Miracle-dz/UniFAR-FTUs huggingface dataset:
Didactic Integration For The Monitoring Of Electric Power Systems Using Power Monitoring Expert With Modbus TCP/IP And IEC 61850 Communication

This article presents a didactic guide for the integration of the EcoStruxure Power Monitoring Expert software with Schneider Easergy P3 and P5 protection relays and the IEC 61850 communication standard, applied at the Salesian Polytechnic University. The purpose of this work is to facilitate the management, configuration, and monitoring of device information, in the context of teaching industrial communication systems, with a focus on monitoring and coordination of protections. The developed didactic guides allow for adequate and effective teaching of the use and application of these tools in the monitoring and coordination of industrial protections. This contributes to strengthening training in key areas for the industrial sector and promotes practical and applied training, of great importance for the education of engineers and specialized technicians in the field of automation and industrial communication.
MIMO antenna design with PBG structure for THz communication

The THz waves have high speed, large bandwidth, large capacity, miniaturized antenna sizes for the future generation of wireless communications like AI, ML, AR, VR, data, imaging and extremely low latency transmit and receive applications. An optimized photonic band gap (PBG) MIMO antenna with high isolation and extremely large bandwidth is proposed on the polyimide substrate of size 1505×646 µm2 for 0.443-0.807 THz frequency band (BW = 0.363 THz). The proposed MIMO has gain 4.79-7.59 dBi, radiation efficiency 65.70-86.67%, total efficiency 61.53 - 85.61 %, and isolation> 18 dB in the proposed band.
Long-distance 2D positioning with diffractive large diameter laser beam and camera

In the construction of ultra-long structures or skyscraper, it is necessary to measure the shape and size of ultra-long scale objects. The static station scanning measurement needs a lot of stitching in the ultra-long measurement. Meanwhile, the mobile measurement based on Simultaneous Localization and Mapping (SLAM) technology is difficult to achieve a good accuracy for long distance measurement with few features. We proposed a more accurate measuring method for ultra-long scale objects based on 2D laser positioning. The core of this technology is achieving accurate long-distance 2D positioning to obtain absolute coordinates in ultra-long structures. A positioning method based on machine vision is proposed to work with low-cost laser generator, with diffractive large diameter laser beam. A fast calibration method is generated to calibrate the positioner by capturing calibration board. Image processing is taken to extract spot core from diffraction spot image and calculate its center to present relative position between laser beam and positioner. Calibration experiment is carried out. The calibrate error is about 0.055 mm. 100 m positioning experiment is designed. It is verified that positioning accuracy is about ±0.3 mm.
Flow Velocity Computation in Solid-liquid Two-phase Flow by Convolutional Neural Network

Flow velocity calculation is one of main tasks in the solid-liquid two-phase parameter detection in dredging engineering. The existing calculation methods for flow velocity mainly are on basis of cross-correlation principle and electrical resistance tomography technique. However, under various flow patterns and complex measurement conditions, these methods remain very inaccurate. In this study, the convolutional neural network is used to calculate flow velocity owing to the generalization and feature extraction ability, while the transfer learning method can train the network in advance. And the normalized electrical resistance tomography image as the input of the network, and the corresponding flow velocity as the output under the condition of the big data with different soil properties and different solid-phase fractions. The average accuracy of the used network is about 21% higher than that of the existing methods for flow velocity calculation. The effectiveness and generalization of the used network is verified by various experiments in dredging engineering.
Portable Data Acquisition and Fluidic System for Electrochemical Sensors

The recent outbreak of infectious diseases has highlighted the necessity of point-of-care detection compared to central lab analysis for more effective epidemic control. Recent developments in the field of biosensors have allowed sensitive, accurate disease diagnosis using low-cost devices. In this work, we describe the development of a portable data acquisition and fluidic system for miniature electrochemical biosensors. The data acquisition system was designed as a single printed circuit board and can perform cyclic voltammetry. The fluidic chamber was designed to work with three miniature sensors which are placed on a single platform. Leakage tests were performed to ensure that each chamber allows sensor isolation and avoids any cross- contamination. Measurements using the fabricated potentiostat board were taken and compared with a commercial potentiostat. It was found that the designed potentiostat was able to measure the same resolution and peak separation in cyclic voltammetry measurements.
COMPARISON OF METHODS OF MEAN BLOOD PRESSURE CALCULATION USING AMBULATORY BLOOD PRESSURE MONITORING RESULTS

Ambulatory blood pressure monitoring (ABPM) is a modern method of BP investigation. However, only some ABPM parameters, as diurnal systolic BP (SBP) and diastolic BP (DBP), are used now as the diagnostic guide according to international recommendations. It is pure knowledge in clinical practice about usage and calculation of mean blood pressure (MBP). Such parameter is the steady flow of blood through the aorta and its arteries and equals the cardiac output multiplied by vascular resistance [1], according to some clinical studies may have predictable value as SBP or DBP, however there is no clinical evidence about these parameters, as information how to calculate in ABPM. The aim of the study is to compare the mean blood pressure calculated by two methods using ABPM parameters. We selected 20 ambulatory blood pressure monitoring (ABPM) patients (median of age 60 [56; 64] years, 65 % women). ABPM was recorded using a bifunctional device (Incart, S.-P., RF). We calculated MBP as [[(2×DBP)+SBP]/3] [2] and as [DBP+[0.01×exp(4,14-40,74/HR)] ×PP] [3], Formula 1 and Formula 2, respectively. The analysis of difference between results of MBP measurements calculated by two methods was performed using the Bland and Altman method. The data show, that MBP calculated by Formula 1 and Formula 2 are as 87.7 [83.2−90.5] and 91.1 [85.4−93.5] mm Hg, respectively. The limits of agreements for the MBP were 0,49/5,91. In general, based on the findings of the current study, the measurements of MBP calculated using different formulas show good agreement. Further studies need to have more clinical evidence to analyze the result
Template-based Neural Program Repair

In recent years, template-based and NMT-based automated program repair methods have been widely studied and achieved promising results. However, there are still disadvantages in both methods. The template-based methods cannot fix the bugs whose types are beyond the capabilities of the templates and only use the syntax information to guide the patch synthesis, while the NMT-based methods intend to generate the small range of fixed code for better performance and may suffer from the OOV (Out-of-vocabulary) problem. To solve these problems, we propose a novel template-based neural program repair approach called TENURE to combine the template-based and NMT- based methods. First, we build two large-scale datasets for 35 fix templates from template-based method and one special fix template (single-line code generation) from NMT-based method, respectively. Second, the encoder-decoder models are adopted to learn deep semantic features for generating patch intermediate representations (IRs) for different templates. The optimized copy mechanism is also used to alleviate the OOV problem. Third, based on the combined patch IRs for different templates, three tools are developed to recover real patches from the patch IRs, replace the unknown tokens, and filter the patch candidates with compilation errors by leveraging the project-specific information. On Defects4J-vl.2, TENURE can fix 79 bugs and 52 bugs with perfect and Ochiai fault localization, respectively. It is able to repair 50 and 32 bugs as well on Defects4J-v2.0. Compared with the existing template-based and NMT-based studies, TENURE achieves the best performance in all experiments.
CFD Simulation of the Flow Reversal Transient in a High Voltage Circuit Breaker

In a high voltage circuit breaker (HVCB) the current interruption process has different phases: high current phase, before current zero phase and after current zero phase. As a result, the gas flow reverses typically 1-3 ms before current zero (CZ) and the arc is blown when the current approaches zero. The flow reversal transient has an impact on the cooling of arc around CZ and thus on the interruption performance for both higher and lower currents. In this work, the flow reversal phenomenon is investigated numerically by simulating all current phases. Flow reversal transients, as observed in the simulation, are confirmed by the measurement. Numerical evaluations are carried out to understand the flow reversal transients and influencing parameters.
Noise and Uncertainty Analysis for Time and Frequency Domain Vibration Measurements using Acceleration Sensors

Displacement measurement systems based on acceleration sensors provide an interesting sensing approach for vibration monitoring. Hereby the displacement is computed by a double integration of the acceleration signals. The main challenge hereby are offset signals in the acceleration signal. Hence a lower cutoff frequency has to be determined for this sensing principle. However, not only DC components in the acceleration signal have to be considered, but also the measurement noise in the acceleration signal has to be taken into account. In this work we present a noise and uncertainty analysis for accelerometer based displacement measurement using a MEMS based sensor. We hereby investigate time domain and frequency domain displacement measurements. Theoretical results and comparative numerical studies are presented.
Diver: Oracle-Guided SMT Solver Testing with Unrestricted Random Mutations

We present Diver, a novel technique for effectively finding critical bugs in SMT solvers. Ensuring the correctness of SMT solvers is becoming increasingly important as many applications use solvers as a foundational basis. In response, several approaches for testing SMT solvers, which are classified into differential testing and oracle-guided approaches, have been proposed until recently. However, they are still unsatisfactory in that (1) differential testing approaches cannot validate unique yet important features of solvers, and (2) oracle-guided approaches cannot generate diverse tests due to their reliance on limited mutation rules. Diver aims to complement these shortcomings, particularly focusing on finding bugs that are missed by existing approaches. To this end, we present a new testing technique that performs oracle-guided yet unrestricted random mutations. We have used Diver to validate the most recent versions of three popular SMT solvers: CVC5, Z3 and dReal. In total, Diver found 25 new bugs, of which 21 are critical and directly affect the reliability of the solvers. We also empirically prove DIVER's own strength by showing that existing tools are unlikely to find the bugs discovered by Diver.
Force sensing utilizing a CoFeSiB microwire: a preliminary experimental study

Amorphous materials, and FeSi-based in particular, have peculiar magneto-mechanical properties which promote their use in many applications: from microelectronics to instrumentation and measurement. In this study, we investigate the use of a CoFeSiB microwire for force sensing purposes. We utilize an inductive method and a linear actuator to assess the magnetic sample behavior while applying a controlled tensile force. The results showed a complex behavior of the magnetization saturation and residual magnetization vs the tensile stress; however, after a threshold force value, a linear relationship is observed between the magnetic parameters and the applied force. Therefore, the performance utilizing different readout strategies are assessed, and the results are discussed concerning the recent literature.
DRL-FORCH: A Scalable Deep Reinforcement Learning-based Fog Computing Orchestrator

We consider the problem of designing and training a neural network-based orchestrator for fog computing service deployment. Our goal is to train an orchestrator able to optimize diversified and competing QoS requirements, such as blocking probability and service delay, while potentially supporting thousands of fog nodes. To cope with said challenges, we implement our neural orchestrator as a Deep Set (DS) network operating on sets of fog nodes, and we leverage Deep Reinforcement Learning (DRL) with invalid action masking to find an optimal trade-off between competing objectives. Illustrative numerical results show that our Deep Set-based policy generalizes well to problem sizes (i.e., in terms of numbers of fog nodes) up to two orders of magnitude larger than the ones seen during the training phase, outperforming both greedy heuristics and traditional Multi-Layer Perceptron (MLP)-based DRL. In addition, inference times of our DS-based policy are up to an order of magnitude faster than an MLP, allowing for excellent scalability and near real-time online decision-making.
Deteksi Dan Klasifikasi Tingkat Keparahan Retinopati Diabetes Dengan Menggunakan Metode Klasifikasi K - Nearest Neighbor

Retinopati diabetes merupakan kerusakan yang terjadi pada mata akibat dari penyakit dibetes mellitus yang menahun. Tingginya kadar glukosa dalam darah adalah penyebab pembuluh darah kapiler kecil menjadi pecah dan dapat menyebabkan kebutaan. Menurut artikel pada harian online kompas terbitan 15/08/08, diestimasi bahwa jumlah penderita diabetes yang semula berjumlah 117 juta pada tahun 2000 akan meningkat mennjadi 366 juta di tahun 2030[4]. Pada harian online itu juga dikabarkan bahwa di asia, diabetes akan menjadi “epidemi” dikarenakan pola makan orang Asia yang berkarbohidrat juga berlemak tinggi dan itu semua tidak diimbangi dengan olahraga yang baik. Dengan naiknya jumlah penderita diabetes maka akan berbanding lurus dengan naiknya jumlah penderita retinopati diabetes. Pada tugas akhir sebelumnya telah dibuat sistem yang dapat mendeteksi dan mengklasifikasikan penyakit retinopati diabetes dengan akurasi sebesar 67,86%[9]. Pada tugas akhir kali ini dibuat perangkat lunak yang dapat mendeteksi dan mengklasifikasi tingkat keparahan retinopati diabetes dengan menggunakan transformasi curvelet dan metode klasifikasi K – Nearest Neighbor (KNN). Sistem ini dapat menggolongkan tingkat keparahan retinopati diabetes tipe non proliferative kedalam empat tingkatan, yaitu non diabetic retinopathy, mild, moderate, dan severe. Dari hasil analisis dan pengujian yang dilakukan pada sistem ini, didapatkan tingkat akurasi sebesar 65%. Kata kunci : Retinopati diabetes, Curvelet, K – Nearest Neighbor.
Coverage Guided Fault Injection for Cloud Systems

To support high reliability and availability, modern cloud systems are designed to be resilient to node crashes and reboots. That is, a cloud system should gracefully recover from node crashes/reboots and continue to function. However, node crashes/reboots that occur under special timing can trigger crash recovery bugs that lie in incorrect crash recovery protocols and their implementations. To ensure that a cloud system is free from crash recovery bugs, some fault injection approaches have been proposed to test whether a cloud system can correctly recover from various crash scenarios. These approaches are not effective in exploring the huge crash scenario space without developers' knowledge. In this paper, we propose Crash Fuzz, a fault injection testing approach that can effectively test crash recovery behaviors and reveal crash recovery bugs in cloud systems. CrashFuzz mutates the combinations of possible node crashes and reboots according to runtime feedbacks, and prioritizes the combinations that are prone to increase code coverage and trigger crash recovery bugs for smart exploration. We have implemented CrashFuzz and evaluated it on three popular open-source cloud systems, i.e., ZooKeeper, HDFS and HBase. CrashFuzz has detected 4 unknown bugs and 1 known bug. Compared with other fault injection approaches, CrashFuzz can detect more crash recovery bugs and achieve higher code coverage.
A Diminutive Multi-Octave Super Wideband Printed LPDA Antenna For EW Applications

A diminutive multi-octave super wideband Printed Log Periodic Dipole Array (PLPDA) antenna is presented in this communication. Its operational frequency band is 1.5 to $33GHz$. The introduced design has a super wide impedance bandwidth (182.61%) with a maximum realized gain of $9dB$ and over the band VSWR $< 2$. It has a compact size and simple classical structure thus having ease of fabrication. The accomplishment of the propounded antenna makes it competent for electronic warfare (EW) systems as it is befitting for $L$, S, C, X, Ku, and Ka band applications. The antenna modeling and simulations are performed by using ANSYS HFSS (High-Frequency Structure Simulator).
A physics‐based model for the ionization of samarium by the MOSC chemical releases in the upper atmosphere

Atomic samarium has been injected into the neutral atmosphere for production of electron clouds that modify the ionosphere. These electron clouds may be used as high‐frequency radio wave reflectors or for control of the electrodynamics of the F region. A self‐consistent model for the photochemical reactions of Samarium vapor cloud released into the upper atmosphere has been developed and compared with the Metal Oxide Space Cloud (MOSC) experimental observations. The release initially produces a dense plasma cloud that that is rapidly reduced by dissociative recombination and diffusive expansion. The spectral emissions from the release cover the ultraviolet to the near infrared band with contributions from solar fluorescence of the atomic, molecular, and ionized components of the artificial density cloud. Barium releases in sunlight are more efficient than Samarium releases in sunlight for production of dense ionization clouds. Samarium may be of interest for nighttime releases but the artificial electron cloud is limited by recombination with the samarium oxide ion.
Does the Stream API Benefit from Special Debugging Facilities? A Controlled Experiment on Loops and Streams with Specific Debuggers

Java's Stream API, that massively makes use of lambda expressions, permits a more declarative way of defining operations on collections in comparison to traditional loops. While experimental results suggest that the use of the Stream API has measurable benefits with respect to code readability (in comparison to loops), a remaining question is whether it has other implications. And one of such implications is, for example, tooling in general and debugging in particular because of the following: While the traditional loop-based approach applies filters one after another to single elements, the Stream API applies filters on whole collections. In the meantime there are dedicated debuggers for the Stream API, but it remains unclear whether such a debugger (on the Stream API) has a measurable benefit in comparison to the traditional stepwise debugger (on loops). The present papers introduces a controlled experiment on the debugging of filter operations using a stepwise debugger versus a stream debugger. The results indicate that under the experiment's settings the stream debugger has a significant ($\mathrm{p} < .001$) and large, positive effect $(\eta_{p}^{2}=.899;\ \frac{M_{stepwise}}{M_{stream}} \sim 204\%)$. However, the experiment reveals that additional factors interact with the debugger treatment such as whether or not the failing object is known upfront. The mentioned factor has a strong and large disordinal interaction effect with the debugger ($\mathrm{p} < .001; \eta_{p}^{2}=.928$): In case an object is known upfront that can be used to identify a failing filter, the stream debugger is even less efficient than the stepwise debugger $(\frac{M_{stepwise}}{M_{stream}}\sim 72\%)$. Hence, while we found overall a positive effect of the stream debugger, the answer whether or not debugging is easier on loops or streams cannot be answered without taking the other variables into account. Consequently, we see a contribution of the present paper not only in the comparison of different debuggers but in the identification of additional factors.
Kinect V2 Camera Based Vision System for Robotic Grinding

The vision system often plays a vital auxiliary role in the high-precision grinding process of the robot. This paper presents an eye-in-hand vision system which incorporates a depth camera on the six-axis robotics and scans from multiple angles to extract the information of workpieces to be ground. Contour and centroid extraction were applied for locating workpiece and planning path of fine scanning. After obtaining the multi-angle point cloud images, we reconstructed the point cloud of the workpiece surface through point cloud registration and segmentation. Harris corner detection was used to recognize bumps on the surface. Then grinding information was uniformly converted to the robot coordinate system through coordinate transformation. The experimental results show that this system can obtain comprehensive and precise grinding information, ensuring the smooth execution of robotic grinding.
Understanding and Detecting On-The-Fly Configuration Bugs

Software systems introduce an increasing number of configuration options to provide flexibility, and support updating the options on the fly to provide persistent services. This mechanism, however, may affect the system reliability, leading to unexpected results like software crashes or functional errors. In this paper, we refer to the bugs caused by on-the-fly configuration updates as on-the-fly configuration bugs, or OCBugs for short. In this paper, we conducted the first in-depth study on 75 real-world OCBugs from 5 widely used systems to understand the symptoms, root causes, and triggering conditions of OCBugs. Based on our study, we designed and implemented Parachute, an automated testing framework to detect OCBugs. Our key insight is that the value of one configuration option, either loaded at the startup phase or updated on the fly, should have the same effects on the target program. Parachute generates tests for on-the-fly configuration updates by mutating the existing tests and conducts differential analysis to identify OCBugs. We evaluated Parachute on 7 real-world software systems. The results show that Parachute detected 75% (42/56) of the known OCBugs, and reported 13 unknown bugs, 11 of which have been confirmed or fixed by developers until the time of writing.