Miracle-dz/UniFAR-FTUs huggingface dataset:
Hierarchical Variance Analysis of Solar Cell Production Using Machine Learning and Numerical Simulations

Solar cells are an important source of renewable energy and have become a major industry with annual production capacities above 200 GW. The large amount of data produced during the solar cell production process calls for big data solutions and machine learning based approaches to improve quality and increase efficiency. In this work, we analyzed solar simulator data from 60,000 PERC solar cells using a hierarchical model based on machine learning, and compared the results to a theoretical model to extract missing information and determine the most likely input parameters for each produced solar cell. This approach enabled us to gain insight into the mechanisms that lead to the variance of conversion efficiency.
Reducing the Photovoltaic Operation and Maintenance Costs Through an Autonomous Control Operation Center

An advanced control operation center to enable corrective, preventive and predictive maintenance, while also ensuring optimal photovoltaic (PV) plant performance was developed in this work. The developed software solution hosts innovative algorithms able to ensure data quality, while also allowing early failure and performance loss diagnosis without disrupting the normal operation of the PV plant. It is primarily based on real-time analysis of measurement data, machine learning and statistical analysis. The solution was validated experimentally against field measurements from an operating PV power plant of 1.8 $\mathbf{MW}_{\mathbf{p}}$ installed in Greece. The results showed technical availability and energy yield improvements of the test PV plant by handling intelligently the detected faults through the smart ticketing system. Optimal maintenance planning (e.g., optimum hardware replacement/maintenance, cleaning schedules, etc.) can thus lead to a reduction of operation and maintenance (O&M) costs and hence directly impacting positively the levelised cost of electricity (LCOE).
FLNET2023: Realistic Network Intrusion Detection Dataset for Federated Learning

Developing new defensive mechanisms to respond to evolving security threats is crucial for enforcing network security. Recently, defenses leveraging Federated Learning (FL) have become prominent in Intrusion Detection Systems (IDS) to incorporate the surging growth and distributed nature of the network infrastructure. To evaluate these FL-based IDSes and to achieve better detection performance, researchers commonly perform equal and balanced partitions of the existing popular datasets, such as NSL-KDD, UNSW-NB15, and CICDDoS2019, among clients. However, partitions of these datasets are not representative of the class-imbalanced scenarios found in real-world networks where each client may possess different categories of attack traffic with an uneven number of instances in their dataset. Moreover, they overlook the fundamental data distribution property in a network environment where data must be associated with individual IP addresses, particularly the destination IPs that are identifiable as FL clients. To fill these gaps, we introduce a novel dataset, Federated Learning for Networks (FLNET2023), which is strategically generated by gathering data from network traffic across ten unique routers within a real-world network topology emulated using the CORE tool. We also evaluate the FLNET2023 using two FL aggregation algorithms and compare its performance against the latest intrusion detection dataset, CICDDoS2019, with traditional partitions to demonstrate the challenges of FL-based IDSes on realistic datasets.
A Sub-1.4 dB Noise Figure 6-18 GHz Low Noise Amplifier Using 0.15 µm GaAs pHEMT Technology

This paper describes the design of a 6-18GHz ultrawideband low noise amplifier (LNA) based on 0.15 µm GaAs pseudomorphic high electron mobility transistor (pHEMT) technology. The LNA generates flat gain, low noise figure, and acceptable input and output return losses from 6-18 GHz using a cascode structure, source degenerate inductors, and RLC negative feedback networks. Simulation results show that the noise figure is lower than 1. 4dB, the gain of the LNA is 24.5±1dB, and both input and output return losses are less than −10dB. The input 1dB compression point (IPldB) of the LNA is −10dBm.
Reconstructing Cardiac Voltage Using Data Assimilation: Effects of Observation Distribution

Both experimental and clinical cardiac electrophysiology data often are recorded with limited spatial resolution or inconsistent spatial distribution. Rather than interpolating such data, which neglects constraints imposed by dynamics, data assimilation can be used to mitigate the effects of low-resolution data by providing estimates of voltage in unmapped areas. Here, we investigate the effects of different spatial distributions of observation data on cardiac voltage reconstructions over time using data assimilation. We used an ensemble Kalman filter method to reconstruct complete, uniform voltage data from observational data with different spatial distributions. Stable spiral-wave and sustained spiral-wave breakup cases from the modified Mitchell-Schaeffer model were analyzed. Coarse uniform observations and observations restricted to half the domain were considered. Stable spiral wave dynamics could be reconstructed well, with some lag at the coarsest resolutions and for half-domain observations far from the spiral core. Breakup cases were more difficult to match quantitatively; scenarios with fewer spirals or greater observation coverage of the most intense breakup regions could be reconstructed more accurately. Overall, we found that accurate voltage reconstructions could be generated for stable waves and breakup cases using data assimilation, provided that observations include regions driving breakup.
Performance comparison of applying integer and fractional order calculus to DC motor speed control experiments

Fractional order calculus has been widely studied because it is suspected to perform better than integer one, particularly in control system applications. The performance comparison of the two methods on the derivative and integral orders is fascinating. Experimental application studies will strengthen validation compared to just going through simulations. This research aims to compare the performance of integer and fractional orders on the implementation of DC motor speed control. The experimental results indicated that the fractional order calculus method was commonly more satisfactory than the integer one both in accuracy and convergent rate in controlling DC motor speed. Additionally, it enhanced its accuracy by approximately 2% for integral gain and up to 12% for differential one. However, the integer order calculus approach has a faster settling time and better stability than the fractional order technique in the case of differential gain
Design and Analysis of Energy Harvesting System Based on Piezoelectric Element

Due to the massive demand for energy worldwide, many researchers are starting a deep investigation into the issue. So, this paper deals with generating electric power by converting mechanical waste vibration into electric power by attaching a suitable piezoelectric element to a metallic cantilever beam attached to a mechanical engine. The second aim is to use this harvested energy to power Internet of Things (loT) technology. The experimental rig consists of three rectification models: Half Wave Rectifier (HWR), Full Wave Rectifier (FWR), and Cascade Capacitor Rectifier (CCR). These three circuits were built with three different diodes: silicon diode (1N4007), germanium diode (1N60), and Shockley diode (1N5819). The gained practical result showed that after 30 seconds of charging, the associated voltage from the first rectifier was 3 volts, 3.45 volts, and 3.55 volts, with 1N4007, 1N60, and 1N5819, respectively. Meanwhile, the second rectifier gave 2.25, 3, and 3.5 volts for the same sequence of three employed diodes. However, the overall situation regarding the third rectification circuit, where the induced voltage was 6, 6.8, and 7 volts in association with the three diodes mentioned above, in other words, the minimum and maximum harvesting voltage were 2.25V (FWR plus 1N4007), and 7 V (CCR plus 1N5819), i.e., the obtained percentage increase is 311%.
Quantum-Resistant Reliable Authentication and Lightweight Security Scheme for 5G enabled Narrow Band Internet of Things

The Narrow Band Internet of Things (NB-IoT) paradigm has become a central point of the 5G networks. Though the NB-IoT mechanism has progressively been incorporated in the conventional LTE networks, notably the 5G network-based NB-IoT scheme realizes certain security breaches in recent times. Hence, the need for effective authentication and data transmission scheme is much more vital for security enhancement. Conventional security schemes follow both symmetric and asymmetric cryptographic techniques such as AES, ECC, DES and many other famous algorithms to assist in preventing against data security issues. All of these cryptographic techniques are inadequate to give protect against quantum attacks. This study plan to propose a secure quantum-resistant data transmission strategy and authentication for a 5G based NB-IoT scheme. The proposed method follows the implementation of data transmission strategy and access authentication concurrently for NB-IoT devices based on lattice-based attribute and fully homomorphic encryption techniques. The proposed scheme will not just minimize the network overhead but also provide robust security with privacy shielding and anti-quantum capability.
A Case Study on the Internal Mutual Interference of Multitenant FPGA

Previous researches have shown that a remote user can cause the circuit of another user in a cloud FPGA to fail by creating mutual interference inside the FPGA. However, the fundamental mechanism of mutual interference is still not clear. This paper conducts a series of tests to investigate the condition and the type of the function failure. Two critical parameters of the interference source, the number of interference units $({N}_{INT})$ and the frequency of the interference $({f}_{INT})$ are examined. The behavior of a microprocessor is monitored at the instruction level during the interference experiments. Our test results show that ${N}_{INT}$ is inversely proportional to ${f}_{INT}$ at the failure threshold. The relationship indicates that the deciding factor of interference source on the failure of the microprocessor is the average current. The ratio of interference frequency to microprocessor frequency plays no role in the failure threshold. It is observed that the malfunctions of the program count (PC) and the instructions are the main reasons for the system failure. The interference also causes the pipeline to stall. The results presented in this paper help to understand the bottom mechanism of FPGA failure under remote attack. It also helps to make countermeasures to protect the FPGA.
Prosody-Based Vocal Emotional Alignment in Infant-Caregiver Interaction

Infants actively interact with caregivers to develop social competence by aligning behaviors and mental states. Previous research has demonstrated the early ability to align core affect through distinct naïve nonverbal behaviors. However, the extent to which infants align vocal emotions with caregivers more finely has remained largely unexplored. This study investigated the nuances of vocal emotions during 60 minutes of natural conversations between caregivers and their 9-month-old infants from 25 families. To this end, a pre-trained deep neural network (DNN) based on contrastive learning was applied to extract emotion representations using the prosodic features of infants' and caregivers' vocalizations. The results revealed that infants and caregivers align their vocal emotions in natural interactions. While infants showed less variability in vocal emotions than their caregivers, the variabilities of both parties were positively correlated at the family level, indicating the role of parental vocal emotions in facilitating infant vocal emotions. These findings provide insight into the early interactions of vocal emotions in family conversations and highlight the significance of the quality of caregivers' vocal expressions, i.e., the displayed emotional variability.
Distributed Online Optimization via Kernel Reproduced Gradient Descent

This paper focuses on a stochastic optimization problem in distributed online learning. To accelerate the op-timization in a distributed framework, a reproducing gradient method is proposed based on kernel learning. Considering the practicality of distributed settings, this method utilizes random Fourier features for gradient learning, which enables privacy protection as model parameters and gradients do not need to be transmitted over the network. Further, a distributed online reproduced gradient descent algorithm is proposed, and the convergence rate of the algorithm is provided. Numerical results show that our method outperforms the existing distributed stochastic gradient descent based on gradient tracking.
Design and Implementation of a Configurable I/O Expander with the Wide-range Voltage and Temperature

I/O expander is a popular device in embedded systems, often serving as system adhesive. It is commonly based on I2C to support bi-directional communication between multiple interconnected devices in a master-slave structure. After analyzing some products in the market, we have proposed a configurable I/O expander with the wide-range voltage and temperature. It consists of communication block, control block, interrupt block, and IO block. At the same time, we propose the ultra-low voltage design methodology to be compatible with as many devices as possible. In addition, we also insert BIST circuit to improve reliability and yield. After a series of simulation, the proposed design was fabricated in 0.35$\mu$m CMOS process, and the test on chip level is carried out after tape-out. The results show that the I/O expander has better performance and faster response compared with other works, which is very suitable for end-side devices in the IOT and servers.
Role of Fiber Direction and Ionic Heterogeneities in Atrial Arrhythmia Simulations

Digital twins are useful tools to simulate atrial fibrillatory behavior in patients with atrial fibrillation (AF). However, the degree of complexity needed for their clinical use is still unclear. The usage of fiber direction (FD) and ionic heterogeneities (IH) can be a key factor to improve digital models when simulating AF conditions. FD and IH were included in a 3D anatomical model of the atria. Multiple stimulation protocols to induce AF were simulated in this model, adding and suppressing the effect of FD and IH on it. FD had an important role as its inclusion produced 51% of the protocols to generate arrhythmic behavior, whereas without FD only a 24% of the total cases were arrhythmic. On the other side, IH did not produce notorious differences when it was removed from simulations, since the percentage of reentries were 38% when they were included to 37% when they were not. In average, arrhythmic patterns were generated in 37% of simulations, amongst them, 4% were functional reentries and 33% were anatomic reentries. Inclusion of FD in detailed simulation is necessary in order to induce fibrillatory patterns similar to those reported in clinical practice. This will allow a better fit of digital models to their respective patient data.
38 GHz Wideband and High Gain Stacked Antenna Modelling for 5G Applications

This paper introduces the design features of a novel stacked antenna that possesses highly compact broadband and omnidirectional radiation properties. The proposed antenna covers a wide frequency range of 34 to 44 GHz, encompassing dual potential bands in the 5G spectrum such as 36, and 38 GHz. The proposed antenna has a simple design process, low complexity, and desirable features such as a wide impedance bandwidth, and omnidirectional radiation. These qualities make it suitable for fulfilling the requirements of 5G applications. The achieved minimum gain is 6 dBi for the entire frequency range of operation.
Improving the Performance and Yield of Colloidal Quantum Dot Solar Cells Through Electron Transport Layer Optimization

Colloidal quantum dots (CQDs) are promising materials for photovoltaic applications due to their solution processibility and size-dependent band gap tunability. The electron transport layer (ETL) is an important component of PbS CQD solar cells, and the quality of the zinc oxide nanoparticle (ZnO NP) ETL film significantly impacts both the power conversion efficiency (PCE) and fabrication yield of CQD solar cells. We report on multiple methods to improve the quality of ZnO NP ETL films and demonstrate increased PCE and device yield in standard CQD solar cells employing optimized ZnO NP films. We also discuss the application of these methods in an inverted CQD solar cell architecture.
Evaluating the Availability of Service Function Chain under Different Elasticity Strategies Based on Colored Petri Net

The Service Function Chain (SFC) is composed of multiple Service Functions (SFs) that have distinct structures and offer specific services to users. Availability is a critical metric for evaluating the SFC’s ability to deliver services. However, due to resource limitations and the dynamic nature of traffic flow, which necessitates the elastic deployment of SFCs to maintain service quality, there is a requirement to investigate how to evaluate the availability of SFCs under elastic deployment methods. In this paper, the fundamental concept and premises of the basic elasticity strategy for elastic SFC are analyzed at first. Then, utilizing Colored Petri Nets (CPNs), SFC models are proposed with different structural compositions under various elasticity strategies. These models include the serial structure, the parallel ”and” structure, and the parallel ”or” structure, and they are divided into three layers to simulate the data packets processing process of SFC, taking into account the element of time to get the time of the running time and elasticity scaling time. Subsequently, the availability of elastic SFCs is analyzed and evaluated by incorporating a mathematical model that considers elasticity. Availability values for elastic SFCs under different strategies and structures are derived through experiments. The results demonstrate that elasticity implementation has a significant impact on SFC availability. Factors such as the SFC structure, dynamic SFC deployment methods, execution time of the elastic strategies, and interval time between elastic strategy execution all influence the availability of elastic SFCs. Finally, sensitivity analyses are conducted to examine the factors that affect the availability of elastic SFCs. Ranges of important parameter values needed to ensure different levels of availability are provided. These findings shed light on the key considerations for achieving resilient SFC availability.
Synchronization of Conventional Electrocardiogram Recordings for Accurate Vectorcardiography Reconstruction

The vectorcardiogram (VCG) provides a comprehensive representation of the heart's electrical activity in 3D aiding in the diagnosis and treatment of cardiovascular diseases. The conventional electrocardiogram (ECG) records twelve leads intermittently at intervals of 2.5 seconds, with lead II typically recorded continuously, which poses a challenge for reconstructing the VCG, as each lead's beats belong to different time instances. The purpose of this research is to propose and validate a methodology for accurately synchronizing the recording beats to reconstruct the VCG. To achieve this goal, a phantom was created to mimic the standard 12-lead ECG setup. The temporal offset of each beat from the first is calculated using cross-correlation utilizing the continuous lead and the same offset is applied to all leads, and finally reconstructing the VCG. The results demonstrate precise synchronization, as evidenced by Pearson correlation values of $0.9959\pm 0.0034$, an MAE of $0.0077\pm 0.0024\ mV$, and an RMSE of $0.0119\pm 0.0038$ mV in the VCG reconstruction. This technique is essential for the accurate diagnosis and treatment of cardiovascular diseases and can be applied to conventional ECG recordings taken on paper to obtain VCG.
Test Case Generation for Autonomous Driving Based on Improved Genetic Algorithm

From reducing traffic congestion to improving transportation, autonomous vehicles have immense potential in enhancing productivity and quality of life. As a safety-critical system, autonomous vehicles must undergo extensive testing before being deployed on public roads to ensure their safety and reliability. Given the complexity and high dimensionality of testing scenarios for autonomous driving, this paper proposes a test case generation method based on an improved genetic algorithm. The LGSVL simulator is used to conduct simulation tests on the Baidu Apollo system. The experimental results demonstrate that the test cases generated by this method can effectively test various safety violations of autonomous vehicles and improve the efficiency of generating effective test cases.
A Cluster Based Relay Assisted Communication for Prioritized Mobile End-Users

Next Generation wireless networks are primarily focused on minimizing the power wastage issues among communication nodes. The user nodes can be highly prioritized like a mobile ambulance that always seeks priority and uninterrupted service throughout. The existing Millimeter Wave Beamforming (MMW-BF) algorithms steer and switch the beams as desired, but the nodes are subjected to excessive power wastage issues. The proposed cluster based hybrid optimization algorithm when used in MMW beamforming, involves both Butterfly Optimization Algorithm (BOA) and Ant Colony Optimization Algorithm (ACOA). The combined Butterfly- Ant Colony Optimization Algorithm (BACOA) minimizes the excessive power wastage by choosing an optimum cluster head to relay the information and simultaneously finding the optimum path of relaying the information to the destination. The novel BACOA approach in MMW-BF minimizes the power wastage and maximizes the overall energy efficiency while serving the critical or prioritized nodes like mobile ambulance, subjected to preemptive decision rules. The simulation results shows that the proposed BACOA outperforms the conventional ACO and BAO in terms of convergence rate, Achivebale sum rate and Energy Efficiency. The proposed method finds wide application in providing uninterrupted connectivity for prioritized mobile end users like emergency mobile ambulance that expects un interrupted connectivity throughout.