Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating Voltage Estimation in a Nanogrid Using Digital Twin Models and Real-Time Smart Meter Data

The rapid deployment of renewable and distributed energy resources in secondary distribution networks is changing the way these systems are operated. With high presence of distributed generation, the observability of the network made at traditional bulk points is not sufficient. Current trends are now pushing towards high-speed, real-time state estimation of electricity networks and their assets, which can assist in the operation of these systems. In this context, digital twin technologies replicating the characteristics and capturing the performance of the network have found application in smart grids as supportive tools of the predictability and evaluation of power system assets. In this paper, the performance of two digital twin approaches (a model-free and a model-based approach) of a nanogrid (located at the University of Cyprus in Nicosia, Cyprus) with high photovoltaic (PV) penetration are evaluated for the estimation of voltage levels across a range of meteorological conditions. The digital twin performance is evaluated against historic and real-time data acquired at 1-second resolution from the nanogrid’s assets. The voltage estimation results demonstrate high agreement exhibiting errors in the range of 0.50% to 1.81% depending on the bus and the level of PV power variability.
Computing Surface Integral Equation Matrices with Shared Memory Parallelization

An efficient shared memory parallelization strategy is presented for computing matrices resulting from surface integral equation based methods in time-harmonic electromagnetics (EM). In the numerical solution of EM problems with methods such as the method of moments (MoM) a significant portion of the solution time is dedicated to filling dense interaction matrices. Developing efficient parallelization strategies to take advantage of all of the available computational resources of multi-core central processing units (CPU) is thus critical in the fast solution of computationally demanding EM problems. The proposed scheme is applied to the electric field integral equation (EFIE)-based MoM and shown to demonstrate good parallel efficiency.
Forest-Related SDG Issues Monitoring for Data-Scarce Regions Employing Machine Learning and Remote Sensing - A Case Study for Ena City, Japan

We proposed a combined machine learning approach with a deep convolutional neural network (CNN) to monitor forest utilization toward Sustainable Development Goals (SDGs) for data-scarce regions. First, we employed the Random Forest (RF) classifier using Google Earth Engine (GEE) for forest mapping. Then, we designed a deep CNN architecture that works for tree species/age mapping from coarse and polygonal ground-truth data. The proposed network has U-shape and comprises 3D Atrous Convolutions. The model was optimized by a weighted cross-entropy loss function. We trained the model with times-series Sentinel 1, 2, and Digital Elevation Model (DEM) data with sparse annotations. Our proposed models achieved 94.5% overall accuracy (OA) for forest mapping, 77.80% (OA) for tree species, and 81.74% (OA) for tree age classification, respectively in Ena city, Japan. The outcome of our study indicates the potential of remote sensing and machine learning in monitoring forest development, conservation, and utilization toward SDGs from coarse ground-truth data. Our source code for the implementation is available at: https://github.com/anhp95/forest_attr_segment
Sonomyography shows feasibility as a tool to quantify joint movement at the muscle level

Several methods have been used to quantify human movement at different levels, from coordinated multi joint movements to those taking place at the single muscle level. These methods are developed either in order to allow us to interact with computers and machines, or to use such technologies for aiding rehabilitation among those with mobility impairments or movement disorders. Human machine interfaces typically rely on some existing human movement ability and measure it using motion tracking or inertial measurement units, while the rehabilitation applications may require us to measure human motor intent. Surface or implanted electrodes, electromyography, electroencephalography, and brain computer interfaces are beneficial in this regard, but have their own shortcomings. We have previously shown feasibility of using ultrasound imaging (Sonomyography) to infer human motor intent and allow users to control external biomechatronic devices such as prosthetics. Here, we asked users to freely move their hand in three different movement patterns, measuring their actual joint angles and passively computing their Sonomyographic output signal. We found a high correlation between these two signals, demonstrating that the Sonomyography signal is not only user-controlled and stable, but it is closely linked with the user’s actual movement level. These results could help design wearable rehabilitation or human computer interaction devices based on Sonomyography to decode human motor intent.
Bare Land Soil Moisture Estimation Using Alos-2 Palsar-2

Soil water content in agricultural land is a major factor for forecasting drought, estimating crop yields, and grasping methane emissions. The needs of spatiotemporal soil moisture data are increased recently. However, spatially seamless soil moisture estimations are challenging due to the lack of ground truth and the small spatial representativeness of point-based in-situ measurement. In contrast, microwave imagery has an advantage for soil moisture monitoring with high sensitivity to dielectric constant of land surface. Japan Aerospace Exploration Agency (JAXA) provides stable accuracy and high resolution at 6.25m spatial resolution microwave images with L-band observation using PALSAR-2 sensor of ALOS-2 since 2014. The objective of this study is volumetric soil water content estimation using dielectric constant derived by Dubois algorithm with PALSAR-2. Then we converted the dielectric constant result into soil moisture using Topp model. As the result, the average of soil water content in Ibaraki field was estimated as 28% by using PALSAR-2. When it was compared with in-situ, the RMSE was 5.2%. Further spatial and quantitative comparisons have shown.
Electrical Load Forecasting Utilizing an Explainable Artificial Intelligence (XAI) Tool on Norwegian Residential Buildings

Electrical load forecasting is an essential part of the smart grid to maintain a stable and reliable grid along with helping decisions for economic planning. With the integration of more renewable energy resources, especially solar photovoltaic (PV), and transitioning into a prosumer-based grid, electrical load forecasting is deemed to play a crucial role on both regional and household levels. However, most of the existing forecasting methods can be considered black-box models due to deep digitalization enablers, such as Deep Neural Networks (DNN), where human interpretation remains limited. Additionally, the black box character of many models limits insights and applicability. In order to mitigate this shortcoming, eXplainable Artificial Intelligence (XAI) is introduced as a measure to get transparency into the model’s behavior and human interpretation. By utilizing XAI, experienced power market and system professionals can be integrated into developing the data-driven approach, even without knowing the data science domain. In this study, an electrical load forecasting model utilizing an XAI tool for a Norwegian residential building was developed and presented.
A Weakly-Supervised, Multitask Deep Learning Framework for Shadow Mitigation in Remote Sensing Imagery

We propose a weakly-supervised, multitask framework for training a convolutional neural network to solve the problem of cloud shadow mitigation given only cloud and shadow masks as labels. The network minimizes the Wasserstein distance between shadows and their proximal sunlit neighborhoods, generating a supervisory signal directly from within the input image. We extract further utility from the shadow mask through multitask learning by introducing an auxiliary task of shadow segmentation. Our approach is advantageous since it performs mitigation in an end-to-end framework which requires only a shadowed image for inference. We apply this process to the Landsat 8 OLI SPARCS validation data set and demonstrate plausible results.
Intra-day Electricity Market Bidding for Storage Devices using Deep Reinforcement Learning

This paper investigates the use of Deep Reinforcement Learning (DRL) to control a profit-seeking storage device trading in the European Continuous Intra-day Electricity Market (CIM). The main objective is to study whether model-free DRL can profitably trade on the CIM. Two DRL agents are compared: Twin Delayed Deep Deterministic Policy Gradients (TD3), and TD3 with behavior cloning. The agents are trained and evaluated in a simulated CIM environment, which uses historical market data to simulate other market participants. A Rolling Intrinsic (RI) algorithm is used as a benchmark. Results indicate that the agents are profitable and occasionally outperform RI, in one instance obtaining 162.03% of RI profit. However, none of the agents consistently outperforms the baseline. These results suggest that DRL has the potential to increase profitability considerably compared to RI, but that the observation provided to the agent is not descriptive enough of the CIM to learn a robust policy. Future research could use additional features in the observation, or model-based DRL to improve performance.
A Comparison of Force-Plate Based Center of Mass Estimation Algorithms

Estimating horizontal center of mass (CoM) is an important process that is used in the control of self-paced treadmills, as well as in clinical and scientific biomechanical analysis. Many laboratories use motion-capture to estimate CoM, while others use force-plate based estimates, either because they cannot access motion-capture or they do not want to be taxed with post-processing optoelectronic data. Three force-plate derived center of mass estimation algorithms were compared against a benchmark motion-capture technique. Two of them have recently been reported in the literature, and both rely on numerical integration of 2nd-order differential equations. We propose a third technique that uses an algebraic equation to directly relate center of pressure to center of mass without numerical drift. Twenty-four healthy adults participated in a five-minute steady-state walking test to compare these algorithms. The sample-by-sample standard deviation of the three force-plate based algorithms from the motion-capture benchmark algorithm was evaluated. The algebraic technique provided less error than either of the two more common integration techniques (p<0.05). The results of this study support the viability of using only ground reaction forces for self-paced treadmills and also show that a simple algebraic model is preferred to integration approaches. The use of an algebraic estimation simplifies control implementation for self-paced treadmill applications and eliminates the need for event-based drift recalibration.
A Model Conversion Algorithm from URDF to DH for Camera Robot

The kinematic parameters of camera robot are described by Unified Robot Description Format (URDF). Compared to Denavit-Hartenberg (DH) method, URDF is more flexible. But DH method has an advantage in inverse solution control at position level. Sometimes only URDF file can be obtained due to the engineering project requirements, such as modularity and security. It is easy to convert DH to URDF. There is a lack of research for inverse conversion. In this paper, three conversion principles were proposed. The relationship of base coordinate system and joint coordinate system was divided into six situations. The DH transformation parameters were solved based on the three principles. Finally the extend conversion algorithm for serial joints chain was supplied. The robot kinematic conversion algorithm from URDF to DH provides a mathematical tool for requirements of engineering projects.
Extracting High-Resolution Cultivated Land Maps from Sentinel-2 Image Series

The recent advances in Earth observation and artificial in-telligence allow us to improve the agricultural management practices through effectively exploiting the spectral, spatial, and temporal characteristics of the area of interest captured by satellite images. In this paper, we tackle the problem of extracting high-resolution (2.5-meter) cultivated land maps from Sentinel-2 multispectral images, and propose a machine learning algorithm for this task. It aggregates the spectral, spatial, and temporal features of the upsampled images, and is independent from the number of observations captured for a given scene. The experimental results, performed within the framework of the Enhanced Sentinel-2 Agriculture chal-lenge show that our technique manifests high generalization abilities over the unseen data and elaborates high-quality cul-tivated land maps. Finally, utilizing this algorithm led us to taking the $6^{\text{th}}$ place in the aforementioned challenge.
Aerosol Single Scattering Albedo Estimated Across East Asia from Advanced Himawari Image Data

Single scattering albedo (SSA) is an important parameter affecting the radiative forcing of aerosol. However, current SSA retrieval only relies on several typical aerosol models, limiting the range of SSA variation. This study proposed a new parameterization scheme of the aerosol model, which optimized the independent SSA value into mixed combination of three basic aerosol components, then constructed lookup table to obtain hourly SSA from the Advanced Himawari Image (AHI) sensor. The comparison is encouraging the retrieved SSA results agree well with AERONET, especially for high aerosol loading (AOD > 0.3 at 470nm), with correlation coefficient of R = 0.50, RMSE = 0.02, and approximately 93% of retrieval results falling within the 5% EE envelope. Under normal conditions, R = 0.46, RMSE = 0.04, and approximately 68% of retrieved SSA fall in within the uncertainty of $\Delta$ SSA = ±0.05. The comparison demonstrates the algorithm has the excellent ability to estimate SSA in pollution over land.
Design and Evaluation of a Knee Flexion Assistance Exoskeleton for People with Transtibial Amputation

People with below-knee amputation walk with asymmetric gaits that over time can lead to further musculoskeletal disorders and decreased quality of life. While prosthesis technology is improving, prosthetic ankles may be fundamentally limited in their ability to restore healthy walking patterns because they do not assist the residual knee joint. The knee on the residual limb has muscular deficits due to the loss of the gastrocnemius, a biarticular muscle that crosses both the ankle and knee. Here we present the design, development, and preliminary evaluation of a robotic knee exoskeleton for people with transtibial amputation. The device is intended to restore gastrocnemius-like flexion moments to the knee on the residual limb. The exoskeleton uses a custom offboard actuation and control system to allow for a simple and lightweight design with high torque capabilities. A preliminary walking experiment with one person with transtibial amputation was conducted. The exoskeleton provided a range of knee flexion torque profiles and had an RMS tracking error of 1.9 Nm across four assistance conditions. This device will be used in future studies to explore the effects of providing knee flexion assistance to people with transtibial amputation during walking. Long term, findings from studies with this exoskeleton could motivate future assistive device designs that improve walking mechanics and quality of life for people with limb loss.
Senescence Reversion in Plant Images Using Perception and Unpaired Data

Recent work on using herbarium images for automatic plant identification, and in particular to do domain adaptation to field images, has been promising. A potential way to address such domain adaptation problem is generative: Hallucinate how a herbarium image would have looked like when it was in the field, and use such synthetic, fresh and green image for plant identification purposes. Such generative task, called senescence reversion, has been poorly explored. To our knowledge, it has been studied only in terms of paired data, meaning, with pairs of dry and fresh images of the same specimen. Such paired data is hard to produce, curate and find. In this work we explore herbarium senescence reversion via unpaired data. The lack of pairs at specimen level presents its own challenges, as capturing the intricacies of each species depends on images of different specimens from different domains. We explore learning a mapping from a herbarium image to leaf plant images and vice-versa, aligning two models, one for each herbarium and leaf domain. We experiment against the state-of-the-art paired baseline Pix2Pix which yields a SSIM of 0.8986, compared to our unpaired approach that yields 0.8865, showing very similar results on reconstruction metrics regardless of our approach not having the luxury of pairs by specimen. Additionally, we apply perceptual loss in order to improve the natural look of the synthetic images. The balance of how much perception is good to avoid reconstruction problems is also studied. Lastly, using a new unpaired dataset built by ourselves, our results show that using a low lambda value from 0.025 to 0.05 for perceptual loss, helps getting lower Frachet Inception Distances and higher Inception Scores. To our knowledge, no other work has focused on reverting senescence of herbarium sheet images based on unpaired data.
Optimal Gain-scheduled POD for Power Systems with Hybrid HVDC Links

The evolving High Voltage Direct Current (HVDC) technology integrated into the modern power grids can help improve operation stability and damp undesired low-frequency oscillations in the system. This paper presents a Power Oscillation Damping (POD) strategy for power systems with a hybrid (LCC-VSC) HVDC link. The work consists of a centralized supervision algorithm that monitors the dynamics of several system variables and sets the appropriate gains to the POD controller from a lookup table (LUT) generated offline via simulation-based Particle Swarm optimization analysis. The mathematical modeling for the test system with an embedded HVDC link is presented, and the optimal tuning problem is defined using performance-oriented objective functions. Details for the detection and scheduling algorithm, LUT construction, and controller structure are provided. The nonlinear simulation model is implemented in MATLAB, and the results support the effectiveness of the proposed approach.
Asymmetrical Relaying in Molecular Communications

Molecular communication via diffusion (MCvD) is a novel communication technique that uses the diffusive characteristics of molecules for enabling the communication between nanomachines. Since the molecules propagate following a random motion, MCvD schemes are usually limited to a short communication range. Most of the molecular relaying schemes in the literature consider symmetric setups where transmitters and receivers are placed at the same distance from the relay, which is difficult to provide in practical scenarios and a possible cause of failure. In this study, asymmetric molecular links of a relay system are investigated. In order to achieve a satisfactory overall performance in spite of the asymmetries, two parameter optimization methods are proposed for the uplink of a relaying system, based on emitting different types of molecules with different diffusion coefficient values from the transmitters. Due to the channel symmetry, the solutions presented in this study are expected to hold for the downlink as well. The resulting bit error rate (BER) performances are presented and discussed.
A Framework for the Construction and Tailoring of Engineering Development Process Models

A well-balanced project design using appropriate process models is an important success factor for the management of engineering development projects. There exists a wide range of different process models, including traditional waterfall-based models as well as agile development models like Scrum. Such standardized process models offer a good starting point for the management of engineering development projects; however, they have to be tailored in order to meet the needs of a specific project. Additionally, in complex environments, it might be necessary to construct dedicated process models, like hybrid combinations of several models. In this article, the groundwork for a systematic approach to the construction and tailoring of process models for engineering development projects is presented. It considers relevant international standards as well as traditional and agile process models and can serve as a basis for the subsequent tailoring processes. The developed framework offers the possibility to add distinctive characteristics to a process model, like means which facilitate quality, communication, or knowledge transfer. The evaluation of the framework was done by means of a case study, a survey, and expert interviews.
3-D Printed Millimeter-Wave Metal-Only Dual-Band Circularly Polarized Reflectarray

This article presents 3-D printed metal-only dual-band millimeter-wave circularly polarized (CP) reflectarrays. The reflectarray’s unit cell (UC) is made of an anisotropic metal brick mounted on a full metal ground. In-plane rotating the metal brick introduces geometric Pancharatnam-Berry (PB) phase-shifting, and vertically adjusting the position of the UC provides propagation phase-shifting. The geometric PB and propagation phase-shifting offers two different degrees of freedom, enabling independent wavefront shaping of CP wave over dual-band. Specifically, the low-band CP beam-shaping solely depends on the propagation phase. In contrast, the high-band CP beam-shaping depends on the geometric PB and the propagation phases. The coupling issue between dual-band is minimal because of the unique dual-band phase-shifting methods. In addition, 3-D printed dual-band left-hand circularly polarized (LHCP) horn antennas are designed, printed, and used as the feed. To demonstrate, two reflectarray antennas with different dual-band CP beam directions are fabricated and measured.
Multiwavelength campaign on Mrk 509 - IX. The Galactic foreground

Context. The diffuse gas in and nearby the Milky Way plays an important role in the evolution of the entire Galaxy. It has a complex structure characterized by neutral, weakly and highly ionized gas. Aims. We probe this gas through the observation of its absorption lines in the high-energy spectra of background sources. Methods. We used high-quality spectra of AGN Mrk 509, located at high Galactic latitudes obtained with XMM-Newton ,H ST and FUSE. We used advanced absorption models consisting of photo- and collisional-ionization. Results. We constrain the column density ratios of the different phases of the interstellar medium (ISM) and measure the abundances of C, N, O, Ne, Mg, Al, Si, S, and Fe. We detect seven discrete interstellar clouds with different velocities. One is a typical lowvelocity cloud (LVC) and three belong to the family of the intermediate-velocity clouds (IVCs) found near the Galactic disk. These four clouds show large deviation from Solar abundances in the gas phase, mostly caused by dust depletion. The other three clouds are ionized high-velocity clouds (HVGs) and are located either in the Galactic environment or in the Local Group halo as suggested by the signatures of collisional ionization. The similar abundances and ionization structure of the HVCs suggest a common location and origin: they might belong to the remainder of an extragalactic cloud which was captured by the Galaxy. Conclusions. We have shown that combined UV/X-ray spectroscopy is a powerful tool to investigate the ISM. In common Galactic clouds, like LVCs and IVCs, the ISM shows a complex structure consisting of at least three different temperature phases.