Miracle-dz/UniFAR-FTUs huggingface dataset:
A technique for finding the second simple shortest paths using associative parallel processors

This paper proposes a technique to build the data structure for finding the second simple shortest paths on a model of associative parallel processors (the STAR-machine). It includes three associative parallel algorithms represented on the STAR-machine as the corresponding procedures. We prove the correctness of these procedures and evaluate their time complexity. Among these algorithms, the new version of the Dijkstra algorithm and the associative parallel algorithm for finding the matrix of tree paths can be used to solve other graph problems on the STAR-machine.
Issues and Trends in Expatriation Management

As organisations become globalised, there is an increasing need to employ expatriates for global assignments. Investigate the issues in the expatriation process from different perspectives are of utmost important to enhance understanding and improve performance of the companies in global world. This study had used a qualitative approach with the purpose to investigate the opinions from four Malaysian Human Resource Managerial Personnel regarding the issues in expatriate management in the Multinational Companies’ subsidiaries. The findings revealed that some of the issues and trends that companies encounter include the high costs of expatriation; expatriation is a necessary alternative; non-returning of expatriates to home country; managing expectations; difficulty in retaining global careerists; difficulty to achieve work-life balance; the utilisation of the home trip allocation to travel to other countries; increasing diversification of expatriates, increasing women and intra-Asia transfer; and local employees need to adjust themselves every time there is changes of expatriates in company. It is hoped that the findings of this study could be used as major references for any organizations to improve their expatriation practices by considering the related issues pertaining to expatriation management.
Modular White-Box Model of single-phase Photovoltaic Systems for Harmonic Studies

This paper presents a modular model for single-phase photovoltaic (PV) systems for low-voltage studies. The main aim of the model is to assess the harmonic behavior of these systems. Besides steady state analysis, the model allows also dynamic studies, e.g. of harmonic instabilities. In order to identify and assess possible reasons for such instabilities on the inverter side, an easy interchangeability of the model components is provided based on a modular concept. The modular model can be applied to simulate various PV-system implementations in time-domain simulations. An impedance-based assessment based on the fingerprint method is used to assess the different behavior of the PV-systems regarding their topology and the choice of parameters. A comparison between different Phase Locked Loops (PLLs) and different designs of the grid-side filter is drawn to illustrate the different behavior of the PV-system in a steady state operating point. The model is implemented in MATLAB/Simulink and the Simscape Power Systems library.
Process Induced Wafer Warpage Optimization for Multi-chip Integration on Wafer Level Molded Wafer

In this paper, the demonstration of test vehicle by two kinds of process flows noted as "C4 first" and "C4 last", which integrate chips on mold-based, Cu via wafer with glass carriers, are presented. Their warpage behavior during wafer-form integration will be experimentally and numerically evaluated, and also compared with wafer warpages of 2.5D assembly which applied Si interposer with TSV (through Si via). The C4-first flow is to attaching chips on wafers where the C4 bumps have been completed between mold layer and glass carrier. This flow is similar to 2.5D manufacturing process that the Si interposer was temporarily bonded on a carrier which can suppress the interposer warpage variation during reflow process. The temporary glue is required to protect the C4 bumps during chip on wafer procedures. In regarding to the cycle time and cost, the flow to complete C4 after chips attaching will be further studied. The processed induced u-bump (micro-bump) and u-pad (micro-pad) shift post jointing are observed to be larger than that in 2.5D flow. In the manufacturing process of molded wafer, the high CTE glass carriers are used to reduce CTE mismatch between molding compound and carrier. The significant wafer warpage changing from concave to convex shape was observed after chips attaching on the wafer. And the warpage will be further increased after underfilling and molding processes. To well predict and address the warpage trend, the finite element analyses are carried out to understand the process-induced warpage behaviors, and thus to select better material and process parameter. The key parameters affecting wafer warpages like material properties of molding compound, glass carrier and top-mold thickness are determined by finite element simulation. In light of the handing procedure of wafer form assembling equipment, the preferred wafer warpage shape is determined by experimental results. Finally, the test vehicle have been assembled with substrate as well. The chip module warpage in 2.5D structure is about double than that with molding interposer at 230°C and reversed direction in a convex shape, which is different from molding chip module.
Detection and Characterization of Domestic Heat Pumps

Smart meters allow utilities to gain access to tremendous amount of metering data, from which they can improve their knowledge on customers. In this paper, we propose a method to detect and characterize domestic heat pumps from household power consumption data. Appliance detection is not a trivial task, owing to the variability in heat pump load dynamics and to the distortion of their power consumption signature by other appliances. Compared to State-of-the-art methodologies that relies on energy disaggregation with supervised learning and high-resolution data (e.g., 10 seconds), our novel approach uses lower resolution data. Also, it is semi-supervised and relies on a Bayesian framework allowing to continuously learn as new data becomes available. To do so, the overall power consumption signals are decomposed and approximated into a dictionary of boxcar functions using sparse signal approximation to isolate heat pumps activity. The learning phase consists then to generate distributions summarizing power consumption, operation time and frequency of heat pumps activation events summarized as boxcar functions after approximation. During the test phase, the distributions are used as prior to calculate the likelihood that a boxcar function is generated by a heat pump. Using standard classification performance measure and an application to data from the EcoGrid EU project, the methodology reaches high performance in heat pump detection.
THE ROLE OF Y1R-EXPRESSING DORSAL HORN INTERNEURONS IN PAIN

The spinal Neuropeptide Y system is a potential target for development of new pain therapeutics. Lumbar intrathecal injection of NPY is antinociceptive, reducing hyper-reflexia to thermal and mechanical stimulation, particularly after nerve injury and inflammation. We have previously shown that intrathecal injection of Neuropeptide Y-sap (NPY-sap) is also antinociceptive, reducing nocifensive reflex responses to noxious heat and formalin. In the present study, we sought to determine the role of dorsal horn Y1R-expressing neurons in pain by destroying them with NPY-sap and testing the rats on three operant tasks. Lumbar intrathecal NPY-sap 1reduced CFA-induced hyper-reflexia on the 10°C cold plate, 2reduced cold aversion on the thermal preference and escape tasks, 3was analgesic to noxious heat on the escape task, 4reduced the CFAinduced allodynia to cold temperatures experienced on the thermal preference, feeding interference, and escape tasks, and 5did not inhibit or interfere with morphine analgesia. Introduction The superficial dorsal horn of the spinal cord is the first key area of nociceptive gating and modulation within the CNS (Melzack and Wall, 1965), and
A 1.04 - 4V, Digital-Intensive Dual-Mode BLE 5.0/IEEE 802.15.4 Transceiver SoC with extended range in 28nm CMOS

The proposed fully-integrated digital-intensive TRX SoC allows dual-mode protocols of BLE 5.0 and IEEE 802.14. 5 with extended-range wireless connectivity and simple ad-hoc mesh networks for the IoTs in smart homes. A 1.04 - 4V with dual-mode supply schemes enables the SoC to be applied for various IoT systems energy-effectively and extends battery lifetime cost-effectively. The TRX employs a low insertion loss CMOS transmit- receive switch, a class-D power amplifier with HD2 calibration, a low-IF RX architecture with ΔΣ ADC-based complex filter, and a low power ADPLL with LMS-based two-point direct frequency modulation. The TRX implemented in a 28nm CMOS process achieves the maximum output power of +10dBm while consuming 45mW at the TX, <-102dBm at IEEE802.15. 4 mode while consuming 6mW at the RX.
On the Solvability of Steady-State Load Flow Problems for Multi-Carrier Energy Systems

The coupling of single-carrier network into multicarrier energy systems (MES) has recently become more important. Conventional single-carrier steady-state load flow models are not able to capture the full extent of the coupling. Different models for multi-carrier networks have been proposed, either based on the energy hub concept or using a case specific approach. However, the effect of the coupling on solvability and well-posedness of the integrated system of non-linear equations has not been discussed. Using a general load flow model on a small example MES, this paper discusses the problems arising due the coupling of single-carrier networks, and provides guidelines to obtain a solvable steady-state load flow model for MES.
Reliability of Glued Joints on Flexible Substrates During Accelerated Current Ageing

This paper deals with reliability of joints glued by non-conductive adhesives (NCA) on flexible substrates during electrical current ageing. Also the comparison of NCA joints ageing with ECA joints ageing is presented in this paper. Two types of adhesives were used for the experiment-non-conductive UV curable acrylic adhesive Loctite AA3926 (NCA) and electrically conductive two part epoxy adhesive MG 8331S (ECA). The purpose of the experiment was to verify the reliability of NCA joints during the electrical current accelerated ageing. The electrical resistance of UV NCA joints on flexible DuPont Pyralux substrate before, during and after electrical current load was better than resistance of ECA joints and UV NCA can be recommended.
THE PROTON INJECTOR FOR THE EUROPEAN SPALLATION SOURCE

The update of the design of the PS-ESS source and of its LEBT has been carried out in 2013 and the construction is now ongoing. The Ion Source will be able to provide a proton beam current larger than 74 mA to the 3.6 MeV RFQ. Several innovative solutions have been implemented in the redesign phase in order to cope with high-reliability/high-performance requirements of the ESS project. A flexible magnetic system will allow to investigate alternative configurations for future beam current upgrade of the machine, based on the formation of a denser plasma. New set-ups have been also explored for beam extraction, transport and chopping. Calculations have shown that space charge compensation up to 95 % is needed to preserve the low emittance in the low energy beam transfer line (LEBT). In order to obtain the optimal proton beam pulse rise and fall time we propose a LEBT chopping configuration that permits hundred nanosecond rise and fall times despite the LEBT compensation needs few microseconds. Some hints about numerical modelling efforts at LNS will be also given.
Measurement and FEM analysis of DC/GIC effects on transformer magnetization parameters

Topologically derived equivalent circuit models for transformers can be improved by more accurate parameterization of the magnetization characteristics. We examined the changes in transformer magnetization parameters by measuring the effects of dc on a scaled down model single- phase four-limb (1p4L) transformer and confirmed them with FEM modelling. The results define the non-linear character of the transformer inductance when partial saturation occurs, such as in over-excited transformers or with half-wave saturation caused by leakage dc or geomagnetically induced currents. The analysis clarifies the difference between the constant air inductance of a winding $(\mathrm {L}_{\mathrm {a}\mathrm {i}\mathrm {r}})$ and the instantaneously changing saturation inductance $\mathrm {L}_{\mathrm {terminal}}$ used in low-frequency transformer equivalent circuit models and shows how to choose a representative value for the equivalent circuit parameter. The tests lead to a more accurate graphical depiction of related parameters during transformer half-cycle saturation than usually presented.
Algorithm of Moving Object Detection of Surveillance Video Combined with WiFi Technology

In this paper, the algorithm of moving object detection of surveillance video combined with WiFi technology is proposed to solve the problems of moving object detection in complex environment. The algorithm uses WiFi signals to locate people in the camera field of view (FOV). Then the statistical results are compared with the number of moving objects detected by the camera at the same time. Finally, the detection results and background complexity are predicted based on the comparison results. By adjusting the relevant parameters of the moving object detection by feedback mechanism, the adaptive ability of the detection algorithm is further enhanced in the complex background, and the detection robustness of the algorithm is improved. In order to evaluate the performance of this algorithm, WiFi technology is combined with PBAS and SOBS moving object detection algorithms in complex background. The experimental results show that our algorithm can adjust the relevant parameters quickly according to the complexity of the environment, has good environmental adaptability and it can improve the accuracy of moving object detection.
Fully-Filled, Highly-Reliable Fine-Pitch Interposers with TSV Aspect Ratio >10 for Future 3D-LSI/IC Packaging

Si interposer with 10 μm-width, 100 μm-deep through-silicon via (TSV) has been fabricated using electroless (EL) Ni as barrier and seed layers, and characterized for their electrical resistance. The chemistry of electroless-Ni plating bath was meticulously adjusted for the conformal formation of Ni along the TSV side wall. From the resistance value of 36 ΩW per TSV obtained from the Kelvin measurement of these Cu-TSV chain showed that the electroless Ni layer well acts as a good seed layer for completely filling the high aspect ratio TSVs by Cu-electroplating.
3D Silicon Photonics Interposer for Tb/s Optical Interconnects in Data Centers with Double-Side Assembled Active Components and Integrated Optical and Electrical Through Silicon Via on SOI

In this paper, we present the concept, fabrication, process and packaging of a 3D Si photonics interposer. This Si photonics interposer merges passive photonic and electronic functionalities within a single chip. The interposer is populated with active optical and electronic add-ons, which are flip-chip bonded to the interposer using thermo-compression bonding. The interposer itself is then flip-chip bonded to a glass and Si carrier for further testing purposes. This integration concept enables a high connection density (Gb/s/mm²) by assembling 40Gb/s per channel opto-electrical components on both sides of the interposer. Communication between components on both sides of the interposer is enabled by optical and electrical TSVs with a 3dB bandwidth >28GHz. A single mode photonic layer, designed for 1. 55µm wavelength is integrated within the interposer to be used for routing and switching of the optical signals. Main fabrication and packaging steps are described here, together with some demonstrator evaluation results.
Expression for Conductor Resistance in the Frequency Range 2-150 kHz

This article discusses the accuracy of the method recommended by the IEC 60287-1-1 for including skin and proximity effect in the estimation of the ac resistance of two parallel conductors at the supraharmonic range (2-150 kHz). The IEC resistance model is compared against a generalized model in order to visualize and quantify the error incurred when using the standard’s simplified formula for typical low-voltage conductors carrying supraharmonic distortion. The study reveals that the cable resistance model used impacts the resonance peak attenuation, leading the IEC formula to show errors as significant as 14%.
Regional and correlative sweat analysis using high-throughput microfluidic sensing patches toward decoding sweat

Sweat is used to examine fluid and electrolyte loss, ion correlations, and glucose dynamics in healthy and diabetic cohorts. Recent technological advancements in wearable sensors have made it easier to detect sweat components, but our limited understanding of sweat restricts its application. A critical bottleneck for temporal and regional sweat analysis is achieving uniform, high-throughput fabrication of sweat sensor components, including microfluidic chip and sensing electrodes. To overcome this challenge, we introduce microfluidic sensing patches mass fabricated via roll-to-roll (R2R) processes. The patch allows sweat capture within a spiral microfluidic for real-time measurement of sweat parameters including [Na+], [K+], [glucose], and sweat rate in exercise and chemically induced sweat. The patch is demonstrated for investigating regional sweat composition, predicting whole-body fluid/electrolyte loss during exercise, uncovering relationships between sweat metrics, and tracking glucose dynamics to explore sweat-to-blood correlations in healthy and diabetic individuals. By enabling a comprehensive sweat analysis, the presented device is a crucial tool for advancing sweat testing beyond the research stage for point-of-care medical and athletic applications.
Sentinel 1 Radar Data Use to Recognize Thawed/Frozen Soil State in Some Russian Arctic Regions in 2017–2018

This paper deals with the identification of thawed/frozen soils in the topsoil layer for three stations in Russia: Salekhard, Tiksi and Norilsk by using Sentinel 1B C-band radar data for the period of 2017–2018. The determining of the frozen/thawed soil state is carried out in three ways: 1) by multi-temporal radar data on the basis of a significant in 3–5 dB the backscatter coefficient difference in the transition to freezing/thawing soil state, 2) by finding the threshold value of the backscatter coefficient at which the temperature in the topsoil layer falls below 00C, 3) by texture features.
Defect Detection for the TSV Transmission Channel Using Machine Learning Approach

Through silicon via (TSV) is a key enabler for 3D integration technology to provide high-density and high-speed transmission channels. However, the serious defects including open circuit and short circuit will result in functional failure. In this paper, a method to identify the open and short defect and detect the defect position is developed based on machine learning approach. Taking S parameters as training dataset, the predictive model is trained and evaluated, the performance of models based on bagging method and boosting method are compared. The results demonstrate the predictive model could correctly classify the defect type and predict the defect position accurately.
OptimalControl of Investments forQuality of Supply Improvement inElectricalEnergyDistributionNetworks

This paper considers the problem of deciding multi-period investments for maintenance and upgrade of electrical energy distribution networks. After describing the network as a constrained hybrid dynamical system, optimal control theory is applied to optimize profit under a complex incentive/penalty mechanism imposed by public authorities. The dynamics of the system and the cost function are translated into a mixed integer optimization model, whose solution gives the optimal investment policy over the multi-period horizon. While for a reduced-size test problem the pure mixed-integer approach provides the best optimal control policy, for real-life large scale scenarios a heuristic solution is also introduced. Finally, the uncertainty associated with the dynamical model of the network is taken care of by adopting ideas from stochastic programming.
Characterization of Fine Pitch Hybrid Bonding Pads using Electrical Misalignment Test Vehicle

Cu/oxide Hybrid Bonding (HB) technology is currently the ultimate fine pitch 3D interconnect solution to reach submicron pitches. It's an attractive technique to address the needs of several applications such as smart imagers, high-performance computing and memory-on-logic folding. But test and characterization of such fine-grained 3D interconnect is still an open issue; Cu-Cu interconnects are prone to many structural defects due to fabrication process, such as misalignment, which needs to be thoroughly tested to ensure the performance of 3D-ICs. In this work, we focus on testing and characterizing, on-wafer, misalignment defect induced at the bonding step. A misalignment test structure was fabricated in a Wafer-to-Wafer (W2W) assembly configuration with a pitch of 3. 42µm and 1. 44µm using a very small measurement step for an accurate misalignment measurement (respectively 45nm and 22nm). Electrical tests have been performed using five multi-pitch wafers with 71 measurements points per wafer. The experimental results show that the results of the proposed test structure are aligned with conventional overlay measurements. Finally, the impact of misalignment defect on resistance and capacitance parameters was demonstrated.
Real-Time Circuit Breaker State Detection and Identification Method Based on Faster R-CNN*

Deep Learning is the latest research achievement in the field of Artificial Intelligence. To detect and identify the state of circuit breaker in ambient air automatic monitor station, Deep Learning was proposed to realize the circuit breaker state recognition in real-time. The Deep Convolutional Neural Network was used to construct the state detection and identification model of the circuit breaker and provided the detection process. The remote monitoring system of ambient air automatic monitor station and inspection data were used to build the experimental data platform, with the circuit breaker state recognition data was acquired from the multiple ambient air automatic monitor stations. Through training the model with the Deep Learning, the experimental results showed that the circuit breaker state detection and identification method have a simple recognition process and high accuracy. The real-time Detection and Identification function of the circuit breaker state was realized, meanwhile, the assistant decision-making function was provided to the ambient air automatic monitor station.