Miracle-dz/UniFAR-FTUs huggingface dataset:
Measurement-based Network Model Reduction Of Distribution Systems Using Two-port Networks

This paper proposes a measurement-based method to derive a reduced-network model of distribution systems in real-time using two-port networks. Instead of relying on an offline model, the method uses only synchronized voltage and current phasor data collected from a limited number of strate-gically placed distribution-level phasor measurement units. An important feature of the measurement-based approach is that it is adaptive to operating-point changes. Through numerical examples, we verify the accuracy of the reduced-network model by comparing its power flow solution and optimal power flow solution to that of the original distribution system from which measurements are obtained.
Application of Multi Agent System and Transition Matrix Analysis to Logistics System for Equal Distribution under Disaster Situation

The logistics under disaster situation has recently been dealt with in many publications and most of the studies focus on optimization techniques from the viewpoint of the Operational Research. On the other hand, it is effective to grasp roughly the transportation capability of the logistics system in the disaster response plan, which may constitute a part of disaster prevention plan, prior to optimization. Under disaster circumstance, equal distribution system for evacuation refugees is essential and such system should be established taking into account the constraints of the logistics system under the circumstance different from ordinary situation. Two analysis tools for the equal distribution system are introduced in this paper, in order to estimate the performance of the logistics system for disaster response. One is a simulator based on the Multi-Agent System, in which the vehicles, e.g. trucks, ships, aircrafts, as agent determine their delivery destination corresponding to sufficiency ratios of the relief supplies of respective areas. The other is a method based on the transition matrix analysis that estimates where the bottleneck takes place. Combining these two tools enables to design more efficient and robust logistics systems. Applying these tools to a logistics system in a disaster response plan of a city, where a large scale disaster is concerned, effective countermeasures are quantitatively clarified.
Elevated Right Atrial Pressure Associated with Alteration of Left Ventricular Contractility and Ventricular-Arterial Coupling in Pulmonary Artery Hypertension*

Pulmonary artery hypertension (PAH) is a progressive disorder which leads to heart failure and death. Development of dilated right ventricle (RV), progressive RV dysfunction and increased right atrial (RA) pressure make the RV transition from a compensated to a decompensated phase and eventually leads to heart failure. However, the relationship between elevated RA pressure and left ventricular contractility and ventricular arterial coupling (VAC) has not been well studied. 36 patients were recruited and underwent both right heart catheterization (RHC) and cardiac magnetic resonance (CMR). Left ventricular (LV) pressure-volume loops were reconstructed from RHC and CMR. LV contractility was assessed by end-systolic elastance (Ees) using single-beat method and arterial elastance (Ea) was estimated as the ratio of end-systolic pressure and stroke volume (SV). The VAC was calculated as the ratio of Ees and Ea (i.e. Ees/Ea). The results demonstrated a nonlinear relationship between RA pressure and Ees, RA pressure and VAC. Ees increased when RA pressure increased to 7 mmHg and then decreased when RA pressure exceeded 7 mmHg. Ees were 2.79 ± 1.61 mmHg/ml, 4.27 ±1 33 mmHg/ml, 2.69 ± 0.89 mmHg/ml and 2.36± 1.10 mmHg/ml at ascending quartiles of RA pressure, respectively (quartile 1: RAP≤5 mmHg; quartile 2: 510 mmHg). Similarly, VAC were 1.36 ± 0.61, 1.93±0.86, 1.16 ± 0.55 and 0.95± 0.27 the four quartiles (both ANOVA P < 0.05). We found that there was a nonlinear relationship between RA pressure and LV contractility, and between RA pressure and ventricular-arterial coupling. A cut-off value of 7 mmHg of RAP may indicate a decompensated LV hemodynamics.
A Survey on Schedulability Analysis of Rate-Adaptive Tasks

In automotive real-time systems applications, Multiprocessor Control Units (MCU) handle the engine particular tasks which depend on the specific crankshaft rotation angles and are also a function of the instantaneous angular velocity of the engine. The timing behavior of Adaptive Variable Rate (AVR) tasks has been analyzed in several papers, whose results allow verifying the behavior of engine control applications under different sets of assumptions. This survey paper highlights the major scheduling policies that have been studied in past. In particular, scheduling of such tasks at an arbitrary mode is derived for static as well as dynamic state of the engine.
Adaptive walk control for hexapod robot based on iterative model-checking and Timekeeper control

This paper proposes an adaptive walk control for a hexapod robot which is controlled on the basis of a gait control, Follow-the-Contact-Point (FCP) gait control. The FCP gait control is a decentralized and event-driven controller and decides the order and timing of the legs to contact on the ground. Thanks to that, The robot is capable to walk over various uneven terrain. However, adaptive and real-time adjustment of the timing is required to keep walking performance, such as deadlock-free and not to drag the feet on the ground. Therefore, in this paper, such the adjustment method for the FCP gait control is addressed. First, the behavior of each leg of the robot is modeled by a timed-automaton, and specifications that the robot should satisfy to keep walking performance are expressed by computational tree logic. Next, upper/lower boundaries of time which is elapsed in each control state of the FCP gait control are derived based on an algorithm which is utilizing model checking. Then, the adaptive method “Timekeeper control” to guarantee the time boundaries in real-time is proposed. Finally, the validity of the proposed method is verified through experiments in which a real hexapod robot walks on uneven terrain.
Electronically Steerable Quasi-Uniform Leaky-Wave Antenna Based on Substrate Integrated Waveguide

In this paper, we introduce a novel electronically beam steered leaky-wave antenna. The proposed quasi-uniform leaky-wave antenna is implemented on a substrate integrated waveguide. The introduction of switches over the slots on the broad wall of the antenna leads to beam steering with variations in the side-lobe levels, directivity, and gain of the antenna. A center frequency of 28.5 GHz has been selected. The optimized length, width, and height of the antenna are 68 mm, 24 mm, and 0.3 mm, respectively. Scanning capability, low profile, simplicity, and high gain in the proposed structure makes it a suitable candidate for 5G navigation systems and cellular networks.
A Novel Four-Dimensional Modulation Format Based on Subset Selection

Coherent optical transmission systems have a four-dimensional (4-D) signal space (two quadratures in two polarizations), which can be used to create 4-D modulation formats that have better sensitivity than traditional two-dimensional modulation signal. In this paper, we propose an interesting four-dimensional modulation format based on subset selection for coherent optical fiber communications. We compare the proposed modulation format to the polarization multiplexed quadrature amplitude modulation(PM-QAM) and the set-partitioning M polarization-multiplexed QAM (M-SP-QAM). Simulation results show that for the same spectral efficiency, the proposed modulation format can get SNR gains of up to1dB compared with PM-MQAMs at 7% forward exchange control (FEC) threshold at a BER of 1e-3 in back-to-back system. After 800 km transmission distance, the signal-to-noise ratio (SNR) gain is 1.05dB. It is also shown that the mapping process of the proposed modulation format has lower latency and occupies less memory units compared with the commonly used look-up-tables mapping method.
Statistical and ANN based prediction of wind power with uncertainty

This paper presents the wind energy forecast model based on two different methods. The statistical and ANN methods are used for one day ahead short-term wind energy forecasting. The weather data are available from world weather online site and actual power data are provided by state load dispatch center (SLDC), GETCO, Gujarat. A six-input variable are used for training of forecasting models. Statistical models and ANN models are developed using ARMA and Feed Forward Neural Network with gradient decent momentum back propagation algorithm. The model accuracy is evaluated by Mean Absolute Percentage Error (MAPE) and Mean Squared Error (MSE), compare forecasted result of both the models and try to minimize the MAPE and MSE.
Tri-copter Drone Modeling with PID Control Tuned by PSO Algorithm

The tri-rotors1 are more recent kinds of drones, as compared with the mostly used quad-rotors1 because of the numerous special characteristics over the other types of multi-copters. Many technical features specialize tri-copters like small volume that is useful in slender places, light weight, extended battery existence, and agility in translation and turns. In this paper, (single tri-rotor) design is theorized and the nonlinear mathematical model is derived completely by Newton-Euler formula then the Proportional1-Integral1 and Derivative1 (PID) controller1 is utilized to control the rotational and translational equations1, six PID controllers are used for six Degrees of Freedom (DOF) equations of the model with the associated parameters are tuned by Particle1 Swarm1 Optimization1 (PSO) method to minimize the whole Integral1 Time1 Absolute1 Errors1 (ITAEs) for the tri-rotor model and the effects are gained by Simulink1 in MATLAB1. The results were satisfactory with the stability of the system and with little delay. "
EFFECT OF SOIL AMENDMENTS AND NITROGEN FERTILIZER SOURCES ON SUGAR BEET PRODUCTIVITY GROWN UNDER SALT AFFECTED SOIL CONDITIONS

A field experiment was carried out during the successful winter season of 2012/2013 on sugar beet (Bete vulgaris L) c.v Gloria, on newly reclaimed salt affected soil at the experimental farm of El-Hosinia Station, Agriculture Research Center, Shrakia Governorate, Egypt. The main objectives of the study were to examine the use of soil amendments, (ferrous sulfate 7Mg/fed, gypsum 5Mg/fed and aluminum sulfate 6.5Mg/fed) and different nitrogen sources, (Ammonium Nitrate 33.5%N, Ammonium Sulfate 20.5% N and Ureaformaldhyde 38%N) on sugar beet productivity and its quality, root content of NPK, soil fertility and some chemical soil properties after harvest. The experimental layout was spilt plot design with three replicates, soil amendments were the main plots while nitrogen sources were the sup plots. The obtained results indicate that the combined application of either the soil amendments or different nitrogen sources markedly increased most of sugar beet yield and its quality parameters i.e. root yield, sugar yield, TSS %, sucrose % and purity % significantly. Interaction between soil amendment ferrous sulfate and N source ureaformaldhyde recorded the highest values of root yield (8.883t/fed), sugar yield (1.430t/fed), TSS% (23.10%) and sucrose % (16.73%), respectively. Also, the highest value of N, P and K concentration in roots were obtained by application of ureaformaldhyde as nitrogen fertilizer combined with ferrous sulfate or aluminum sulfate or gypsum. Also, results showed that application of soil amendments in combination with nitrogen sources improved soil fertility, which reflected on the increases of available N, P and K values. Moreover, results revealed also that application of soil amendments combined with different nitrogen sources significantly reduced the values of soil EC, ESP and pH.
A Novel Kernel Clustering Algorithm

K-means algorithm is one of the most famous clustering algorithms in data mining due to its simplicity. Kernel K-means is an extension of K-means to cluster nonlinear separable data. However, it still has some limitations like sensitivity and convergence to the local optima. In this paper, we show how to implement a new novel kernel-clustering algorithm that is robust and converges to the global solution. We show using artificial and real data sets that the proposed kernel algorithm performs better than the standard kernel K-means algorithm.
Potential of Off-grid Solar PV/Biogas Power Generation System: Case Study of Ado Ekiti Slaughterhouse

In Nigeria, some economically important facilities are not functioning optimally due to lack of/inadequate electricity from the national grid. To solve this problem, these facilities result to using diesel generators which are mostly uneconomical and environmentally unfriendly. Improving comparative economic advantage means that Renewable Energy Sources (RES) with reduced emission and long term cost of energy is a viable alternative. This work describes the potentials of using solar PV/biogas power system to supply the slaughterhouse located in Ado Ekiti, South West Nigeria through an optimal design and techno-economic analysis. With an annual average daily radiation of 4.93 kWh/m 2 /d, the facility slaughters an average of 25 cows daily to yield a biomass supply of about 1150 kg/day. Simulation results from HOMER software present an optimal PV/biogas generator/battery/converter system that is able to supply the 164 kWh daily load requirement of the facility. The PV system supplied 38% of the annual total electricity production which stood at 76,384 kWh/yr. Cost analysis also indicates that the system has levelized cost of energy of $ 0.236/kWh and a Net Present Cost (NPC) of $ 92,988. 60% of this cost is shared equally between the PV and battery storage system while the biogas generator and digester and converter cost make up 25% and 15% respectively.
Evaluating sampling techniques for measuring ghost crab (Ocypode quadrata) burrow density

Sandy beach ecosystems are boundaries between land and sea and operate as critical components of larger, dynamic coastal systems. Beaches facilitate nutrient and sediment exchange, are shaped by impact of wind and waves, and are habitat for terrestrial and marine organisms (Defeo et al., 2009; Schlacher, et al., 2007). A wide variety of human activities threaten beach ecosystems: intense recreation such as trampling and off-road vehicle use; physical manipulation for resources, navigation, and land or ocean access; pollution of the beach face or waterways; and climate change impacts such as rapid rise in sea level and altered weather patterns (Brown and McLachlan, 2002; Defeo et al., 2009). Thus, beaches are sites of a familiar conflict between human use and conservation of ecosystem function, composition, and structure. Management of natural resources and ecosystems aims to address this conflict by balancing goals of human use with the preservation of ecological services (James, 2000; Schlacher et al., 2008).
Water End Use Clustering Using Hybrid Pattern Recognition Techniques - Artificial Bee Colony, Dynamic Time Warping and K-Medoids Clustering

The smart water meter collected data has made a great progress for the categorization of residential water end use events, the efficiency and accuracy still need to be improved. In this paper, an advanced algorithm is proposed for clustering the end-use category of a mechanical appliance. For this study, the database of end use events was collected using smart meters from over 200 households located in South-east Queensland (SEQ), Australia. Firstly, the raw data is pre-processed and physical characteristics (e.g., volume, duration, max flowrate, etc.) are extracted. Due to the type of the dataset is water end used flow data, which based on time series, a K-Medoids clustering algorithm based on the Dynamic Time Warping algorithm is used for clustering. In addition, a swarm intelligence which is named Artificial Bee Colony algorithm brings the whole system into equilibrium. Numerical experiments are based on toilet flushing events. Results indicate that the hybrid technique improves the clustering accuracy from 82.85% to 95.71%, and it can be implemented to other mechanical water end use events such as clothes washers and dish washers.
Natural range of variation of red fir and subalpine forests in the Sierra Nevada Bioregion

Meyer, Marc D. ; North, Malcolm P. 2019. Natural range of variation of red fir and subalpine forests in the Sierra Nevada bioregion. Gen Tech. Rep. PSW-GTR-263. Albany, CA: U.S. Department of Agriculture, Forest Service, Pacific Southwest Research Station. 135 p. Comparisons between historical and current conditions indicate that modern red fir (Abies magnifica) and subalpine forests of the Sierra Nevada bioregion are largely within their natural range of variation (NRV) with respect to compositional, functional, and some structural variables. Exceptions common to both forest types include a considerable shift in the tree size class distribution to smaller diameters, decrease in large-diameter tree densities (e.g., red fir, lodgepole pine [Pinus contorta], western white pine [P. monticola]), and greater homogenization of forest structure at both stand and landscape scales. Red fir forests had an increase in tree cover in lower canopy strata, and a lengthening of fire return intervals and fire rotations during much of the 20th century. Subalpine forests had increases in treeline growth and recruitment, and a marginal lengthening of fire return intervals and fire rotations during much of the 20th century. For both forest types, fire frequency and severity, moisture stress, the incidence of pathogens and insects, and tree mortality rates are projected to increase and likely exceed the NRV with climate change. Climate envelope models consistently project a substantial loss in suitable habitat or high climate vulnerability of both red fir and subalpine forests in the Sierra Nevada bioregion by the end of the 21st century, suggesting that the greatest changes in these forests in the coming decades will occur as a direct or indirect consequence of climate change.
Modelling the Addition of Limestone in Cement using HYDCEM

Hydration models can aid in the prediction, understanding and description of hydration behaviour over time as the move towards more sustainable cements continues. HYDCEM is a new model to predict the phase assemblage, degree of hydration and heat release over time for cements undergoing hydration for any w/c ratio and curing temperatures up to 450C. HYDCEM, written in MATLAB, complements more sophisticated thermodynamic models by predicting these properties over time using user-friendly inputs within one code. A number of functions and methods based on up to date cement hydration behaviour from the literature are hard-wired into the code along with user-changeable inputs including w/c ratio, curing temperature, chemical compositions, densities and enthalpies. Predictions of hydration product volumes from the silicate, aluminate and ferrite phases can be determined, including C-SH, calcium hydroxide, hydrogarnet (if applicable) ettringite and monosulfate. A number of comparisons have been made with published phase assemblages using thermodynamic models and HYDCEM predictions to assess its accuracy and usefulness. This paper presents simulations of cement hydration and microstructure development with and without the additional of ground limestone using the HYDCEM model, both in terms of monocarbonate growth at the expense of monosulfate and ettringite. Comparisons with published phase assemblages show good agreement in terms of volumetric growth and behaviour.
Automatic Malware Classification via PRICoLBP

Creating effective features is a critical issue in malware analysis. It requires a proper tradeoff between discriminative power and invariance. Previous studies have shown that it is fairly effective to design features based on the binary code. However, the current existing binary-based features seldom take into consideration the problem of obfuscation, such as relocated sections, incomplete code and redundant operations. In this paper, we propose a novel Pairwise rotation invariant co-occurrence local binary pattern (PRICoLBP) feature, and further extend it to incorporate the Term frequency-inverse document frequency (TFIDF) transform. Different from other static analysis techniques, our method not only achieves better linear separability, but also appears to be more resilient to obfuscation. In addition, we evaluate PRICoLBP-TFIDF comprehensively on three datasets from different perspectives, e.g., classification performance, classifier selection and performance against obfuscation. What's more, we compare our PRICoLBP-TFIDF method with other techniques, and demonstrate that PRICoLBP-TFIDF is quite an efficient and effective tradeoff between discriminative power and invariance.
New Vertebrate Fossil Site from the Early Cretaceous Sao Khua Formation, Sakon Nakhon Province, Northeastern Thailand

A new vertebrate fossil site, “Phu Sung” in Sakon Nakhon Province was discovered. Various vertebrate fossils belonging to fresh water shark, bony fish, turtle, crocodile and dinosaur were found in reddish silty mudstone of the Early Cretaceous Sao Khua Formation of the Khorat Group. Crocodilian remains including a complete skull and partial articulated skeleton are very well preserved associated with turtle remains. Moreover, well preserved 19 turtle shells were found accumulated together. These discoveries will certainly fulfill our knowledge about these aquatic taxa from the Early Cretaceous Sao Khua Formation. The exceptional preservation of Phu Sung fossils could probably relate to the paleoenvironment in the Early Cretaceous of Thailand.
A tale of four cities: A semantic analysis comparing the newspaper coverage of air pollution in Hong Kong, London, Pittsburgh, and Tianjin from 2014 to 2017

A computer-aided semantic analysis (using Linguistic Inquiry and Word Count [LIWC]) examined how newspaper coverage of air pollution from 2014 to 2017 may affect the public agenda in four cities—Hong Kong, London, Pittsburgh, and Tianjin. Results show that after controlling for the real-time air quality, the agenda-setting effect was found in Hong Kong, London, and Pittsburgh, but not Tianjin. Tianjin’s reports also contained more future-framed words but fewer present-framed words than other cities.