Miracle-dz/UniFAR-FTUs huggingface dataset:
Contribution of NOMA Signalling to Practical Multibeam Satellite Deployments

This work explores the contribution of Non-Orthogonal Multiple Access (NOMA) signalling to improve some relevant metrics of a multibeam satellite downlink. Users are paired to exploit Signal-to-Noise Ratio (SNR) imbalances coming from the coexistence of different types of terminals, and they can be flexibly allocated to the beams, thus relaxing the cell boundaries of the satellite footprint. Different practical considerations are accommodated, such as a spatially non-uniform traffic demand, non-linear amplification effects, and the use of the DVB-S2X air interface. Results show how higher traffic volumes can be channelized by the satellite, thanks to the additional bit rates which are generated for the strong users under the superposition of signals, with carefully designed power levels for DVB-S2X modulation and coding schemes in the presence of non-linear impairments.
Physical interpretation of the Riemann hypothesis

An equivalent formulation of the Riemann hypothesis is given. The physical interpretation of the Riemann hypothesis equivalent formulation is given in the framework of quantum theory terminology. One more power series related to the Riemann Xi function and the Riemann hypothesis is considered. Some roots of the polynomial connected with the power series are studied. It is shown that the Riemann hypothesis is true. But it is undecidable and must be considered as an axiom.
e-Health Assisted Smart Supermarket

Worldwide improvement is technology can be noticed at a faster pace in every piece of application and developments. At the same time, the exploitation of technologies in certain sector is very stumpy. Usually people buy various things from the shops without knowing the ingredients present in that. Where some of the ingredients may not be good for the individuals based on their health conditions. The intake of such items creates health issues in various ways and feel later for consuming such items. So far there is no pre-indication system to indicate the purchaser about the health issues for the items purchased from the shop. In this paper, proposed a smart trolley that assists the customer about the health issues related of a product he/she picked to purchase with the support of a mobile app in the smart phone.
A Robust and Efficient GQD based biosensor for wearable applications

Researchers present a magnetized fluorescence biosensor with a "on" switch that is built on molybdenum disulfide (MoS2) nanosheet, graphene quantum dots (GQDs), as well as iron oxide nanosheet. This will be used for the prompt isolation of circulation tumorcells (CTCs) in a manner that is both effective and sensitive. This is discussed here how an easy procedure known as electrochemical synthesis may be used to generate photo luminescent GQDs that are modified including an aptamer (epithelial cell adhesion molecule (EpCAM) sensors) as well as a magnetized compound enabling one-step bioimaging and enriching of CTCs. In order to build "turn-on" biosensing magnetized fluorescent nanocomposite’s, MoS2 nanosheet, which act as a fluorescence quencher, are combined with both the aptamer that was stated before, which was combined using Iron Oxide and GQD compound (MFNs). Because of the one-step CTC solvent extraction, this scheme has minimal cytotoxicity and an average capture effectiveness of 92 percent, which would be greater than those of other magnetic nanoparticles. This is made possible by the fact that perhaps the technology only requires one step. Furthermore particular, the MFNs were capable of rapidly identifying and labeling CTCs within 15 minutes, which was much faster than conventional one-step and two-step marker detection approaches. In addition to this, since aptamers are present, the MFNs have had the potential to target and specifically collect CTCs (both low- and high-EpCAM-expressing cells). In fact, the identification of up to 10 cancer cells in the whole blood was accomplished with a high level of sensitivity. As a result, there seems to be a significant possibility that the MFNs could be utilized as global biosensing nanocomposite’s again for purpose of fluorescence-guided cancer cell enriching and bioimaging.
Cymo: A Storage Model with Query-Aware Indexing for Spatio-Temporal Big Data

Spatio-temporal data are generated continuously and becomes gigantic with the rapid growth of mobile devices and applications. How to store and index spatio-temporal big data is crucial to support efficient queries for effective data management and analytics. With their high write throughput and good scalability, distributed NoSQL data stores such as HBase are widely adopted as storage engines in spatio-temporal big data systems, in which a key-value-based storage model is required so one or a group of spatio-temporal data points can be converted into one key-value pair. However, existing techniques cannot govern an inherent dilemma caused by adding time dimension within spatial information, that is, we can either optimize for space-preferred or time-preferred queries but not both.In this paper, we propose a novel storage model with query-aware indexing, called Cymo, that can adapt different query patterns. Our basic idea is to divide the spatio-temporal space into multiple subspaces so each subspace can effectively exploit its query characteristics to obtain the best storage model. Specifically, Cymo includes a learning model that can predict query patterns for each subspace based on historical workloads. Also, in Cymo, a virtual layer is proposed to hide the heterogeneity of different storage models with a unified query interface, by which different storage models can be hidden and transparent from applications while different query patterns can be effectively exploited in query optimization. We have implemented Cymo based on HBase and further integrated it into GeoMesa, a representative spatio-temporal big data system. Experimental results based on real taxi datasets show that Cymo improves query latencies significantly (1.53× to 10×) compared with GeoMesa. We have released the open source code of Cymo for public access.
Learning and Generalizing Cooperative Manipulation Skills Using Parametric Dynamic Movement Primitives

This paper presents an approach that generates the overall trajectory of mobile manipulators for a complex mission consisting of several sub-tasks. Parametric dynamic movement primitives (PDMPs) can quickly generalize the online motion of robot manipulation by learning multiple demonstrations in offline. However, regarding complex missions consisting of multiple sub-tasks, a large number of demonstrations are required for full generalization, which is impractical. In this paper, we propose a framework that reduces the number of demonstrations for a complex mission. In the proposed method, complex demonstrations are segmented into multiple unit motions representing sub-tasks, and one PDMP is formed per each segment, resulting in multiple PDMPs. The phase decision process determines which sub-task and associated PDMPs to be executed online, allowing multiple PDMPs to be autonomously configured within an integrated framework. In order to generalize the execution time and regional goal in each phase, the Gaussian process regression (GPR) is applied. Simulation results from two different scenarios confirm that the proposed framework not only effectively reduces the number of demonstrations but also improves generalization performance. The actual experiments also demonstrate that the mobile manipulators effectively perform complex missions through the proposed framework. Note to Practitioners—This paper presents an approach of learning from demonstration (LfD) to generalize complex movements of robots. Parametric dynamic movement primitives (PDMPs) compute styles of movements from multiple demonstrations. However, the complexity of the PDMP increases as the mission involves more sub-tasks. In this paper, we resolve this issue by segmenting the complex mission into multiple sub-tasks and configuring multiple PDMPs. This work effectively reduces the number of required demonstrations for PDMPs, moderates the complexity of the algorithm. Also, the proposed approach allows flexible sub-task sequencing. It enables the mission in an unlearned sequence or a new combination of sub-tasks. The proposed approach is validated in both simulation and experimental results. Our approach is applicable for complex missions whose sub-tasks are clearly identified
Design of Modular High-Speed Copper Coated Solid Rotor Induction Machine

This paper presents the concept of a modular high-speed solid rotor induction machine. The utilization of this type of machine is mainly for the verification of the solid rotor manufacturing technology, where it is necessary to verify whether the rotor can withstand centrifugal forces at nominal speed. Due to the very simple construction of the machine, enclosed in a frame that can be easily assembled and disassembled, the rotor can be replaced very easily. Therefore, it is not necessary to create a prototype for another machine but to verify the technology for the production of the solid rotor. In this case, copper coated solid rotor technology is used. The concept and dimensions of the machine are also presented here, including the calculated electromagnetic parameters and rotor dynamics of the machine, using numerical methods. The advantage of utilizing the machine is in the reduction of the cost of producing different machines.
Categorization and Grading of Spices Using Deep Learning

Detection of spices from images and recognition based on image processing is a popular research topic. Food is the most vital aspect of human life. Every food consumed on a daily basis contains a variety of spices that make it delicious and flavorful. People are especially concerned about their health. The spices used in meals play a vital role in the prevention of dietary, obesity, and other such issues. The aim of this project is to create an automatic system for detecting and recognising Indian spices on different images, so that dieticians may properly analyze nutrition and other types of health dangers. Color and texture data were primarily used in the recognition method for a better categorization outcome. This approach has been evaluated on a variety of spices. The different spices were classified using a Convolution Neural Network (CNN). The proposed system involves the CNN model for categorization. The spices dataset is generated by collecting images from the internet and creating more images for training by using data augmentation for 4 categories. The spices images contain 640 as training data and another 128 images taken separately for testing data. For obtaining an optimum model with increased classification accuracy different combinations of number of hidden layers and epochs are analyzed. The overall network performance losses for various cases are also observed. Experimental results produced the best test accuracy of 91.14% and the best training accuracy of 97.19%.
Microfabrication and Characterisation of an Electrostatic MEMS Energy Harvester for Biomedical Applications

We present the fabrication process and characterization of a novel Silicon-on-Glass electrostatic MEMS vibration energy harvester. This device is aimed to give supplementary energy to the battery of leadless pacemakers by using the vibrations generated by heartbeat movement. The proposed device is compliant with Leadless Pacemakers requirements: its dimensions are compatible with the 6mm- diameter capsules of leadless pacemakers for catheterism, and the materials composing the device are not ferromagnetic, allowing MRI imaging of implanted patients.
A Methodology to Design Hairpin Winding for Improved Thermal Performances in a Permanent Magnet Assisted Synchronous Reluctance Motor

This paper discusses the benefits of using hairpin winding on the thermal behaviour of a permanent magnet assisted synchronous reluctance motor. With respect to a reference motor with round-wire winding, a new design using hairpin winding was proposed in order to match the same performance specifications. Despite having additional AC copper losses, the new hairpin design has shown better thermal performances increasing therefore the continuous torque. A parametric analysis has also shown that the hairpin motor can still achieve the required performances with a reduced active length improving torque and power density.
Charm and bottom quark masses: An update

Using new four-loop results for the heavy quark vacuum polarization and new data for bottom quark production in electron-positron annihilation, an update on the determination of charm- and bottom-quark masses through sum rules has been performed. The previous result for the charm-quark mass, ${m}_{c}(3\text{ }\text{ }\mathrm{GeV})=0.986(13)\text{ }\text{ }\mathrm{GeV}$, based on the lowest moment, is supported by the new results from higher moments which lead to consistent values with comparable errors. The new value for the bottom quark, ${m}_{b}(10\text{ }\text{ }\mathrm{GeV})=3.610(16)\text{ }\text{ }\mathrm{GeV}$, corresponding to ${m}_{b}({m}_{b})=4.163(16)\text{ }\text{ }\mathrm{GeV}$, makes use of both the new data and the new perturbative results and is consistent with the earlier determination.
Utilizing Ear Biometrics for Individual Identifications Using HOG and LBP

The demand for more secure authentication has increased on several occasions. Exploiting biometrics in various forms, such as face, voice, handwriting, and gait recognition, is a reliable method for authentication. Recently, the analysis of ear images as a biometric method has become a robust identification method. This paper aims to utilize two different techniques of feature extraction: histograms of oriented gradients and local binary patterns to extract the desired features. Whereas principal component analysis is used to reduce the space of the feature dimensionality. For classification, linear discriminant analysis is used. The proposed technique is applied to the images of the (IIT Delhi-I) database. The proposed method has yielded good achievements compared to other studies.
Wound Field Synchronous Drive Cycle Current Parameters Optimization: A Metamodel-Based Approach

With the growth of the electric and hybrid vehicle market, the optimization of electric machines is inevitable. These machines are optimally designed to respect a drive cycle composed of a considerable number of operating points. By considering saturation phenomena and all different losses in the studied machine the optimization process then becomes very time-consuming. To overcome this limitation, very fast electromagnetic models should be considered. To this end, a Gaussian Process (GP) metamodel is proposed and used to predict the electromagnetic performances and losses of a wound field synchronous machine, which is a permanent magnet-free machine solution for electric mobility. Optimal currents for armature and excitation windings are computed over the entire drive cycle using loss minimization strategy.
Three-dimensional molecular line transfer: A simulated star-forming region

We present the first non-local thermodynamic equilibrium (non-LTE), comoving frame molecular line calculations of a star-forming cluster simulated using smoothed particle hydrodynamics (SPH), from which we derive high-resolution synthetic observations. We have resampled a particle representation on to an adaptive mesh and self-consistently solved the equations of statistical equilibrium in the comoving frame, using TORUS, a three-dimensional adaptive mesh refined radiative transfer code. We verified the applicability of the code to the conditions of the SPH simulation by testing its output against other codes. We find that the level populations obtained for optically thick and thin scenarios closely match the ensemble average of the other codes. We have used the code to obtain non-LTE level populations of multiple molecular species throughout the cluster and have created three-dimensional velocity-resolved spatial maps of the emergent intensity. Line profiles of cores traced by N 2 H + (1-0) are compared to probes of low-density gas, 13 CO (1-0) and C 18 O (1-0), surrounding the cores along the line of sight. The relative differences of the line centre velocities are shown to be small compared to the velocity dispersion, matching recent observations. We conclude that one cannot reject competitive accretion as a viable theory of star formation based on observed velocity profiles.
Aramid Pulp Reinforced Clay Aerogel Composites: Mechanical, Thermal and Combustion Behavior

In this work, we reported that aramid pulps (AP) reinforced clay aerogel composites with improved mechanical strength, good thermal insulation and fire resistance based on the combination of AP, Poly(vinyl alcohol) (PVA) and sodium montmorillonite (MMT), which present a promising prospect in the thermal insulation application. The PVA-MMT-APx (x: denotes the mass content of AP) aerogel composites present an isotropic “lamella-honeycomb” porous structure, which endows them with excellent comprehensive performance. With the AP content increasing, the extremely low density is kept, ranging between 67–73 mg/cm3, and the low thermal conductivity is maintained within 40.9–47.9 mW·m−1·K−1. The mechanical strength is significantly improved with the maximum compressive modulus increasing from 2.95 to 5.96 MPa and the specific modulus rising from 44.03 to 81.64 MPa∙cm3/g. Their detailed heat transfer process has been analyzed, which provides a deep understanding to the low thermal conductivity of the PVA-MMT-APx aerogel composites. Based on the combination of thermogravimetric analysis and combustion behavior, the PVA-MMT-APx aerogel composites are demonstrated to possess improved thermal stability and fire resistance. This study puts forward a facile approach to utilizing AP to reinforce clay aerogel composites, which provides new insight into the development of thermal-insulating, fire-safe and high-strength thermal insulation materials.
Interest Rate Sensitivity of Savings Accounts 1

Interest rate risk measurement and management of non-maturity deposit balances presents a challenge for practitioners and academic researchers as well. The paper provides a review of several methodological approaches focusing on the area of savings accounts rate sensitivity modeling and estimation. The proposed interest rate sensitivity models are tested on a Czech banking sector dataset providing mixed results regarding the cointegration type models generally reco-mmended in the literature. On the other hand, the analysis shows that simpler regression models may provide more robust results if the cointegration tests between the saving accounts rate and the market rate series fail. According to the empirical results, the sensitivity of the domestic savings rates is slightly higher for companies compared to rates for individuals, but in both cases well below 50%.
MANAGEMENT OF CLASS II MALOCCLUSION WITH TWIN FORCE BITE CORRECTOR: A CASE SERIES

Class II malocclusion is the most common sagittal malocclusion after class I and involves mandibular retrognathism. The treatment modalities include both removable functional and fixed functional appliances but fixed functional can be preferred as they have certain advantages. The present case series discusses a hybrid type of fixed functional appliance, i.e., Twin Force Bite Corrector appliance in three cases: A 15-year-old female patient with Class II skeletal due to mandibular retrognathism, a14-year-old male patient with Sagittal Class II malocclusion due to mandibular retrognathism, and a 14-year-old female patient with Sagittal Class II malocclusion due to mandibular retrognathism. The effects produced by this appliance are like the conventional fixed functional appliances, but it has a distinct advantage of quick chair-side fabrication and simple design.
REBL: Entity Linking at Scale

REBL is an extension of the Radboud Entity Linker (REL) for Batch Entity Linking. REBL is developed after encountering unforeseen issues when trying to link the large MS MARCO v2 web document collection with REL. In this paper we discuss the issues we ran into and our solutions to mitigate them. REBL makes it easier to isolate the GPU heavy operations from the CPU heavy operations, by separating the mention detection stage from the candidate selection and entity disambiguation stages. By improving the entity disambiguation module we were able to lower the time needed for linking documents by an order of magnitude. The code for REBL is publicly available on GitHub. modest This paper describes our experience with optimizing the Radboud Entity Linking (REL) toolkit for batch processing large corpora. REL annotates individual documents efficiently, requiring only modest computational resources, while performing competitively when compared to the state-of-the-art methods on effectiveness. It considers entity linking as a modular problem consisting of three stages:
MRI changes of adjacent segments after transforaminal lumbar interbody fusion (TLIF) and foraminal endoscopy: A case–control study

Background: Intervertebral foramen endoscopy has developed rapidly, but compared with transforaminal lumbar interbody fusion (TLIF), the progress of degeneration is unknown. We aim to compare the changes of intervertebral disc and intervertebral foramen in adjacent segments after TLIF and endoscopic discectomy for patients with lumbar disc herniation (LDH). Methods: From 2014 to 2017, 87 patients who were diagnosed with single-level LDH and received surgery of TLIF (group T, n = 43) or endoscopic discectomy (group F, n = 44) were retrospectively analyzed. X-ray, MRI, CT and clinical symptoms were recorded before operation and at the last follow-up (FU). The neurological function was originally evaluated by the Japanese Orthopaedic Association (JOA) scores. Radiological evaluation included the height of intervertebral space (HIS), intervertebral foramen height (FH), intervertebral foramen area (FA), lumbar lordosis (CA) and intervertebral disc degeneration Pfirrmann scores. Results: There was no significant difference in baseline characteristics, JOA improvement rate, reoperation rate and complications between the two groups. The age, average blood loss, average hospital stays and average operation time in group F were lower than those in group T. During the last FU, HIS, CA and FA decreased in both groups, and the changes in group T were more significant than those in group F (P < .05). There was no significant difference in FH changes between the two groups (P > .05). Conclusion: Both TLIF and endoscopic surgery can achieve good results in the treatment of LDH, but the risk of lumbar disc height loss and intervertebral foramina reduction in the adjacent segment after endoscopic surgery is lower.
On the geometry and the moduli space of beta-deformed quiver gauge theories

We consider a class of super-conformal β-deformed = 1 gauge theories dual to string theory on AdS5 × X with fluxes, where X is a deformed Sasaki-Einstein manifold. The supergravity backgrounds are explicit examples of Generalised Calabi-Yau manifolds: the cone over X admits an integrable generalised complex structure in terms of which the BPS sector of the gauge theory can be described. The moduli spaces of the deformed toric = 1 gauge theories are studied on a number of examples and are in agreement with the moduli spaces of D3 and D5 static and dual giant probes.