Miracle-dz/UniFAR-FTUs huggingface dataset:
The effects of alignment and ellipticity on the clustering of galaxies

We investigate the effects of halo ellipticity and alignment with larger-scale structure on the galaxy correlation function. We base our analysis on the galaxy formation models of Guo et al. (2011), run on the Millennium Simulations. We quantify the importance of these properties of the galaxy distribution by randomizing the angular positions of satellite galaxies within haloes, either coherently or individually, while keeping the distance to their respective central galaxies fixed. We find that the effect of disrupting the alignment with larger-scale structure is a ~2 per cent decrease in the galaxy correlation function around r=1.8 Mpc/h. Sphericalizing the ellipsoidal distributions of galaxies within haloes decreases the correlation function by up to 20 per cent for r<1 Mpc/h. Similar results apply to power spectra and redshift-space correlation functions. Models such as those based on the Halo Occupation Distribution, which adopt a spherically averaged profile for the galaxy distributions within haloes, will therefore significantly underestimate the clustering on sub-Mpc scales.
Higher-Order Filters With Folded Circular SIW Cavities

The seventh-order filter, this contribution discusses, relies on folded circular substrate integrated waveguide (FCSIW) cavities. It comprises two different cavity types and exhibits 5% fractional bandwidth at 10-GHz center frequency. Simulation and measurements demonstrate its functionality. According to its order, the filter shows high isolation over a large frequency band. The experiments validate the proposed layout procedure for FCSIW cavity filters of arbitrary filter order.
Motion Compensation for Body-frame Doppler Estimation of Radar Sensors on Multi-rotor UAV Platforms

We propose a motion compensation method for body-frame Doppler estimation using radar applications mounted on a multi-rotor uncrewed aerial vehicle (UAV). The Doppler shift resulting from orientation variation can be corrected by a simple model to transform the Doppler into the body frame with the fusion of radar measurements and data from the inertial measurement unit (IMU). We verify this model through multiple coherent pulse intervals (CPIs) based on our own radar simulator for real time kinematic (RTK) data in a forward flight scenario. After applying the proposed motion compensation method, the RMSE for the ego-velocity was improved to 0.0688.
TIPS: Transformer Based Indoor Positioning System Using Both CSI and DoA of WiFi Signal

In a channel state information (CSI) based indoor positioning system, the positioning performance becomes susceptible to multipath fading effects especially in non-line-of-sight environments. We propose a transformer-based indoor positioning system (TIPS) to address this challenge. The proposed TIPS utilizes a self-attention mechanism to process the continuous WiFi CSI observed from predetermined routes as fingerprints in a given indoor environment. Each route is then considered a sentence, whereas the position along the route is treated as a word in terms of natural language processing. Consequently, the problem of predicting the position with the fingerprints can then be considered the task of predicting the current word with previous words, which can be efficiently solved using the proposed TIPS. In order to fully exploit the relations among positions, we propose embedding the information of the direction of arrival (DoA) on top of the collected CSI as inputs to the TIPS. Thus, the transformer of the proposed TIPS can better capture the dependencies of the positions in the route and significantly boost positioning accuracy. To exhibit the superiority of the proposed TIPS in a radio frequency (RF) environment, we demonstrate a hardware implementation of an RF testbed consisting of an emulator of WiFi access point and user equipment. Through extensive computer simulations and experimental tests, it is demonstrated that the proposed TIPS can reduce the positioning error down to 20 cm, which is a significant improvement compared to the current state-of-the-art models.
On Soft Decoding of Some Binary RLL-Transmission Codes in Systems with Coherent BPSK Modulation

Recently some RLL-LDPC and RLL-Turbocodes were proposed. In BSC the hard decoding methods applied for the original codes - not possessing the RLL properties could be used for these codes without any changes. The questions arise if also the standard soft decoding methods could be used for their decoding in Additive White Gaussian Noise channel without changes. In this paper it is shown that the answer is positive if coherent BPSK modulation is used and the received signal is filtered by Matched Filter. It is shown that the only necessary operation is the inversion of the log-likelihood ratio forming the input to the decoder from the channel for values corresponding to the inverted bits by modifier in transmitter.
The High Time Resolution Universe survey – XIV. Discovery of 23 pulsars through GPU-accelerated reprocessing

We have performed a new search for radio pulsars in archival data of the intermediate and high Galactic latitude parts of the Southern High Time Resolution Universe pulsar survey. This is the first time the entire dataset has been searched for binary pulsars, an achievement enabled by GPU-accelerated dedispersion and periodicity search codes nearly 50 times faster than the previously used pipeline. Candidate selection was handled entirely by a Machine Learning algorithm, allowing for the assessment of 17.6 million candidates in a few person-days. We have also introduced an outlier detection algorithm for efficient radio-frequency interference (RFI) mitigation on folded data, a new approach that enabled the discovery of pulsars previously masked by RFI. We discuss implications for future searches, particularly the importance of expanding work on RFI mitigation to improve survey completeness. In total, we discovered 23 previously unknown sources, including 6 millisecond pulsars and at least 4 pulsars in binary systems. We also found an elusive but credible redback candidate that we have yet to confirm.
UHF-RFID AoA Positioning with Multiple Arrays on Agricultural Machinery

Worker safety in agricultural and forestry sites where Remotely Controlled Farm Machinery (RCFM) operate is a serious problem which requires the development of innovative smart systems. Radiolocalization of workers developing their tasks in the machinery vicinity turns out to be an effective method to prevent fatal collisions. In this context, passive RFID technology at the UHF band is a favorable solution due to the simplicity of installation. This paper proposes the use of two commercial RFID arrays to triangulate the location of workers and other obstacles in the surroundings of a RCFM. optimization and performance of the system geometry are investigated by numerical analysis as the most significant parameters change, by obtaining sizing criteria useful for on-site deployment of the system.
Sensorless Control of PMSM Based on Active Flux Observer and MRAS Stator Resistance Identification

This paper introduces a robust sensorless control system for permanent magnet synchronous motor (PMSM) drives using the active flux observer with serial combined PMSM voltage and current models. To assure system robustness, the stator resistance identification is proposed using a MRAS with the stator current angle error in adaptation mechanism. The extensive simulation results prove the effectiveness of the proposed sensorless control system with very good steady state and dynamic performance in large speed range 1:50, with minimum speed of 20 rpm (2% rated speed) and 1°max rotor position error, for step speed and rated torque references, step speed reversal at lower speed, and linear variation of stator resistance.
Multivariate Sequential Modelling for Student Performance and Graduation Prediction

High dropout rate and low student performance were inevitable issues for educational institutions in many countries. Consequently, this study presents an automated technique to predict student performance and graduation using student data with separated and combined prediction method. The data was collected from an Indonesia university. Long Short-Term Memory (LSTM) and Gate Recurrent Units (GRU) as an outstanding model in handling sequence data was proposed in this study. According to our study, both LSTM and GRU have a great performance above 90% in predicting each task. The performance of both architecture was surpass each other depending on the corresponding task. In early prediction, the student graduation prediction can give a satisfiable performance since the first semester, despite having tradeoff in recall. Whereas in student performance prediction, the RMSE value was acceptable since the second semester. Overall, the performance of student performance and graduation prediction was better if used separated method than combine method. This pipeline work can be replicate and improve for similar task in other universities with feature adjustments based on data availability.
Characteristics of Rational Classifications in Game-Theoretic Algorithms of Pattern Recognition for Unmanned Vehicles

In pattern recognition, depending on how the training sample is represented, a theory associated with the standard representation of the training sample (information) and the structural theory are distinguished. In the first case it is convenient to set and describe objects that are independent of each other, objects that have a rigid, static nature. In the second case, when information is specified in a structural way, it is convenient to study, describe and recognize objects whose basis presents an invariable internal or external structure. This paper considers a fundamentally different case of setting learning information. It deals with objects and processes that are based on conflict. Such features are peculiar to the data sets that an unmanned vehicle receives while driving, which requires minute-by-minute formation and decision-making in the context of the current traffic situation. Naturally, it is necessary to involve game-theoretic models to describe conflicts. It is these issues, i.e., the systematic study of game-theoretic models in the problems of recognition and classification, that the work is devoted to. It is emphasized that the classification of recognizable objects, as some speculative system, must meet the requirements of sufficiency of grounds and coverage of the totality of existing and possible recognizable objects. The most important purpose of classification is to describe the properties of its classes and subclasses, types and subspecies of recognized objects, which makes it possible for it to be used for recognizing specific objects, which subjects encounter in certain areas of activity. The paper introduces the concept of stable classification, formulates and proves the theorems based on the introduced concept, the sufficiency of the conditions for the correctness of the algorithm of pattern recognition, using the game-theoretic approach and evaluation of the degree of stability of the classification underlying it. The generated theorems allow the most optimal synthesis of algorithms for constructing a regular mapping and a matched continuation family, which, in turn, makes it possible for us to take into account the relationships in the training sample written in the form of a game-theoretic training model.
Evaluation of Life Span of PV Inverter

This paper presents an evaluation of the life span of the photovoltaic inverter and its components. The basic methodology for estimation of the transistors and capacitors in the inverter are presented. The proposed solutions are useful for definition of the time for exploitation and how the energy efficiency of the photovoltaic system could be improved by rehabilitating the inverter and its components.
Comparison of volumetric measurement methods in medicine

Nowadays, digital pathology is an unavoidable element in making a medical diagnosis. This discipline makes it possible to carry out studies that in the past were only inaccurate or not possible at all. An example of such a test is the volumetric measurement, in which the medical professional is able to determine the volume of a piece of tissue. New technologies such as 3D visualization make it possible to perform such medical examinations quickly and accurately. In this paper, we present some of the imaging modalities currently used in medicine to perform volumetric measurement, and we present our own volumetric measurement solution, which we created with a graphics engine, in order to take advantage of polygon-based visualization. At the end of the paper, we present test results for our own volumetric measurement solution.
112 GBaud (224 Gb/s) large output swing InP DHBT PAM-4 DAC-driver

In this paper, we report on the design, optimisation and and electrical measurements of a PAM-4 DAC-driver fabricated in 0.7-μm InP/GaAsSb DHBT technology, with the capability to directly drive an electro-optical modulator. Circuit measurements in PAM-4 mode show high diagram quality at 90 GBaud (180 Gb/s) with a record 5.5–Vpp differential output swing. An over 12-dB gain control capability is shown. Moreover, a record 3.35-Vpp output swing is obtained in PAM-4 at 112 GBaud (224 Gb/s). Power consumption is 1.1 and 0.6 W for two operating symbol-rates respectively.
Automotive Radar Parameter Estimation for Cognitive Interference Mitigation

A methodology to estimate waveform parameters of unknown interfering signals and number of interfering sources within the field of view of victim radar (VR) is presented using real time measured data. On the basis of identified interference parameters, waveform parameters of VR can be cognitively adapted to mitigate interference using ‘sense and avoid’ approach.
Optimal sizing of behind-the-meter BESS for providing stackable services

The behind-the-meter (BTM) battery energy storage system (BESS) is mainly utilized for providing load management. But the saved electricity bill hardly offsets the high upfront investment cost. The multi-revenue streams created by certain stackable services can offset the initial cost by reasonably designing the size and operation strategy of BESS. Therefore, to maximize the return rate on BESS investment, a two-stage optimal model for optimizing the power and energy capacity of a BTM BESS is proposed in this paper. The provided stackable services by BESS include energy arbitrage and frequency regulation. A hybrid algorithm combining the genetic algorithm and a mixed-integer linear programming model is employed to co-optimize the size and operation strategy of BESS. The real load data from the plastic manufacturing industry and the frequency regulation information from the PJM market are employed as databases to validate the availability and effectiveness of the proposed model and the hybrid algorithm.
A High Linearity 6 GHz LNA in 130 nm SiGe Technology

In this work, a high linearity LNA at 6 GHz with 1.15 dB NF, 27.7 dB gain is reported with IHP 130nm SiGe BiCMOS technology. The reported LNA achieves a highly linear performance and has -12.2 dBm IP1dB and 4.2 dBm IIP3. A two-stage cascode amplifier with inductive degeneration topology is used to obtain high gain. Optimum transistor biasing and sizing achieves an input matching without using any inductor which allowed low NF and high linearity. Additionally, custom, not PDK defined, transistor layouts have been created to decrease the core's parasitics to enhance the LNA's performance. LNA uses 3.3V as supply and consumes 98 mW DC power while occupying a 0.75 mm2 die area without pads. The reported LNA achieved one of the highest figures-of-merit among silicon-based LNAs and has comparable performance to SOI CMOS LNAs.
Context-enriched modeling using Knowledge Graphs for intelligent Digital Twins of Production Systems

Current industrial automation systems are facing increasing dynamics. Thus, acquiring and managing heterogeneous data within the Digital Twin to enable decision making is necessary although challenging. Knowledge Graphs unify and relate data, enabling the derivation of new insights. In this contribution, an approach for context-enriched modeling of cyber-physical production systems is proposed, in order to realize a Knowledge Graph enhanced intelligent Digital Twin further considering the context. Therefore, the modeling approach of the Knowledge Graph considers context and serves as a base for the graph embeddings to gain further knowledge about the production system. This knowledge is used within the architecture of the intelligent Digital Twin. The resulting benefits, i.e. diverse manifestations of an improved decision making, are highlighted by the use case of self-organized reconfiguration management.
How Manufacturers Can Easily Improve Working Range of Passive RFIDs

Radio-Frequency IDentification (RFID) technology permits a reader to wirelessly query a tag for its embedded globally unique identifier. Passive RFID tags, which are small, low-cost (a few cents each), and batteryless, can be reliably read only when they are within a few meters of the reader since the tag must power up itself by harvesting energy from the reader. Past work attempts to increase the RFID range by providing them with more energy, such as by synchronizing multiple custom design RFID readers and performing beamforming. However, we demonstrate that a passive tag's range is limited not only by the need for the tag to harvest energy but also by the need for the tag to decode the reader's transmission, and vice versa. Thus, instead of modifying readers, we ask if a tag's manufacturer can increase passive RFIDs' range by lowering the data rate. Our results show that the working range can be increased by a factor of about 10 by simply using a low data rate. Our real-world experiments using customized tag prototypes have a range of ~40 m, with an SNR exceeding 12 dB.
Adaptive Mode Transformation for Wear Leveling in Nonvolatile FPGAs

Nowadays, field programmable gate arrays (FPGAs) have been widely adopted to serve as accelerators in artificial intelligence and big data related applications. Since the static random access memory (SRAM)-based FPGA is suffering from limited density and high leakage power, nonvolatile FPGAs have been proposed, where SRAM is replaced with emerging nonvolatile memories (NVMs). Multilevel cell (MLC), which can store multiple bits within one memory cell, further improves the density of nonvolatile FPGAs and shows great potential to enable large on-chip memory. However, it suffers from limited lifetime. In this article, we propose a wear leveling scheme to improve lifetime of MLC-based nonvolatile FPGAs. Instead of generating a series of configuration files for runtime reconfiguration, we propose to identify write-heavy MLC regions and dynamically transform them to durable single-level cell (SLC) mode. Specifically, we propose three modules: 1) pertaining to write behavior monitor; 2) approximate cost calculator; and 3) mode transformation manager to achieve adaptive mode transformations. We consider FPGA features to design these modules, which is different from implementations for MLC-SLC transformation in CPU architecture. Evaluation shows that the proposed scheme can improve lifetime for MLC nonvolatile FPGAs by $6.03\times $ , at cost of 12.5% storage overhead.
On cost-effective strategies for opinion diffusion in complex networks

Better understanding of diffusion dynamics in complex networks is of notable scientific and social interest, since it allows predicting, controlling, and delaying information, innovation, or even epidemics. The most effective strategy for fast and impactful opinion dissemination is to use social agents to diffuse and indoctrinate neighboring peers, be it online or offline. Although these agents imply a real-world cost of operation, there is limited literature on the trade-offs between diffusion performance and incurred costs. Our study incorporates a cost awareness model into the classic linear threshold opinion diffusion model and studies the effect of variable network topology, number of spreaders, and spreader active time (i.e., injection strategies). By performing detailed discrete event simulations on several types of network topologies, we uncover a set of general rules for a cost-effective approach in targeting real-world scenarios of influence maximization. Specifically, we determine that a constant opinion injection incurs costs of up to 80% higher than intermittent injection, that increasing the number of spreaders results in a polynomial increase in the cost of operation, and that irregular (random, preferential attachment) topologies imply diffusion costs of up to 3 times higher than regular topologies.
Transitive Transfer Learning for Lungs CT Segmentation

This paper studies the effect of transitive transfer learning (TTL) in the medical image processing field on the practical task of tuberculosis (TB) diagnosis. A series of experiments on semantic segmentation of nodules on lung CT images based on ImageNet, LIDC-IDRI and a private dataset with pulmonary tuberculosis of tuberculoma type were performed. The LIDC-IDRI dataset was selected as a transitive one among other datasets based on the Optimal Transport Dataset Distance (OTDD) metric. The TTL with LIDC-IDRI dataset in this case is designed to reduce the distribution gap between the ImageNet dataset and the private TB dataset. DeepLabV3+ was used for the experiments. The following approaches based on this network were compared: training the network on a private dataset from scratch, training the network on a private dataset using ImageNet dataset weights, pre-training the network on a LIDC-IDRI dataset using ImageNet weights, with post-training on a private tuberculosis dataset. In this way, the effectiveness of conventional deep learning, transfer learning, and transitive transfer learning approaches in the popular lung CT image domain is compared. Experiments have shown that using transitive transfer learning with the LIDC-IDRI dataset effectively bridges the distributional gap between the ImageNet source domain and the target domain of the private tuberculosis dataset. The accuracy increments for mIoU, F1 and F2 score were 0.1622, 0.1577 and 0.1645, respectively. The obtained results proved the efficiency of TTL with dataset selection based on OTDD in the task of segmentation of CT lung images on private practical datasets.
Concept for a Distributed Picking Application Utilizing Robotics and Digital Twins

The following paper describes a concept for a grasping application which utilizes technologies from the fields of distributed computing, robotics and Digital Twins in the sense of Industry 4.0. Hereby, the goal of the application is to have a computer vision system detect toy bricks which a robot has to pick and pass to a user. The application is divided into loosely coupled and distributed services that communicate with one another using a message broker.