Miracle-dz/UniFAR-FTUs huggingface dataset:
Transition-edge sensor array development

Transition Edge Sensors (TES) have been under heavy research in recent years thanks to their excellent energy resolution and high quantum efficiency. TES is based on the sharp transition between the superconductive and the normal state in superconductive thin films. When a single photon hits a TES its energy is converted into heat, which can be measured with this sensitive detector. TES was first developed to be used in space research for radiation detection. Nowadays they are finding new ground in other research areas thanks to their superior features and the fact that cooling techniques together with readout techniques have matured enough. The main motivation for our research is to develop TES arrays for detectors in e.g. X-ray absorption spectroscopy. The processing of TESs fabricated in Nanoscience Center in Jyväskylä are presented as well as the first 32 pixel TES array. In this thesis the basic properties of TESs like critical temperature and resistivity of the absorber material are under investigation. Also the use of ALD deposited insulator layer for the so-called Corbino-TES (CorTES) is discussed. Transitioreunailmaisimia (TES) on tutkittu viime vuosien aikana paljon niiden erinomaisen energia resoluution sekä korkean suorituskykynsä ansiosta. TES-ilmaisimilla mitataan säteilyä ja niiden toiminta perustuu suprajohtavanja normaalitilan välillä tapahtuvaan herkkään vastuksen muutokseen suprajohtavissa ohutkalvoissa lämpötilan muuttuessa. Kun TES:iin osuu yksittäinen fotoni, sen energia muuttuu lämmöksi, joka voidaan mitata tällä herkällä ilmaisimella. Näin saadaan tietoa säteilyn spektristä erittäin tarkasti. Alun perin avaruustutkimukseen suunnitellut laitteet ovat alkaneet saada jalansijaa myös muilla tutkimusalueilla niiden erinomaisten ominaisuuksien ansiosta. Niiden resoluutio, tehokkuus ja lähivuosina tarpeeksi kehittyneet mittausjärjestelmät tekevät siitä halutun menetelmän useihin eri sovellutuksiin. Suurin motivaatio tämän työn takana on kehittää usean pikselin TES-matriiseja esim. röntgenabsorptiospektroskopiaa varten. Tässä työssä esitellään Jyväskylän yliopistossa Nanoscience Centerissä kehitettyjen TES:ien ominaisuuksia, kuten niiden kriittisiä lämpötiloja ja esim. absorberimateriaalin resistiivisyyttä. Työssä esitellään myös esimerkiksi ALD-menetelmällä tuotetun eristemateriaalin käyttö ns. Corbino-TES:ien (CorTES) valmistuksessa.
A NEW MIXED PRECONDITIONING METHOD BASED ON THE CLUSTERED ELEMENT -BY -ELEMENT PRECONDITIONERS

We describe a new mixed preconditioning method for finite element computations. In the clustered element-by-element (CEBE) preconditioning, the elements are merged into clusters, and the preconditioners are defined as series products of cluster level matrices. The (cluster companion) CC preconditioners are based on companion meshes associated with different levels of clustering. For each level of clustering, we construct a CEBE preconditioner and an associated CC preconditioner. Because these two preconditioners complement each other, when they are mixed, they give better performance. Our numerical tests, for two- and three-dimensional problems governed by the Poisson equation, show that the mixed CEBE/CC preconditioning results in convergence rates which are significantly better than those obtained with the best of the CEBE and CC methods.
Detection of prompt luminescence from an imaging plate with heavy particle injection

We have been investigating the basic characteristics of an imaging plate JP) to develop a new type of ultra-heavy-particle detector. To acquire the information on the deposited energy, photostimulated luminescence (PSL) from the injection position is usually utilized, which occurs upon scanning the surface of the IP with a laser beam after the exposure of particles. However, in the use of PSL, there is the disadvantage that it fades with time. On the other hand, when heavy particles are injected to the IP, prompt luminescence (PL) also occurs from the injection position. We have succeeded in detecting the prompt luminescence from each heavy particle with the energy of several hundred MeV/n. Since the PL is useful for quick detection of the injection position, the PSL data can be read within a few seconds by scanning the determined position before any serious fading effect occurs.
A Direct Adaptive Controller With Harmonic Compensation for Grid-Connected Converters

Grid-connected converters are uncertain time-varying systems, whose controllers must deal with the relevant inductive content and high harmonic distortion of the sampled voltages. Adaptive controllers are feasible control strategies for these applications, because their gains are continuously adjusted in response to system perturbations, and they are also robust to unmodeled dynamics and exogenous disturbances. However, adaptive controllers are not necessarily capable of rejecting all harmonic components on grids with high harmonic distortion. Therefore, this work presents a robust model reference adaptive control-based with an adaptive super-twisting sliding mode, which is improved with a harmonic compensation strategy in which additional control actions to deal with the harmonic contents of the <inline-formula><tex-math notation="LaTeX">$5{\text{th}}$</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">$7{\text{th}}$</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">$11{\text{th}}$</tex-math></inline-formula>, and <inline-formula><tex-math notation="LaTeX">$13{\text{th}}$</tex-math></inline-formula> of the fundamental component of the electrical grid are incorporated into the control structure. Experimental results are provided of a 7.5-kW converter connected to a distorted and unbalanced grid with an <italic>LCL</italic> filter, where the developed control structure presents a total harmonic distortion (THD) of 2%, while the controller without harmonic compensation obtains a 2.5% THD.
Differential Response of Mycosis Fungoides Cells to Vorinostat

Mycosis fungoides (MF) is the most common form of cutaneous T-cell lymphoma (CTCL) and is characterized by epidermotrophism of malignant CD4+ T-lymphocytes. When MF advances to a recurrent stage, patients require treatment with systemic therapies such as vorinostat, a histone deacetylase inhibitor. While vorinostat has been shown to exhibit anti-tumor activity in MF, its exact molecular mechanism has yet to be fully discerned. In the present study, we examined the transcriptomic and proteomic profiles of vorinostat treatment in two MF cell lines, Myla 2059 and HH. We find that vorinostat downregulates CTLA-4, CXCR4, and CCR7 in both cell lines, but its effect on several key pathways differs between the two MF cell lines. For example, vorinostat upregulates CCL5, CCR5, and CXCL10 expression in Myla cells but downregulates CCL5 and CXCL10 expression in HH cells. Furthermore, vorinostat upregulates IFN-γ and IL-23 signaling and downregulates IL-6, IL-7, and IL-15 signaling in Myla cells but does not affect these pathways in HH cells. Although Myla and HH represent established MF cell lines, their distinct tumor origin from separate patients demonstrates that inherent phenotypic variations within the disease persist, underscoring the importance of using a variety of MF cells in the preclinical development of MF therapeutics.
Discovering Homogeneous Groups from Geo-Tagged Videos

The popularity of intelligent devices with GPS and digital compasses has generated plentiful videos and images with text tags, timestamps, and geo-references. These digital footprints of travelers record their time and spatial movements and have become indispensable information resources, vital in applications such as how groups of videographers behave and in future-movement prediction. In this paper, first we propose algorithms to discover homogeneous groups from geo-tagged videos with view directions. Second, we extend the density clustering algorithm to support fields-of-view (FoVs) in the geo-tagged videos and propose an optimization model based on a two-level grid-based index. We show the efficiency and effectiveness of the proposed homogeneous-pattern-discovery approach through experimental evaluation on real and synthetic datasets.
Probability Estimation over Large-Scale Random Networks via the Fiedler Delta Statistic

Statistical models for networks have been typically committed to strong prior assumptions concerning the form of the modeled distributions. Moreover, the vast majority of currently available models are explicitly designed for capturing some specific graph properties (such as power-law degree distributions), which makes them unsuitable for application to domains where the behavior of the target quantities is not known a priori. The key contribution of this paper is twofold. First, we introduce the Fiedler delta statistic, based on the Laplacian spectrum of graphs, which allows to dispense with any parametric assumption concerning the modeled network properties. Second, we use the defined statistic to develop the Fiedler random field model, which allows for efficient estimation of edge distributions over large-scale random networks. After analyzing the dependence structure involved in Fiedler random fields, we estimate them over several real-world networks, showing that they achieve a much higher modeling accuracy than other well-known statistical approaches.
Rapid Identification of Carbapenemase-Producing Klebsiella pneumoniae Using Headspace Solid-Phase Microextraction Combined with Gas Chromatography-Mass Spectrometry

Background Carbapenemase-producing Klebsiella pneumoniae is an unprecedented threat to public health, and its detection remains challenging. Analysis of microbial volatile organic compounds (VOCs) may offer a rapid way to determine bacterial antibiotic susceptibility. Purpose The aim of this study was to explore the VOCs released by carbapenemase-producing carbapenem-resistant Klebsiella pneumoniae (CRKP) using headspace solid-phase microextraction/gas chromatography-mass spectrometry (HS-SPME/GC-MS). Methods Test bacteria were incubated in trypticase soy broth to the end of exponential growth phase, and imipenem was added in the middle time. Headspace VOCs were concentrated and analyzed using HS-SPME/GC-MS. Results The compound 3-methyl-1-butanol was found to be a biomarker among the 26 bacterial isolates (10 KPC-positive, 10 NDM-positive, 2 IMP-positive, 2 carbapenemase-negative CRKP, and 2 carbapenem-susceptible K. pneumonoiae). Conclusion This study explored a promising new strategy for the screening of carbapenemase-producing CRKP strains. Further research with larger sample sizes will potentially accelerate the application of biomarkers in routine microbiology.
A 6-DoF Position Sensor for Bearingless Slice Motors

This article describes a position sensor with six degrees of freedom (DoF) measurement capability. This sensor is designed for the position sensing of the rotor in a bearingless slice motor to enable active control. The sensor is designed to fit entirely under the rotor and operates by accessing the rotor bottom surface only, enabling packaging of the pump on the top of the rotor. The sensor has two parts; both operate using eddy currents. One of these parts measures the two radial DoF of the rotor. The other part measures the axial, angular rotation and tip/tilt DoF. The sensor utilizes a conductive target fixed to the underside of the rotor. Motion of this target varies the magnetic fields, which can then be measured by the sensor as variation in either induced voltage or inductance value. The design and fabrication of the sensor along with the signal processing methods are presented in detail. The radial position measurements are the most critical for active levitation due to the small working gaps in these DoF, hence a resolution of $<1.2\ \mu \rm {m}$ and bandwidth of 1 kHz is achieved. This article also describes the closed-loop behavior of a bearingless motor using this sensor.
A Revised Prognostic Cloud Fraction Scheme in a Global Forecasting System

AbstractIn this study, a revised prognostic cloud fraction scheme for atmospheric models is proposed and its performance is evaluated with a diagnostic cloud fraction scheme. A revision is proposed through a direct linkage between hydrometeors in the cumulus parameterization scheme and the amount of predicted cloud fractions. Cloud fractions that are determined via the prognostic cloud fraction scheme appear to be more realistic than those determined via a diagnostic cloud fraction scheme when both are compared with satellite data. In a medium-range forecast test bed, the biases of large-scale features such as temperature, geopotential height, and mean sea level pressure are moderately reduced when the prognostic cloud fraction scheme is used.
Development of a New Elliptic Curve Cryptosystem with Factoring Problem

Problem statement: The security of elliptic curve cryptosystems are based on elliptic curve discrete logarithm problem (ECDLP). However, if an attacker finds a solution to ECDLP, the elliptic curve-based systems will no longer be secure. Approach: To improve this, we develop a new elliptic curve cryptosystem using one of the old/novel problem in computational number theory; factoring problem (FAC). Specifically, our encrypting and decrypting equations will heavily depends on two public keys and two secret keys respectively. Results: We show that, the newly designed cryptosystem is heuristically secure against various algebraic attacks. The complexity of the scheme shows that the time complexity for each encryption and decryption are given by 299Tmul and 270Tmul. Conclusion: The new system provides greater security than that system based on a single hard problem. The attacker has not enough resources to solve the two hard problems simultaneously in a polynomial time.
S01. Force Balance in Saturn's Warped Magnetodisc

In the Cassini era, the north magnetic axis of Saturn’s planetary dipole field has been oriented at angles of > 65◦ with respect to the upstream solar wind flow. This deviation from perfect orthogonality is responsible for the formation of a ‘bowl-shaped’ current sheet in the outer magnetosphere [3]. In order to investigate the force balance in such a structure, we have started by using the formalism of [5] to construct planar disc structures in which centrifugal, pressure and magnetic (J × B) forces are in equilibrium. We extend this planar model to produce bowl-shaped geometries through the representation of the magnetopause ‘shielding field’ with an external dipole, displaced from the planet along the axis of azimuthal symmetry of the modelled system. The location and strength of the external dipole relative to the planetary source determines a curl-free field with north-south asymmetry which, when added to the planetary and disc fields, produces a distortion of the planar disc, ‘sweeping’ it back from the equatorial plane. We examine the effect on the sheet geometry of the external dipole parameters, and make preliminary comparisons with Cassini magnetometer data.
Projection Mapping Method Using Projector-LiDAR (light detection and ranging) Calibration

The projector-LiDAR system performs projection mapping on a long-distance object after calibrating the projector using LiDAR. With LiDAR equipped with an RGB camera, users can calibrate the projector in just two scans. Because this system knows the ray formula for every pixel shooting from the projector, it can make accurate projections. In particular, the system introduces how to perform linear interpolation in the process of obtaining a formula and a methodology that allows users to immediately know the 3D coordinates of a desired part in a 2D image. Through a long-distance experiment, the projection mapping result of the proposed method showed an error of less than 5mm, and the existing system had a maximum error of 30mm.
GAM: General Auxetic Metamaterial with Tunable 3D Auxetic Behavior Using the Same Unit Cell Boundary Connectivity

Research on auxetic metamaterials is important due to their high performance against impact loadings and their usefulness in actuators, among other applications. These metamaterials offer a negative Poisson’s ratio at the macro level. However, usual auxetic metamaterials face challenges in (1) grading the effect, (2) coupling and combining auxetic metamaterials with non-auxetic materials due to boundary compatibility, (3) obtaining the same auxetic behavior in all directions in the transverse plane, and (4) adapting the regular geometry to the component design boundary and shape. The goal of this paper is to present a novel, recently patented tunable 3D metamaterial created to reproduce a wide spectrum of 3D auxetic and non-auxetic Poisson’s ratios and Young’s moduli. This wide range is obtained using the same basic unit cell geometry and boundary connections with neighboring cells, facilitating designs using functionally graded metamaterials as only the connectivity and position of the cell’s internal nodes are modified. Based on simple spatial triangularization, the metamaterial is easily scalable and better accommodates spatial curvatures or boundaries by changing the locations of nodes and lengths of bars.
Utilizing ANN for Predicting the Cauchy Stress and Lateral Stretch of Random Elastomeric Foams under Uniaxial Loading

As a result of their cell structures, elastomeric foams exhibit high compressibility and are frequently used as buffer cushions in energy absorption. Foam pads between two surfaces typically withstand uniaxial loads. In this paper, we considered the effects of porosity and cell size on the mechanical behavior of random elastomeric foams, and proposed a constitutive model based on an artificial neural network (ANN). Uniform cell size distribution was used to represent monodisperse foam. The constitutive relationship between Cauchy stress and the four input variables of axial stretch λU, lateral stretch λL, porosity φ, and cell size θ was given by con-ANN. The mechanical responses of 500 different foam structures (20% < φ < 60%, 0.1 mm < θ < 0.5 mm) under compression and tension loads (0.4 < λU < 3) were simulated, and a dataset containing 100,000 samples was constructed. We also introduced a pre-ANN to predict lateral stretch to address the issue of missing lateral strain data in practical applications. By combining physical experience, we chose appropriate input forms and activation functions to improve ANN’s extrapolation capability. The results showed that pre-ANN and con-ANN could provide reasonable predictions for λU outside the dataset. We can obtain accurate lateral stretch and axial stress predictions from two ANNs. The porosity affects the stress and λL, while the cell size only affects the stress during foam compression.
Electronic Passport as the Basis of the Digital Twin

During the process of decision support system development there is a problem associated with the heterogeneity of the data in terms of relevance, quality and completeness of the research objects description. The solution to this problem is digital twins based on data lake technology implementation. The aim of the work is to create a prototype of an electronic passport (e-passport) of materials as the basis of their digital twin. The digital twin is to contain a set of data on the composition, structure, calculated and experimentally measured properties, participation in the composition of chemical reactions, constructed models and "structure-property" patterns as well. It is proposed to combine the information on a specific material or compound into an information structure — electronic passports of the research object. At the same time, it is advisable to consider the electronic passport as a comprehensive description of the object, including information about the static and dynamic parameters of the atomic-molecular system of the research object. The e-passport as an information object gives a systematic data on the composition of a substance, structure, calculated and experimentally measured properties, the participation of elements in the composition of chemical reactions, constructed models and "structure-property" patterns, thus acting as a generalizing tool convenient for users to present information available in the database of digital twins of materials.
Stretchy: Enhancing Object Sensation Through Multisensory Feedback and Muscle Input

Current works on 3D interaction methods mainly focus on rigid object manipulation and selection, while very few have been done on elastic object interaction. Therefore, we suggest a novel interaction method to observe and manipulate virtual fabric in a VR environ-ment. We use multi-sensory pseudo-haptic feedback (a combination of tactile and visual feedback) and muscle strength data (EMG) to perceive the stiffness of the virtual fabric and flexible objects. For demonstration, we make fabric patches with various stiffness, and the stiffness difference can be distinguished. Our system can be implemented in a virtual cloth store to give consumers information about product stiffness and texture.
Approach to Identifying Areas of Uncontrolled Oscillations in Human-Machine Systems

In the human-machine system, the human operator, acting as a regulator, controls various devices through servomechanisms. The system parameters and the parameters of the human operator model, to one degree or another, can change due to numerous factors. It can lead to a deviation of the output signal from the specified one or loss of stability, which manifests as oscillatory processes. Usually, drive nonlinearities exert a vast load on the system's stable operation, which requires a thorough analysis of the system and taking this fact into account when designing the control system. In this work, conditions are found under which system performance deteriorates. It manifests itself as a significant increase in performance error or the appearance of unwanted oscillation. A simple method was proposed to prevent them by introducing a nonlinear corrective device into the drive control loop. Also, an analysis of the system on the parameter plane of the forcing signal was proposed by calculating the generalized sensitivity functions.
MAGO: Maliciously Secure Subgraph Counting on Decentralized Social Graphs

Subgraph counting aims to count over a large graph subgraphs matching a given shape (e.g., triangle), which plays an important role in various social graph analytics applications such as discovering social roles and characterizing social networks. Subgraph counting over social graphs, however, becomes quite challenging when the social graph to be analyzed is presented in a decentralized manner, where each user only holds a local view. Collecting the local views for subgraph counting-based analytics raises acute privacy concerns, because they capture the sensitive social relationships among individuals. In light of this, we design, implement, and evaluate MAGO, a new system aimed at maliciously secure subgraph counting on decentralized social graphs. MAGO is built from a delicate synergy of insights on graph analytics, lightweight cryptography, and local differential privacy, allowing individual users to securely contribute their local views on a decentralized social graph for a cloud-empowered subgraph counting service. In addition to the protection for data confidentiality, MAGO is also designed to protect against a malicious adversary that might try to tamper with the subgraph counting results. Extensive experiments over real-world social graph datasets demonstrate the practically affordable performance of MAGO for maliciously secure subgraph counting.