Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimental characteristics of a printed open‐slot antenna with reflector for PCS, DCS, and IMT‐2000

In this paper, we investigate a new rectangular‐fed printed open‐slot antenna (POSA) with reflector, which has the advantages of improved radiation‐pattern properties along with broad‐bandwidth characteristics. The measured bandwidth of the proposed POSA with reflector is approximately 47.6% (−10 dB ≥ S11), thus, it can be used for PCS, DCS, and IMT‐2000. But the measured bandwidth of the PCSA with reflector is approximately 46% (with VSWR ≤ 2.0). The tilt angle at maximum gain is enhanced by using a modified POSA with reflector for practical applications. The radiation pattern null and distortion of the open‐slot antenna exhibited better performances than those of the closed‐slot antenna. The back‐lobe level of the POSA with reflector also presented much better results than the PCSA with reflector. The proposed antenna obtained not only wider bandwidth but also better radiation patterns characteristics than the PCSA with reflector. © 2004 Wiley Periodicals, Inc. Microwave Opt Technol Lett 41: 348–350, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.20138
Fuzzy-based analysis of process capability for assembly quality assessment in mechanical assemblies

Process capability indices are useful tools for evaluating the ability of a process to produce products that meet certain specifications. The assembly quality is dependent on the distribution of variations of assembly dimensions, which is in turn dependent on mating conditions in the mechanical assembly. Since it is often difficult to measure the assembly dimensions in the production stages, they are not considered as a direct inspection objective. Rather, the inspection and evaluation of quality is carried out by specifying whether the assembly requirements satisfy the specified limits. Therefore, we can base the process capability indices on the assembly dimensions. In most real life cases, the observations are fuzzy. In this paper, a novel method based on fuzzy concepts for process capability analysis of assembly dimensions in mechanical assemblies is presented. According to this scheme, sample observations of manufactured variables are described as fuzzy numbers. The proposed method is able to estimate the ability of the manufacturing process in satisfying the assembly quality in the mechanical assemblies with asymmetric tolerances which have non-normal distributions. In this paper, a proper criterion based on the probability of fuzzy set to interpret the computed fuzzy results is proposed, so these results are converted to the interpretable results for making a decision to evaluate the assembly quality. Furthermore, a new fuzzy-based quantity factor for expressing the percent contributions of effective manufacturing variables on the assembly quality is presented. The application of the presented method is demonstrated through an example and its results are discussed.
SBO-RNN: Reformulating Recurrent Neural Networks via Stochastic Bilevel Optimization

In this paper we consider the training stability of recurrent neural networks (RNNs), and propose a family of RNNs, namely SBO-RNN, that can be formulated using stochastic bilevel optimization (SBO). With the help of stochastic gradient descent (SGD), we manage to convert the SBO problem into an RNN where the feedforward and backpropagation solve the lower and upper-level optimization for learning hidden states and their hyperparameters, respectively. We prove that under mild conditions there is no vanishing or exploding gradient in training SBO-RNN. Empirically we demonstrate our approach with superior performance on several benchmark datasets, with fewer parameters, less training data, and much faster convergence. Code is available at https://zhang-vislab.github. io .
Impacto de la aplicación de la Ley 13059 de Eficiencia Energética en relación a la nueva ordenanza de usos del suelo de la ciudad de La Plata y la Norma IRAM 11900 de Etiquetado de Edificios

El crecimiento urbano introduce permanente presion a las areas metropolitanas. En paises perifericos la demanda de energia crece a mayor velocidad que el crecimiento poblacional y la oferta, creando crisis y escasez. El objetivo principal de este trabajo es prever el impacto que tendra la Ley 13059 de Eficiencia Energetica en relacion a la nueva Ordenanza de Usos del Suelo de la ciudad de La Plata y la Norma de Etiquetado de Edificios (IRAM 11900) . Esta norma establece una metodologia simplificada para el calculo del nivel de eficiencia energetica de las envolventes de los edificios susceptibles de ser calefaccionados. Todavia, es evidente que existe una importante falta de adecuacion entre los objetivos teoricos formulados para el desarrollo de la ciudad y las conflictivas estrategias tecnicas propuestas para satisfacerlos. En cuanto a la aplicacion de la ley de eficiencia energetica al modelo de edificio de construccion usual en La Plata, no verifica sus valores de transmitancia termica K. Tampoco verifica el riesgo de condensacion ni el coeficiente global de perdidas G. Del mismo modo, cuando se aplica el protocolo de etiquetado de eficiencia energetica que plantea la norma IRAM 11900, el edifico clasifica como H. Se puede ver que el impacto de ahorro potencial en la demanda de energia anual para calefaccion, si se comienza a implementar la Ley de Eficiencia Energetica seria de un 46 %.
Research on the Developing Stragies for Producer Services Agglomeration of Cities

The agglomeration development of producer services is a vital driving force for China to realize the optimization of industrial structure and the transformation of agglomeration growth mode. In order to promote the agglomeration development of producer services in my country, this article puts forward some policy recommendations, including strengthening the rational agglomeration of producer services, optimizing the external environment for the producer services agglomeration, choosing the appropriate development direction of producer services, giving full play to the spillover effect of spatial agglomeration of producer services, promoting the interactive development of producer services and manufacturing industry.
The asymptotic distribution of the isotonic regression estimator over a general countable pre-ordered set

We study the isotonic regression estimator over a general countable pre-ordered set. We obtain the limiting distribution of the estimator and study its properties. It is proved that, under some general assumptions, the limiting distribution of the isotonized estimator is given by the concatenation of the separate isotonic regressions of the certain subvectors of an unrestrecred estimator’s asymptotic distribution. Also, we show that the isotonization preserves the rate of convergence of the underlying estimator. We apply these results to the problems of estimation of a bimonotone regression function and estimation of a bimonotone probability mass function. MSC 2010 subject classifications: 62F30, 62G20, 62G08.
LoRa Networking Techniques for Large-scale and Long-term IoT: A Down-to-top Survey

Low-Power Wide-Area Networks (LPWANs) are an emerging Internet-of-Things (IoT) paradigm, which caters to large-scale and long-term sensory data collection demand. Among the commercialized LPWAN technologies, LoRa (Long Range) attracts much interest from academia and industry due to its open-source physical (PHY) layer and standardized networking stack. In the flourishing LoRa community, many observations and countermeasures have been proposed to understand and improve the performance of LoRa networking in practice. From the perspective of the LoRa networking stack; however, we lack a whole picture to comprehensively understand what has been done or not and reveal what the future trends are. This survey proposes a taxonomy of a two-dimensional (i.e., networking layers, performance metrics) to categorize and compare the cutting-edge LoRa networking techniques. One dimension is the layered structure of the LoRa networking stack. From down to the top, we have the PHY layer, Link layer, Media-access Control (MAC) layer, and Application (App) layer. In each layer, we focus on the three most representative layer-specific research issues for fine-grained categorizing. The other dimension is LoRa networking performance metrics, including range, throughput, energy, and security. We compare different techniques in terms of these metrics and further overview the open issues and challenges, followed by our observed future trends. According to our proposed taxonomy, we aim at clarifying several ways to achieve a more effective LoRa networking stack and find more LoRa applicable scenarios, leading to a brand-new step toward a large-scale and long-term IoT.
Methods for Estimating Consistency Intervals, and the Detection of Changes on Mobile Radio Channels

For simulations, mobile radio channels are often assumed to be random and wide sense stationary, with unchanging stochastic parameters. Measurements, however, show that mobile channel characteristics change rapidly. This paper reports the detection of changes in measured radio propagation data by comparing cumulative distributions for angle of arrival and excess delay in consecutive time intervals. Results include time series showing correspondence between channel parameters and identified points of change, application of the results in the estimation of rms delay spreads, and the proposal of a method by which a model can be developed for controlling changes in channel parameters during simulations
EMBEDDED DEEP LEARNING IN IOT

— The proliferation of IoT devices heralds the emergence of intelligent embedded ecosystems that can collectively learn and that interact with humans in a human-like fashion. Recent advances in deep learning revolutionized related fields, such as vision and speech recognition, but the existing techniques remain far from efficient for resource-constrained embedded systems. This dissertation pioneers a broad research agenda on Deep Learning for IoT. By bridging state-of-the-art IoT and deep learning concepts, I hope to enable a future sensor-rich world that is smarter, more dependable, and friendlier, drawing on foundations borrowed from areas as diverse as sensing, embedded systems, machine learning, data mining, and real-time computing. Collectively, this dissertation addresses five research questions related to architecture, performance, predictability and implementation. First, are current deep neural networks fundamentally well-suited for learning from time-series data collected from physical processes, characteristic to IoT applications? If not, what architectural solutions and foundational building blocks are needed? Second, how to reduce the resource consumption of deep learning models such that they can be efficiently deployed on IoT devices or edge servers? Third, how to minimize the human cost of employing deep learning (namely, the cost of data labeling in IoT applications)? Fourth, how to predict uncertainty in deep learning outputs? Finally, how to design deep learning services that meet responsiveness and quality needed for IoT systems? This dissertation elaborates on these core problems and their emerging solutions to help lay a foundation for building IoT systems enriched with effective, efficient
Retrieval of UVB aerosol extinction profiles from the ground-based Langley Mobile Ozone Lidar (LMOL) system

Abstract. Aerosols emitted from wildfires are becoming one of the main sources of poor air quality in the US mainland. Their extinction in UVB (wavelength range 280–315 nm) is difficult to be retrieved using simple lidar techniques because of the impact of O3 absorption and lacking information of lidar ratio at those wavelengths. The 2018 Long Island Sound Tropospheric Ozone Study (LISTOS) campaign in the New York City region allowed the characterization of lidar ratio for UVB aerosol retrieval. An algorithm for the aerosol extinction retrieval out of the Langley Mobile Ozone Lidar (LMOL) was used in conjunction with the NASA Langley High Altitude Lidar Observatory (HALO) 532 nm aerosol extinction product. This approach requires assuming 2 parameters, the lidar ratio at 292 nm and the Ångström Exponent (AE) between 532 nm and 292 nm. The objective of this work is to determine these two parameters and assess the retrieval error caused by improper assumption of lidar ratio. This work also accomplishes the first know 292 nm aerosol product inter-comparison between HALO and Tropospheric Ozone Lidar Network (TOLNet) ozone lidar. HALO results were compared with the aerosol data retrieved from the 292 nm band from LMOL with different approximations of the lidar ratio and the AE to determine optimal parameters. Using optimized parameters, the LMOL aerosol extinction can be retrieved with a 10 % accuracy up to 3 km. This work highlights the importance of the lidar ratio and AE in the retrieval and validation of 292 nm aerosol profiles obtained from UV-lidar. Errors arise from approaches that utilize a random priori lidar ratio and AE assumption. The lidar ratios at 292 nm determined in this work will also improve our understanding of the UVB optical properties of aerosol in the lower troposphere affected by transported wildfire emission.
On categorial combination of logics

We propose in this paper a generalization of fibring of propositional logic systems within the framework of category theory. Specifically, we generalize the categorial construction of fibring by using arbitrary colimits and limits. We prove that limits and colimits preserve completeness (under reasonable conditions) in the category of propositional Hilbert calculi endowed with general algebraic semantics, generalizing a well-known result in the literature of combining logics.
Supplement of A systematic re-evaluation of methods for quantification of bulk particle-phase organic nitrates using real-time aerosol mass spectrometry

A Systematic Re-evaluation of Methods for Quantification of Bulk Particle-phase Organic Nitrates Using Real-time Aerosol Mass Spectrometry 5 Douglas A. Day, 1,2 Pedro Campuzano-Jost, 1,2 Benjamin A. Nault, 1,2,a Brett B. Palm, 1,2,b Weiwei Hu, 1,2,c Hongyu Guo, 1,2 Paul J. Wooldridge, 3 Ronald C. Cohen, 3,4 Kenneth. S. Docherty, 5 J. Alex Huffman, 6 Suzane S. de Sá, 7 Scot T. Martin, 7,8 Jose L. Jimenez 1,2
A Survey and an Outlook of Requirements, Challenges and Technologies for Development and Deployment of the 5G Networks

: The huge and rapid increase in the number of mobile devices, massive volume of data, and higher data rate are stimulating to rethink thecurrent generation of the cellular mobile communication. The next generation (5G) cellular networks are expectedto meet top-level requirements. Four unique features broadly characterize the 5G networks: ubiquitous connectivity,extremely low latency, network energy efficiency and very high-speed data transfer. The 5G networks would provide novel architectures and technologiesbeyond state-of-the-art architectures and technologies. In this paper, theobjective is to investigate and discuss critical limitations of the fourth generation (4G) cellular networks andcorresponding new features of 5G networks. We identify challenges in 5G networks, new technologies for 5G networks, andpresent a comparative study of the proposed architectures that can be classified on the basis of energy-efficiency, networkhierarchy, and network types. Interestingly, the implementation issues, e.g., interference, QoS, load balancing, densification, handoff, cloud base communication, security-privacy,channel access and energy harvestingare hugely effect the realization of 5G networks.
SGPA: Structure-Guided Prior Adaptation for Category-Level 6D Object Pose Estimation

Category-level 6D object pose estimation aims to predict the position and orientation for unseen objects, which plays a pillar role in many scenarios such as robotics and augmented reality. The significant intra-class variation is the bottleneck challenge in this task yet remains unsolved so far. In this paper, we take advantage of category prior to overcome this problem by innovating a structure-guided prior adaptation scheme to accurately estimate 6D pose for individual objects. Different from existing prior based methods, given one object and its corresponding category prior, we propose to leverage their structure similarity to dynamically adapt the prior to the observed object. The prior adaptation intrinsically associates the adopted prior with different objects, from which we can accurately reconstruct the 3D canonical model of the specific object for pose estimation. To further enhance the structure characteristic of objects, we extract low-rank structure points from the dense object point cloud, therefore more efficiently incorporating sparse structural information during prior adaptation. Extensive experiments on CAMERA25 and REAL275 benchmarks demonstrate significant performance improvement. Project homepage: https://www.cse. cuhk.edu. hk/˜kaichen/projects/sgpa/sgpa.html.
Identification of capacitor position in a radial system

As modern equipment is more sensitive to power quality phenomena, transients introduced by capacitor switching become an increasing concern. The paper addresses the identification of switched capacitor position such that on-line or post-fault measures can be taken. The position is given in terms of proportional line impedance seen from the load bus. Perturbation methods are used to find an asymptotic expression for capacitor position as a function of the transient frequency. This paper presents analytical and simulation results for a standard radial distribution system.
Deep AI Enabled Ubiquitous Wireless Sensing

With the development of the Internet of Things (IoT), many kinds of wireless signals (e.g., Wi-Fi, LoRa, RFID) are filling our living and working spaces nowadays. Beyond communication, wireless signals can sense the status of surrounding objects, known as wireless sensing, with their reflection, scattering, and refraction while propagating in space. In the last decade, many sophisticated wireless sensing techniques and systems were widely studied for various applications (e.g., gesture recognition, localization, and object imaging). Recently, deep Artificial Intelligence (AI), also known as Deep Learning (DL), has shown great success in computer vision. And some works have initially proved that deep AI can benefit wireless sensing as well, leading to a brand-new step toward ubiquitous sensing. In this survey, we focus on the evolution of wireless sensing enhanced by deep AI techniques. We first present a general workflow of Wireless Sensing Systems (WSSs) which consists of signal pre-processing, high-level feature, and sensing model formulation. For each module, existing deep AI-based techniques are summarized, further compared with traditional approaches. Then, we provide a view of issues and challenges induced by combining deep AI and wireless sensing together. Finally, we discuss the future trends of deep AI to enable ubiquitous wireless sensing.
The probable pattern of weaning among children of Arar, Saudi Arabia

Background: The weaning period is a crucial time in an infant’s life. We aimed to study the pattern of the children’s weaning, age, causes, child's problems of weaning, weaning food, sources of information about weaning, and the process of weaning itself among children of Arar, Saudi Arabia. Methodology: This was a cross-sectional study conducted among 452 mothers during the period from September 2017 to March 2018. Mothers were randomly selected from the attendees of the female side of six selected primary health care centers in the city using a systematic random sampling technique (every fourth mother). Results: The majority of children (34.7%) were weaned at 13–24 months, (34.3%) were weaned because they completed 24 months. Crying and the bad mood were the most common problems (33.4%) in case of refusal of weaning. Home food was the most preferred food for the children (96.9%), and information about weaning was gained through different sources, mainly relatives and parents (62.4%). Our study reported that 67.3% stopped breastfeeding just without any reason. Conclusion: In our study, the highest proportion was weaned at 13–24 months. There was a significant misunderstanding about the meaning of weaning in Arar, Northern Saudi Arabia. A program promoting exclusive breastfeeding in the first 6 months must be conducted to increase the mother’s awareness of exclusive breastfeeding.
2021-04216 - GAN-based 3D Manipulation of Car Models

The Inria Sophia Antipolis Méditerranée center counts 34 research teams as well as 7 support departments. The center's staff (about 500 people including 320 Inria employees) is made up of scientists of different nationalities (250 foreigners of 50 nationalities), engineers, technicians and administrative staff. 1/3 of the staff are civil servants, the others are contractual agents. The majority of the center’s research teams are located in Sophia Antipolis and Nice in the Alpes-Maritimes. Four teams are based in Montpellier and two teams are hosted in Bologna in Italy and Athens. The Center is a founding member of Université Côte d'Azur and partner of the I-site MUSE supported by the University of Montpellier.
Design and realization of intelligent tourism service system based on voice interaction

Voice technology is one of the important contents to improve the intelligence and humanization of tourism service system. Combining voice technology, the paper concentrates on application needs and the composition of system to present an overall intelligent tourism service system's framework consisting of presentation layer, Web services layer, and tourism application service layer. On the basis, the paper further elaborated the implementation of the system and its key technologies, including intelligent voice interactive technology, seamless integration technology of multiple data sources, location-perception-based guides' services technology, and tourism safety control technology. Finally, according to the situation of Nanjing tourism, a prototype of Tourism Services System is realized.
A Statistical Analysis of Elastomeric Seal Degradation in a Pneumatic Cylinder With Variation of Temperature

Pneumatic Cylinders are one of major mechanical components which are widely used in various industries such as automobile production line and semiconductor inspection device due to its low contamination and low maintenance cost. Most of existing studies on pneumatic cylinders have focused on their performance improvement such as operating sliding friction measurement, contact stress minimization, appropriate dynamic response, and etc. This research carried out life tests of pneumatic cylinders and the concerned failure mechanism was the degradation of pneumatic polyurethane seal since leakage caused by seal degradation occurs very frequently. The considered operating parameter was environmental temperature. To assess operating temperature as an acceleration parameter, the lifetime of a pneumatic cylinder was tested at high temperature and the degradation mechanism and characteristics of sealing surface were analyzed by statistical Weibull analysis method as well as by surface analysis using pyrolysis gas chromatography. Ultimately this research will help the life prediction of other pneumatic reciprocating machines.Copyright © 2011 by ASME