the original blocks and transmits those blocks, but it may suffer from the processing delays of encoding and decoding at the sender and all the receivers. Therefore, this paper proposes a new extension of MPMC (called Rainbow-MPMC) by incorporating in-network cache at tailored switches, in which a block in transmission can be replaced (overwritten) at a switch on the way by another block previously passing the switch and cached, instead of just being forwarded to downstream. Through a variety of network topology examples, we verify that many different optimal sched-ules of Rainbow-MPMC can be generated in a given network, each of which minimizes every receiver's reception completion time simultaneously while requiring a different number of block replacements. We also showed that the minimum number of block replacements among different optimal schedules varies significantly by network even over similar-sized networks.
A Survey on Recent Intrusion Detection Systems in Cloud Environment

Cloud computing is widely used nowadays as it offers a scalable environment and flexible services; therefore, it becomes a preferred target to exploit by cybercriminals, posing threats to the confidentiality and integrity of shared data and the availability of the offered services, which implies a need for a mechanism that monitors the traffic over the network and detects the intrusion on the cloud. In this paper, we compare recent approaches to intrusion detection based on machine learning and deep learning classifiers trained and evaluated on different benchmark datasets to prevent cloud security issues.
Sensor-Centric Link Adaptation and Transmit Power Control for Energy-Efficient and Quality-of-Service WBANs

Due to the imbalanced power supply and computing capabilities between sensors and the coordinator in Wireless Body Area Networks (WBANs), challenges arise in terms of shortening service duration and increased delay for computation and data transmission tasks. To address this issue while meeting Quality of Service (QoS) requirements, Transmit Power Control (TPC) and Link Adaptation (LA) techniques, are applied to determine transmit power level and modulation and coding schemes (MCS). The proposed reinforcement learning algorithm leverages latent path loss and shadowing (PLS) and Signal-to-Noise Ratio (SNR), and transmit power to effectively exploit the corresponding transmit power level and MCS, enabling efficient learning. It aims to minimize energy consumption while satisfying target QoS requirements, including data rate and error ratio. The results demonstrate that our method achieves significant power savings, with transmit energy reductions of up to 11.1%, and extend sensors operating time by up to 22.4 % compared to existing methods.
An Inquiry into the Winding Phase-Shifting Technique on the Electromagnetic Performance of Wound-Field Flux Modulation Machines

In this paper, the application of a winding phase-shifting technique to two flux-modulation machines – the wound-field flux switching machine (WFFSM) and the DC-excited Vernier reluctance machine (DCVRM) is investigated. Finite element analysis (FEA) is used to compare the electromagnetic performance between conventional and 30-degree phase-shifted windings. The phase-shifted windings result in reduced average torque for both machines (32.3 Nm and 28.7 Nm decreases for the WFFSM and DCVRM, respectively). The WFFSM shows a minor 4.6% torque ripple reduction with phase-shifting while the DCVRM has a 13% increase. Optimization using genetic algorithms and skewing is more effective, reducing torque ripple by 57-72%. The phase-shifted windings introduce low-order harmonics likely due to the concentrated windings and modulated reluctance torque production. The results indicate the winding phase-shifting technique is incompatible with the modulated reluctance and internal harmonic fields of flux-modulation machines. More research is needed to understand these differences compared to wound-rotor synchronous machines where phase-shifting is effective.
Zeta-Based Universal Converter with Zero-Current Switching

AC-link universal converters have emerged as promising topologies for efficient power conversion between various sources and loads. These converters offer enhanced reliability by replacing bulky electrolytic capacitors, which have a shorter lifetime compared to other power electronic components, with small and durable film capacitors. By doing so, the overall lifetime of the converter would be significantly extended. Additionally, AC-link converters achieve high-power density by utilizing compact high-frequency transformers when isolation is required, eliminating the need for large and heavy low-frequency transformers.This paper introduces a new soft-switching Zeta-based universal converter capable of stepping up and down the voltage in a wide range. Building upon the operating principles of the Zeta DC-DC converter, the proposed converter extends its applications to inverters, rectifiers, and AC-AC converters. In this converter, an inductor and a capacitor are responsible for transferring the power from input to output. Another small inductor is employed in series with the link capacitor to allow all switches to have zero-current switching, resulting in improved efficiency. In isolated configurations, the leakage inductance of the transformer can be used for this purpose. Compared to conventional Ćuk-based universal converters, this topology employs a capacitor with a lower voltage rating. The performance of the proposed structure is verified through simulation and experimental results in this paper.
Hierarchical Classification of ISAR Sequences

Inverse synthetic aperture radar (ISAR) is a common radar imaging technique used to characterise and classify non-cooperative targets. A number of different classification approaches have been proposed including the traditional approach which utilises geometric features extracted from images of known targets and more recently deep learning approaches. Hierarchical classification is designed to deal with large scale classification problems when there is a large number of targets. In this paper, a number of convolutional neural networks are compared to determine the best approach for hierarchical ship classification. Recent work on transfer learning, recurrent neural networks, and hierarchical structures are explored with the goal to find the best network design in terms of computational efficiency and classification accuracy.
A Single-Carrier PWM Method for Uniform Step Asymmetrical Multilevel Converters

This paper proposes a general design principle of a single-carrier-based pulse width modulation (SC-PWM) technique for a uniform step, asymmetrical multilevel converter with k series-connected H-bridge inverters under unequal DC-link voltages within each phase. The proposed modulation approach is a straightforward implementation of the conventional phase-disposition PWM. It may produce many output voltage levels (any odd number between 2k+1 and 3k) using only one carrier signal without complicating its implementation scheme. This makes it suitable for real-time implementation in available low-cost microcontrollers and microprocessors. The topological structure of the investigated asymmetrical multilevel converter is presented, and analytical relationships are established to define a general design principle of a uniform step configuration. Then, the proposed method's working principle and its generalized implementation scheme are presented and discussed. The effectiveness of the suggested PWM technique is finally demonstrated by simulation and experimental results for a single-phase, three-cell asymmetric converter operating under unequal DC-link voltages with 7, 11, 13, and 15 voltage levels.
Glasses-free Autostereoscopic Viewing on Laptop through Spatial Tracking

This paper introduces an autostereoscopic viewing method that eliminates the need for specialized 3D glasses. The approach relies on vision-based motion parallax effects achieved by rendering a 3D scene on a 2D display according to the viewer’s head position. Two systems were developed: one employing a single-camera setup, and the other utilizing a dual-camera configuration. By utilizing webcams integrated into a laptop, real-time analysis of the viewer’s head position is performed. A combination of computer vision and range imaging techniques extracts the viewer’s viewing position in the 3D space. This information is then used to manipulate the primary camera object within the Unity game engine, facilitating the rendering of an autostereoscopic view of a 3D scene. The Unity game engine adjusts the scene based on the viewer’s perspective, creating an illusion of a 3D scene on a 2D display as the viewer’s viewpoint changes. The accuracy, range, and output stability of the depth estimation techniques employed in both systems were assessed through systematic evaluation and an application usecase. The systematic evaluation revealed high levels of accuracy in estimating distances from the viewer to the laptop display for both systems, achieved within an effective viewing range of approximately 40 to 160 centimeters. A comprehensive discussion compares the capabilities and performance metrics of the two systems, while the application use-case discussion emphasizes their effectiveness in delivering optimal autostereoscopic viewing on a PC laptop screen.
Electrically Large Complex Objects Recognition Using Deep Learning Approach

In this paper, the shooting and bouncing ray method (SBR) is used to obtain electromagnetic (EM) scattering characteristics of electrically large complex objects, including different ships. Compared with finite integral time domain (FITD) and multi level fast multipole method (MLFMM), the SBR method are time and resource efficient. As a new type of machine learning algorithm, long short-term memory (LSTM) has unique advantages for dealing with sequential data like high resolution one-dimensional range profile (HRRP), since it can learn contextual relevance, simultaneously, solve long-term dependency problems with the help of a set of gate structures. Scattering characteristics database acquired by SBR is input into LSTM network to quickly recognize electrically large complex objects. It has better recognition performance than traditional support vector machine (SVM) and convolutional neural network (CNN) at different percentages of training data. This work will be helpful to neural networks implementations in quick recognition of electrically large complex objects.
Modelling and simulation of nitrogen injection in vegetable olive oil

The aim of this work is to analyze, by means of a CFD analysis, the injection of gaseous Nitrogen (N2) in vegetable oil. This process, called sparging, is important to enhance oil shelf-life, because it allows to separate by stripping the Oxygen (O2) dissolved in the product. In fact, when the tank filling begins, N2 bubbles start to rise towards the free surface of the product, dragging along with themselves part of the dissolved O2. In this way the probability of initiation of oxidative reactions is reduced and, consequently, is reduced also the possibility of oil degradation. The final goal of this work is to compare different sparger configurations and observe how they influence the stripping process during the tank filling phase. The various configurations have been compared in terms of O2 residual inside the tank at the end of the filling phase. The simulations still need to be experimentally validated.
TGP-based dynamic traffic camouflage method

In order to address the issue of attackers leveraging traffic analysis to steal users' network behavioral patterns, this paper proposes a dynamic traffic camouflage method based on Twin Gaussian Processes (TGP), referred to as TGP-based dynamic traffic camouflage method (TTC). TTC utilizes Twin Gaussian Processes to capture the pattern changes in the target traffic for feature prediction of the undisguised traffic. After obtaining the predicted features, constraints are applied and traffic reconstruction is performed based on these constrained features. The reconstructed traffic is indistinguish-able from the target traffic, leading to misclassification by attack-ers. Experimental results using the ISCXTor2016 public dataset demonstrate that the proposed TTC method has stronger obfuscation capabilities in traffic classification compared to previous traffic obfuscation methods. It effectively protects user privacy.
DC Collection and Transmission for Offshore Wind Farms

This paper explores the cost implications of a novel DC-based architecture for interconnecting offshore wind farms to the onshore grid. The proposed architecture comprises a medium voltage DC (MVDC) offshore collection system and high voltage DC (HVDC) transmission via subsea cable. A switched capacitor-based DC/DC converter with fixed voltage ratio is considered to convert MVDC to HVDC. A PSCAD based simulation model is developed to study its performance in steady state as well as its fault response characteristic under various fault scenarios. To establish the economic feasibility, the cost of proposed MVDC-HVDC architecture is compared against two conventional architectures, namely (i) medium voltage AC (MVAC) collection – HVAC transmission and (ii) MVAC collection – HVDC transmission. A spreadsheet-based techno-economic analysis tool is developed to compare the levelized cost of electricity (LCOE) between the three architectures.
Passive Multistatic Localization of Space Objects using LOFAR Radio Telescope

The paper presents the idea and results of the experimental trials devoted to passive multistatic observation of space objects in low-Earth orbits employing an astronomical LOFAR (LOw-Frequency ARray) radio telescope as a surveillance receiver. Terrestrial commercial digital radio transmitters operating in the VHF band were used as illuminators of opportunity. The large antenna array of the LOFAR radio telescope allows us to record relatively weak echo signals reflected from space objects. Reference signals were acquired by software-defined radio receivers located near illuminators of opportunity. Combining the results of bistatic measurements from three pairs of illuminator-receiver, we estimated the position of a space object in a Cartesian coordinate system. The results of the experiments confirm the possibility of determination of the space object’s position utilizing the passive radar system using antenna arrays such as the array in the LOFAR radio telescopes and the commercial terrestrial transmitters as illuminators of opportunity.
A Comparative Analysis of Control Strategies Applied to Electrical Traction Systems

This paper compares three types of Direct Torque Control for Induction Machines for vehicular traction applications. The paper analyzes the classic DTC with hysteresis modulation, a modulated DTC with PI controllers and the Predictive Torque Control (PTC), keeping the switching frequency of the controllers as close as possible to 11 kHz. The paper shows simulation and experimental results on torque and flux control for a similar 1.1 kW machine. Results of torque and flux ripple and mean relative error shows a performance advantage of the PTC in both simulation and experimental results. The DTC with PI controllers shows high sensibility to controller gains and operation point, showing the worst experimental results.