Miracle-dz/UniFAR-FTUs huggingface dataset:
Engineering cyber‐physical systems through performance‐based modelling and analysis: A case study experience report

The process of engineering cyber‐physical systems (CPS) is inevitably challenging because of the intrinsic problem of merging the specification of different ensembles that indicate hardware, software, and physical aspects of such systems. This intrinsic complexity is exacerbated when modelling and analysing the performance characteristics of CPS since multiple models need to coexist in order to get meaningful performance indicators. In this paper, we present a case study, a delivery robots system, whose experience is exploited towards building a guided process for engineering CPS through performance‐based modelling and analysis. Model‐based performance results are provided while analysing different design alternatives, thus to support architects in the process of better understanding the performance characteristics of CPS under development.
A Bilateral Constrained Image Reconstruction Method Using Electrical Impedance Tomography and Ultrasonic Measurement

Electrical Impedance Tomography (EIT) reconstructs the conductivity distribution in a sensing area through an array of boundary electrodes and has received increasing attention in bio-impedance measurement. To provide accurate image reconstruction of irregular conductivity distribution in large sensing area, we propose an efficient and accurate bilateral constrained dual modality reconstruction algorithm using EIT and ultrasonic measurement, where the EIT pre-reconstruction helps to select the time of flight (TOF) time window and the ultrasound reflection information of boundary points contributes to joint iterative reconstruction. In the simulations and phantom experiments selecting abdomen tumor as the object of interest, the proposed method successfully reconstructed the small inclusions. Quantitatively, the proposed bilateral constrained method has improved the reconstruction performance in terms of boundary preservation and robustness compared with single modality EIT and ultrasonic algorithms.
Using Gaussian Mixture Models for Gesture Recognition During Haptically Guided Telemanipulation

Haptic guidance is a promising method for assisting an operator in solving robotic remote operation tasks. It can be implemented through different methods, such as virtual fixtures, where a predefined trajectory is used to generate guidance forces, or interactive guidance, where sensor measurements are used to assist the operator in real-time. During the last years, the use of learning from demonstration (LfD) has been proposed to perform interactive guidance based on simple tasks that are usually composed of a single stage. However, it would be desirable to improve this approach to solve complex tasks composed of several stages or gestures. This paper extends the LfD approach for object telemanipulation where the task to be solved is divided into a set of gestures that need to be detected. Thus, each gesture is previously trained and encoded within a Gaussian mixture model using LfD, and stored in a gesture library. During telemanipulation, depending on the sensory information, the gesture that is being carried out is recognized using the same LfD trained model for haptic guidance. The method was experimentally verified in a teleoperated peg-in-hole insertion task. A KUKA LWR4+ lightweight robot was remotely controlled with a Sigma.7 haptic device with LfD-based shared control. Finally, a comparison was carried out to evaluate the performance of Gaussian mixture models with a well-established gesture recognition method, continuous hidden Markov models, for the same task. Results show that the Gaussian mixture models (GMM)-based method slightly improves the success rate, with lower training and recognition processing times.
Concrete crack detection using context‐aware deep semantic segmentation network

Computer‐vision and deep‐learning techniques are being increasingly applied to inspect, monitor, and assess infrastructure conditions including detection of cracks. Traditional vision‐based methods to detect cracks lack accuracy and generalization to work on complicated infrastructural conditions. This paper presents a novel context‐aware deep convolutional semantic segmentation network to effectively detect cracks in structural infrastructure under various conditions. The proposed method applies a pixel‐wise deep semantic segmentation network to segment the cracks on images with arbitrary sizes without retraining the prediction network. Meanwhile, a context‐aware fusion algorithm that leverages local cross‐state and cross‐space constraints is proposed to fuse the predictions of image patches. This method is evaluated on three datasets: CrackForest Dataset (CFD) and Tomorrows Road Infrastructure Monitoring, Management Dataset (TRIMMD) and a Customized Field Test Dataset (CFTD) and achieves Boundary F1 (BF) score of 0.8234, 0.8252, and 0.7937 under 2‐pixel error tolerance margin in CFD, TRIMMD, and CFTD, respectively. The proposed method advances the state‐of‐the‐art performance of BF score by approximately 2.71% in CFD, 1.47% in TRIMMD, and 4.14% in CFTD. Moreover, the averaged processing time of the proposed system is 0.7 s on a typical desktop with Intel® Quad‐Core™ i7‐7700 CPU@3.6 GHz Processor, 16GB RAM and NVIDIA GeForce GTX 1060 6GB GPU for an image of size 256 × 256 pixels.
Instructional design models for blended learning in engineering education

The purpose of this study is to develop instructional methods and processes for designing authentic contexts of blendedlearning in engineering education. Design strategies for the reproduction of professionals’ authentic contexts are suggestedwith guidelines for the actual development of a blended learning course. According to the development researchmethodology, along with a prototype e-learning course, six design strategies were developed as follows: (1) select andidentify authentic tasks that practitioners or experts can solve; (2) analyze the context of solving the authentic task;(3)model experts’ cognitive and behavioral processes of solving the authentic task; (4) develop assessment tools for theauthentic task; (5) apply instructional strategies to provide authentic contexts by using technologies; and (6) developinstructional resources and environments. Two task analysis methods, activity theory and PARI (Precursor–Action–Results–Interpretation), were employed to identify authentic troubleshooting problems of energy auditing. Constructivistlearning models and strategies were implemented; they had been adopted from situated learning, anchored instruction,cognitive apprenticeship, and goal-based scenarios. The research implications and limitations are discussed for general-ization in future studies.
A Reliable Gravity Compensation Control Strategy for dVRK Robotic Arms With Nonlinear Disturbance Forces

External disturbance forces caused by nonlinear springy electrical cables in the master tool manipulator (MTM) of the da Vinci Research Kit (dVRK) limits the usage of the existing gravity compensation methods. Significant motion drifts at the MTM tip are often observed when the MTM is located far from its identification trajectory, preventing the usage of these methods for the entire workspace reliably. In this letter, we propose a general and systematic framework to address the problems of the gravity compensation for the MTM of the dVRK. Particularly, high-order polynomial models were used to capture the highly nonlinear disturbance forces and integrated with the multi-step least square estimation framework. This method allows us to identify the parameters of both the gravitational and disturbance forces for each link sequentially, preventing residual error passing among the links of the MTM with uneven mass distribution. A corresponding gravity compensation controller was developed to compensate the gravitational and disturbance forces. The method was validated with extensive experiments in the majority of the manipulator's workspace, showing significant performance enhancements over existing methods. Finally, a deliverable software package in MATLAB and C++ was integrated with dVRK and published in the dVRK community for open-source research and development.
A Review of Tools and Techniques for Data-Enabled Formative Assessment

The purpose of this literature review is to understand the current state of research on tools that collect data for the purpose of formative assessment. We were interested in identifying the types of data collected by these tools, how these data were processed, and how the processed data were presented to the instructor or student for the purpose of formative assessment. We identified two categories of data: machine-graded and activity stream data. The data were processed using three methods: unprocessed activity streams, descriptive data analysis, and data mining. Processed data were presented to students through reports and real-time feedback and to instructors through reports and visual dashboards.
Performance of Faba Bean Plants under different Irrigation Regimes and Foliar Application of Certain Growth Regulators in Toshka Area, Egypt

In order to water deficit has become the major limiting factor on the productivity and sustainability of agriculture, especially in arid and semi-arid regions. It could be achieved through two field experiment were carried out during 2016-17 and 2017-18 seasons at the agricultural experimental station farm of Water Studies & Research Complex (WSRC), National Water Research Center, located in Toshka at Abu-Simbel Region, to investigate out the impact of irrigation water regimes (Ir1=60, Ir2=80 and Ir3=100 % of ETc, which were applied using a sprinkler irrigation system) with the foliar applications of indole acetic acid (IAA) application (In1=0, In2=50 and In3=75 ppm) and kinetin (Kn1=0, Kn2=20 and Kn3=40 ppm) on growth, seed yield and quality of faba bean plants (Nubaria 1) as well as the water crop productivity (WCP). The experiment was laid out in strip-split plot design with 3 replications.The obtained results could be summarized as follows: Increasing the applied amount of irrigation water from 1458 (Ir1) to 2430 m 3 fed (Ir3) gave the highest values of all growth characters, except plant height in average values of both seasons. Nevertheless, the application of 100 % ETc (Ir3) resulted in increases in the values of yield and its attributes as well as P and K contents of seeds. The highest values of (WUE), (WCP) and seed quality (protein content) occurred with 60 % ETc (Ir1) of irrigation water level. The growth traits, yield and its attributes, as well as chemical composition of faba bean values and water parameters increased with increasing IAA and kinetin concentrations to 75 and 40 ppm, respectively. With respect to the interaction of the three studied factors, Ir3 (100 % ETc), In3 (75 ppm) and Kn3 (40 ppm) gave the highest values for plant height, leaf area index, total chlorophyll and No. of branches plant.Seed yield was positively significantly and correlated to the leaf area index, total chlorophyll, root length, plant fresh and dry weights, No. of branches and leaves plant, No. of pods plant, 100-seed weight and pod yield plant whereas, the plant height was significantly negatively correlated to seed yield.Therefore, it is recommended to grow faba bean plants (Nubaria 1) with applying 2430 m fed of irrigation water and foliar spraying indole acetic acid (IAA) and kinetin at levels 75 and 40 ppm, respectively to obtain high seed yield and quality of faba bean.
SEDIMENT TRANSPORT AND BEACH TRANSFORMATION.

Laboratory and field investigations were performed in order to formulate a predictive model of two-dimensional beach profile change. The observed transport was classified into six types, and transport formulas were deduced for each type based on a microscale description of sediment movement caused by wave action. A numerical model of two-dimensional beach transformation was then developed. Beach profile changes calculated with the model were then compared with the laboratory results. The model was found to give reasonable results except in the vicinity of the wave plunging point. The sediment transport calculation is based on a sinusoidal velocity profile. The model appeares to give good results as long as the wave motion can be reasonably approximated by linear wave theory.
Sub seasonal variations of weak stratospheric polar vortex in December and its impact on Eurasian air temperature

ABSTRACT Weak stratospheric polar vortex (WPV) events during winter months were investigated. WPV events were identified as being weakest in December, accompanied by the most dramatic increase in geopotential height over the polar region. After the onset of a December WPV event, the dynamic processes influencing Eurasian temperature can be split into two separate periods. Period I (lag of 0–25 days) is referred to as the stratosphere–troposphere interactions period, as it is mainly characterized by stratospheric signals propagating downwards. In Period I, a stratospheric negative Northern Annular Mode (NAM) pattern associated with the WPV propagates downwards, inducing a negative NAM in the troposphere. The anomalous low centers over the Mediterranean and North Pacific bring cold advection to northern Eurasia, resulting in a north-cold–south-warm dipole pattern over Eurasia. The zero line between negative and positive temperature anomalies moves southwards during days 5–20. Stratospheric cold anomalies at midlatitudes propagate downwards to high latitudes in the troposphere and contribute to the dipole structure. During Period Ⅱ (lag of 25–40 days), as downward signals from the stratosphere have vanished, the dynamic processes mainly take place within the troposphere. Specifically, a wave train is initiated from the North Atlantic region to northern Europe. The propagation of wave activity flux intensifies a cyclonic anomaly over northern Europe, which brings cold advection to Scandinavia and warm advection to central Asia. Therefore, a northwest-cold–southeast-warm dipole structure occupies Eurasia and migrates southeastwards during this period. GRAPHICAL ABSTRACT
Chainage efficace de contour

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Chainage efficace de contour Gerard Giraudon
Contribution of personal weather stations to the observation of
deep-convection features near the ground

Abstract. The lack of observations near the surface is often cited as a limiting factor in the observation and prediction of deep convection. Recently, networks of personal weather stations (PWSs) measuring pressure, temperature and humidity in near-real time have been rapidly developing. Even if they suffer from quality issues, their high temporal resolution and their higher spatial density than standard weather station (SWS) networks have aroused interest in using them to observe deep convection. In this study, the PWSs contribution to the observation of deep-convection features near the ground is evaluated. Four cases of deep convection in 2018 over France were considered using data from Netatmo, a PWS manufacturer. A fully automatic PWS processing algorithm, including PWS quality control, was developed. After processing, the mean number of observations available increased by a factor of 134 in mean sea level pressure (MSLP), of 11 in temperature and of 14 in relative humidity over the areas of study. Near-surface SWS analyses, and analyses comprising standard and personal weather stations (SPWS) were built. The usefulness of crowdsourced data was proven both objectively and subjectively for deep convection observation. Objective validations of SWS and SPWS analyses by leave-one-out cross-validation (LOOCV) were performed using SWSs as the validation dataset. Over the four cases, LOOCV root mean square errors (RMSEs) decreased for all parameters in SPWS analyses compared to SWS analyses. RMSEs decreased by 73 to 77 % in MSLP, 12 to 23 % in temperature and 17 to 21 % in relative humidity. Subjectively, fine-scale structures showed up in SPWS analyses, while partly or not at all visible in SWS observations only. MSLP jumps accompanying squall lines or individual cells were observed, as well as wake lows at the rear of these lines. Temperature drops and humidity rises accompanying most of the storms were observed sooner and at a finer resolution in SPWS analyses than in SWS analyses. The virtual potential temperature was spatialized at an unprecedented spatial resolution. It gave the opportunity to observe cold pool propagation and secondary convective initiation over areas with high virtual potential temperatures, i.e. favorable locations for near surface parcel lifting.
Identifying Hierarchical Multiple Distinct Facility Location Problems to Enhance Internet Service Efficiency

The Internet is integrated with new logistics protocols, intelligent analysis, and business models, which is resulting in a collaborative and highly distributed logistics and distribution system for warehouses and retailers. However, the overall benefits still rely on optimal locations and transportation to finish operations. In this study, we deal with a hierarchical multiple distinct facility location problems (HMDFLP) to solve warehouse and retailer location problem and to enhance the real efficiency of internet service. In many real situations, the limited space of the urban environment may lead to practical problems in locating a large warehouses facility in a single location and may need to be partitioned and placed at different locations with synergy patterns to fulfill orders from retailers. The mathematical model is developed for solving this problem and is constrained to five attributes: single sourcing, maximal covering location problem (MCLP), p-median, p-dispersion, and hierarchical location. This study also proposes a fast iterated local search (FSILS) algorithm to bridge this knowledge gap. The experimental results show that the FSILS algorithm is more efficient in terms of computational time than other known similar approaches and has a significant contribution to business strategy in a realistic location environment to promote industry development.
Site Response Characteristics of Compacted Gravel Fill in Iceland

Local site conditions can greatly increase the intensity and character of earthquake shaking and, thus, the extent and type of structural damage. The removal and replacement of in-situ soils with compacted gravel-sized volcanic rock has been prevalent in the Icelandic foundation subgrade construction practice for decades, despite the unknown seismic site response characteristics of the fill (e.g., the predominant frequency and relative site amplification). To fill this knowledge gap, over 500 hours of microtremor measurements were made at six study sites located throughout the Reykjavík, Iceland, capital region. Measurements recorded at various construction stages (e.g., the in-situ or pre-excavation, post-excavation, intermediate grades, and final grade) reveal the change in site response characteristics before and after gravel fill placement. The data was analyzed using the horizontal-to-vertical spectral ratio (HVSR) technique over a bandwidth of 0.3 to 25 Hz. Generally, the pre-excavation condition had a predominant site frequency between 3.5 and 7 Hz with relative amplification between 3.8 and 3.9 times. The placement of gravel fill atop dense to very dense silty sand underlain by bedrock shifts the predominant frequency between 10 and 16 Hz with a relative peak amplification between 2.5 and 5.3 times, generally increasing with fill thickness. Fill underlain by undulating lava rock also results in a higher a predominant frequency between 9 and 10.5 Hz, but little change in relative site amplification occurred at these frequencies (between 0.95 and 1.2 times). This dissimilarity is due to the unique lava rock HVSR signatures which have large amplification values (between 2.6 to 3.9 times) throughout the high-band frequency range. Additional investigations of sites underlain by lava rock are required to draw stronger empirical trends. The data set produced by this study can serve as a useful tool for the local geotechnical and seismological communities to mitigate seismic risk for the capital region. Site Response Characteristics of Compacted Gravel Fill in Iceland
Determination of Rainfall Thresholds for Landslide Prediction Using an Algorithm-Based Approach: Case Study in the Darjeeling Himalayas, India

Landslides are one of the most devastating and commonly recurring natural hazards in the Indian Himalayas. They contribute to infrastructure damage, land loss and human casualties. Most of the landslides are primarily rainfall-induced and the relationship has been well very well-established, having been commonly defined using empirical-based models which use statistical approaches to determine the parameters of a power-law equation. One of the main drawbacks using the traditional empirical methods is that it fails to reduce the uncertainties associated with threshold calculation. The present study overcomes these limitations by identifying the precipitation condition responsible for landslide occurrence using an algorithm-based model. The methodology involves the use of an automated tool which determines cumulated event rainfall–rainfall duration thresholds at various exceedance probabilities and the associated uncertainties. The analysis has been carried out for the Kalimpong Region of the Darjeeling Himalayas using rainfall and landslide data for the period 2010–2016. The results signify that a rainfall event of 48 hours with a cumulated event rainfall of 36.7 mm can cause landslides in the study area. Such a study is the first to be conducted for the Indian Himalayas and can be considered as a first step in determining more reliable thresholds which can be used as part of an operational early-warning system.
Variability of Intraseasonal Oscillations and Synoptic Signals in Sea Surface Salinity in the Bay of Bengal

Intraseasonal oscillations (ISOs) significantly impact southwest monsoon precipitation and Bay of Bengal (BoB) variability. The response of ISOs in sea surface salinity (SSS) to those in the atmosphere is investigated in the BoB from 2005 to 2017. The three intraseasonal processes examined in this study are the 30–90-day and 10–20-day ISOs and 3–7-day synoptic weather signals. A variety of salinity data from NASA’s Soil Moisture Active Passive (SMAP) and the European Space Agency’s (ESA’s) Soil Moisture and Ocean Salinity (SMOS) satellite missions and from reanalysis using the Hybrid Coordinate Ocean Model (HYCOM) and operational analysis of Climate Forecast System version 2 (CFSv2) were utilized for the study. It is found that the 30–90-day ISO salinity signal propagates northward following the northward propagation of convection and precipitation ISOs. The 10–20-day ISO in SSS and precipitation deviate largely in the northern BoB wherein the river runoff largely impacts the SSS. The weather systems strongly impact the 3–7-day signal in SSS prior to and after the southwest monsoon. Overall, we find that satellite salinity products captured better the SSS signal of ISO due to inherent inclusion of river runoff and mixed layer processes. CFSv2, in particular, underestimates the SSS signal due to the misrepresentation of river runoff in the model. This study highlights the need to include realistic riverine freshwater influx for better model simulations, as accurate salinity simulation is mandatory for the representation of air–sea coupling in models.