esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Operation of CCHP Smart Distribution Grid with Integration of Renewable Energy
## 3 Proposed Model of Demand Response  
The DR technique is applied to the electric load to change the load pattern of power consumption and shift the peak load to the off-peak hours. The max load is shaved and shifted to off-peak hours and is performed using a DR program, where the DR program is considered as a decision variable in the optimization problem.  
The aim of this technique is to shave the max load and limit it to a specific value, where the cutting load is shifted from peak to off-peak hours consumption. The following equations are considered to perform the DR program.  
\[P^{t}_{L-f}=\left\{P^{t}_{L}-\left(P^{t}_{L}-P^{t}_{L-p}\right)\cdot\delta^{t} _{c}\right\}+P^{t}_{L-m} \tag{25}\]  
\[P^{t}_{L-m}=a_{p}\cdot\left\{\frac{\sum_{t\in\Omega}\left(P^{t}_{L}-P^{t}_{L-p} \right)\cdot\delta^{t}_{c}}{h_{p}}\right\}\cdot\delta^{t}_{f} \tag{26}\]  
\[\text{for }L^{t}_{p}>P^{t}_{p}\text{ {and }}L^{t}_{q}>P^{t}_{q}\delta^{t}_{c}=1 \tag{27}\]  
\[\delta^{t}_{c}=0\text{ for }L^{t}_{p}\leq P^{t}_{p}\text{ {and }}L^{t}_{q}\leq P^{t}_{q} \tag{28}\]  
\[\text{for }t_{1}\leq t\leq t_{2}\delta^{t}_{f}=1 \tag{29}\]  
\[\text{for other values of }\text{ }t\delta^{t}_{f}=1 \tag{30}\]  
\[0\leq a_{p}\leq 1 \tag{31}\]  
\[0\leq a_{q}\leq 1 \tag{32}\]  
where \(P^{t}_{L-m}\) is the hours, \(a_{p}\) is the percentage of the cutting load during the high load hours and supplied again during off-peak hours, \(h_{p}\) is the number of off-peak hours (h), \(P^{t}_{L}\) is the base load (kW), \(P^{t}_{L-f}\) is the final modified load, \(P^{t}_{L-p}\) is the peak load, and \(\Omega\) is the set of on-peak hours during which the energy is reduced.  
The peak shaving parameters affect the performance and results of the DR program, and this is reflected in the results of the optimizing approach, cost value, and scheduling of the energy generation devices. Therefore, these parameters should be chosen carefully. In this study, these parameters are chosen based on operational thresholds commonly found in similar systems and supported by open literature resources.
## 2 Audio Features and Pre-Trained Neural Networks
### Pre-Trained Networks  
Convolutional neural networks (CNN) have been commonly used for multi-channel sound scene classification in the recent years. CNNs are a sub-type of neural networks that utilize multiple convolution stages for classification [32]. Similar to the traditional neural network, CNNs are composed of three layers, namely: the convolutional layer, the pooling layer, and the fully connected layer [33]. Nonetheless, instead of a traditional fully connected layer, only a subset of the previous layer neurons is connected to the next ones. This suggests improvements in run time, computational complexity, and memory requirements.  
There are various pre-trained convolutional neural network models for classification. This is achieved through the use of transfer learning, which allows the reuse of a previously trained network's weights to train a new network model [34], typically using new training data representing new classes. Several advantages of transfer learning include an improved efficiency both in time duration requirements of the model building process, training, and the learning workflow [35]. Further, several research works also report improved results by using transfer learning on pre-trained networks as opposed to training a network from scratch [36].  
Various examples of pre-trained CNN models include AlexNet [37], GoogleNet [38], ResNet [39], Inception-ResNet [40], Xception [41], SqueezeNet [42], VGGNet [43], and LeNet [44]. These networks are trained with large datasets, and the weights are saved in order to be re-used for transfer learning. Table 1 provides a summary of the comparison between these pre-trained networks in terms of their basic characteristics, including the year of introduction, network size in MB, image input size, number of layers, number of parameters, and the 5% error rate. Nonetheless, as per our previous works, the AlexNet model returns the highest accuracy for domestic audio classification applications [45; 46].
## 2 Discussion on Thermal Imaging Diagnostics in Electrical Power Engineering
### Low-Voltage Installations
The measured temperature difference between the overheated phase temperature T and the ambient temperature T\({}_{\text{o}}\), for the current load I, is converted into a temperature difference \(\Delta\)T\({}_{\text{on}}\), corresponding to the rated load I\({}_{\text{n}}\), according to the formula:  
\[\Delta\text{T}_{\text{n}}=(\text{T}_{\text{n}}-\text{T}_{\text{on}})=(\text {T}-\text{T}_{\text{o}})\cdot\bigg{(}\frac{\text{I}_{\text{n}}}{\text{I}} \bigg{)}^{\text{k}} \tag{2}\]  
where:  
T\({}_{\text{o}}\)--the clamp and ambient temperature for the current load I, K;  
T\({}_{\text{n}}\), T\({}_{\text{on}}\)--the clamp and ambient temperature for rated load I\({}_{\text{n}}\), K. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Fault Class** & **\(\Delta\)T\({}_{\text{n}}\), K** & **Recommendations** \\ \hline
1 & \textgreater{}30 & IMM \\ \hline
2 & 5–30 & UR \\ \hline
3 & \textless{}5 & O \\ \hline \hline \end{tabular} Abbreviations: IMM—immediately, UR—urgently, and O—observe.  
\end{table} 
Table 2: Assessment of the need to repair the clamps and conditions of a low-voltage installation based on the calculation of the temperature rise \(\Delta\)T\({}_{\text{n}}\) for the nominal load I\({}_{\text{n}}\)[20]. The permissible values of \(\Delta\text{T}_{\text{n}}\) are specified in Table 2.  
In another paper [23], the linear formula has been obtained for the temperature of the hot spot as a function of the current. It allows us to evaluate the temperature that would be reached if the nominal current circulated through the circuit. The drawback of this is that heat conditions are not considered as they previously were.
## 4 Conclusions  
In this work, an AM Ti-6 Al-4V part with four internal holes was investigated by using the LUT system. The results obtained from LUT were validated by using an XCT method. The main conclusions can be summarized as follows:  
1. The amplitude of laser-generated ultrasonic Rayleigh wave attenuated approximately linearly with the increasing internal hole diameter.
2. Owing to the diffraction by holes, amplitude of longitudinal waves decreased. Meanwhile, the longitudinal wave may take a longer time to arrive at the top surface of the sample.
3. Sub-millimeter holes with a diameter of 0.8 mm was clearly identified in both B-Scan and C-Scan images obtained from the utilized LUT system. Holes with diameters of 1.0 and 2.0 mm were also identified.  
Combined with the advantages of being completely non-contact and its integration ability due to the long distance from focal lens to the part, the LUT method can be further utilized in the online monitoring of the AM process.  
[PERSON] contributed to the experimental study, data analysis, and preparation of the manuscript. [PERSON] and [PERSON] helped to conduct the LUT inspection. [PERSON], [PERSON], and [PERSON] reviewed the manuscript and provided supervision and guidance in this research. [PERSON] edited the manuscript. [PERSON] and [PERSON] contributed to the AM fabrication of the sample. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key Research and Development Program of Guangdong Province (grant no. 2018B090905001); the Natural Science Foundation of Guangdong Province (grant no. 2018A030313044); the Science and Technology Program of Shenzhen (grant no. JCYJ20170816171733384); the Hubei Provincial Natural Science Foundation of China (grant no. 2017 CFB657); and the Fundamental Research Funds for the Central Universities (grant no. 2042018 kf0240).  
The LUT equipment is supported by the National Laboratory of Solid-State Microstructures, Nanjing University, China.  
The authors declare no conflict of interest.  
Figure 7: XCT test results: (**a**) sectional top view and (**b**) sectional front view.
## 3 Results and Discussion
### Step 4: Optimizing Quaternary Cementitious Matrices
#### 3.3.1 Compressive Strength of Quaternary Cementitious Matrices  
As depicted in Figure 17 the addition of \(\mu\)S in pastes containing at least 20% FA by mass of binder is causing a retardation of the strength development, delivering lower compressive strengths. More than that, 10% \(\mu\)S, delay the strength gain by 28 days. These results were expected since according to CEN [41] the maximum allowable \(\mu\)S content in PC/\(\mu\)S cements is 10% by mass. In other words, for every 1% \(\mu\)S by mass, at least 9% of PC content by mass, is required so that there will be enough Ca(OH)\({}_{2}\) produced by PC hydration for the \(\mu\)S to react with. Therefore, the 10% \(\mu\)S addition was excessive and as an effect, unreacted \(\mu\)S particles were predicted to be distinguished by SEM investigations.  
Figure 17: Cylinder compressive strength of \(\mu\)S modified cementitious matrices based on PC60 LS20 FA20.  
As in the case of \(\mu\)S enhanced PC60 LS20 FA20, the \(\mu\)S enhanced low PC content PC43 LS20 FA37, showed similar compressive strength gain characteristics, although the \(\mu\)S content was lower in both combinations (Figure 18):  
* At day 7 both series had similar starting points (about 21 MPa for PC60 LS20 FA20 and 25 MPa for PC43 LS20 FA37), indicating that pozzolanic reactions had already been initiated within the first 7 days.
* After day 28 the compressive strength of the reference paste is almost the same for both reference pastes, but for the \(\mu\)S enhanced ones, an improvement was observed. Therefore, although high LS contents are reported to be causing deterioration of the compressive strength at later ages the presence of \(\mu\)S offered a limited (due to the additional presence of FA) enhancement.  
A marginal improvement in strength was also observed at day 1, followed by deterioration at day 28 and 56 with respect to the reference paste. These results were in agreement with other findings, suggesting that the increasing OPC replacement is liable for the decrease in both compressive and flexural strengths [12]. The limited amount of PC and the high amount of FA present created some antagonism with the \(\mu\)S particles. Moreover, since for 1% \(\mu\)S, at least 9% of PC content by mass, is required so that the \(\mu\)S can react with the Ca(OH)\({}_{2}\) produced by PC hydration for these series of pastes, the \(\mu\)S content should have been limited to approximately 4.8% by mass. Furthermore, knowing that for high replacements of PC with FA and LS the resulting strengths will be low, it was of particular interest to investigate if the nanoparticles could alter the performance of the low PC content blended cements, carried out in other research [42].
## 4 Simulation and Results Discussion
### Weight Factor  
The conventional MPC incorporates a weight factor before the stator's flux error in the cost function. Traditionally, this factor requires offline adjustment. However, considering that all variables within this function possess identical properties, there is no necessity for  
Figure 8: Dynamic response of the PMSM under the proposed MITC: (**a**) Variation curve of rotational speed _Nr_; (**b**) Variation curve of electromagnetic torque _Tc_; (**c**) Variation curve of stator magnetic flux linkage _ps_.  
Figure 9: Average switching frequency for (a) Conventional MPC, (b) Proposed MPTC, and (c) Conventional DTC.  
weight factor adjustment. Extensive simulations and experimental tests are performed to determine the appropriate weighting factors in related studies. Moreover, these weighting factors are highly sensitive to different motor parameters. Table 2 lists the comparison parameters for the main values obtained by the traditional MPC and the proposed MPTC.  
A comparison of the results obtained by the multi-objective ranking method with other methods for optimizing the weighting factor offline shows similar results. However, the algorithm mentioned in the work does not require tedious offline calculations and weighting-factor optimization.
## 2 Methods
### Correction Technique of Displacement Signal Measured by UAV
#### 2.2.1 Empirical Mode Decomposition  
A residual \(r(t)\) and \(N\) IMFs \(h_{i}(t)\) can be obtained by decomposing the original signal \(x(t)\) through an iterative sifting process by the EMD:  
\[x(t)=\sum\limits_{i=1}^{N}h_{i}(t)+r(t) \tag{2}\]  
where one of the \(h_{i}(t)\) is the bridge true displacement signal, \(N\) is the number of IMFs.  
The process of EMD is shown in Figure 1. When the original signal \(x(t)\) is not monotonic, it is put into the sifting process as \(r_{i}(t)\) (\(i=0\)). The sifting process is as follows: the upper and lower envelope curves of the signal are formed by finding the local extremum of the signal, and the mean curves \(m(t)\) of the upper envelope \(s_{+}(t)\) and lower envelope \(s_{-}(t)\) are removed from the signal to obtain a new signal \(r_{i}^{\prime}(t)\). If \(r_{i}^{\prime}(t)\) does not meet Equation (3), it will be put into the sifting process again, otherwise, \(r_{i}^{\prime}(t)\) is the first IMF \(h_{i}(t)\) (\(i=1\)), and the sifting process is ended. Remove the obtained first IMF from the original signal to obtain a new signal, and judge whether the new signal is monotonous. If not, repeat the above process; otherwise, it is the residual separated from the original signal, and the decomposition process is ended. The resulting residual and all IMFs are the components of the original data. The expression of RT of \(r_{i}^{\prime}(t)\) is as follows:  
\[RT=\frac{\sum\begin{bmatrix}r_{i}^{\prime}(t)-r_{i}(t)\end{bmatrix}^{2}}{\sum \begin{bmatrix}r_{i}(t)\end{bmatrix}^{2}}\leq\in \tag{3}\]  
where \(\in\) can be set between 0.2 and 0.3 [21].  
The signal measured by a UAV including false displacement can be decomposed with EMD to obtain the true vibration signal of the bridge, which is one of the IMFs. EMD is not only suitable for the analysis of nonlinear and non-stationary signals but also suitable for the analysis of linear and stationary signals.
# A New Method of GPS Water Vapor Tomography for Maximizing the Use of Signal Rays
## 2 Methods
### New Tomographic Equation  
As shown in Figure 1, the research area is divided into n voxels and the water vapor density of each voxel is \(x_{i}(i=1,2\cdots n)\) in the traditional method. To improve the utilization of GPS data, the water vapor density in the four directions outside the research region are added to the observation equation as unknown parameters. Figure 2 displays the four new parameters (\(x_{n+1}\), \(x_{n+2}\), \(x_{n+3}\), and \(x_{n+4}\) for the north, east, south, and west, respectively) in the proposed method from a top view (left) and a side view (right). The boundaries of these four regions are selected based on the location of GPS receivers and the specifics of the tomographic area (see the process in the following experiment). The observation equation of the proposed method is as follows:  
\[\text{SWV}^{q}=\sum_{i=1}^{n}s_{i}^{q}\cdot x_{i}+\left(s_{n+1}^{q}\cdot x_{n+ 1}+s_{n+2}^{q}\cdot x_{n+2}+s_{n+3}^{q}\cdot x_{n+3}+s_{n+4}^{q}\cdot x_{n+4}\right) \tag{8}\]  
This equation is similar to that in the traditional method; only the four parameters in the parentheses are added. The distances produced by satellite rays outside the research area are expressed as \(s_{n+1}^{q},s_{n+2}^{q},s_{n+3}^{q},s_{n+4}^{q}\) and they equal 0 when the signal rays penetrate from the top boundary of the area. The new functional model for the water vapor tomography comprises the new observation equation and the constraint equation, which is described as:  
\[\left(\begin{array}{c}\text{SWV}_{q\times 1}\\ 0_{n\times 1}\\ 0_{(n-m)\times 1}\end{array}\right)=\left[\begin{array}{c}A_{q\times(n+4)}\\ H_{n\times(n+4)}\\ V_{(n-m)\times(n+4)}\end{array}\right]\cdot X_{(n+4)\times 1}+\left[ \begin{array}{c}\Delta 1_{q\times 1}\\ \Delta 2_{n\times 1}\\ \Delta 3_{(n-m)\times 1}\end{array}\right] \tag{9}\]  
Figure 2: Water vapor tomographic voxels in the proposed method (**left** for top view, **right** for side view).  
where A from Equation (8), H from Equation (4), and V from Equation (6) represent the coefficient matrices of the new observation equation, the horizontal constraint equation, and vertical constraint equation, respectively; \(X=[x_{1},x_{2},\cdots,x_{n},\cdots,x_{n+4}]\) is a vector of the water vapor density containing all voxels to be estimated; \(\Delta_{1},\Delta_{2},\Delta_{3}\) are the noises of the new observation equation, the horizontal constraint equation, and vertical constraint equation, respectively.
## 3 Results and Discussion
### Milling Operative Parameters
On the one hand, stone rotational speed decreased milling time, while, on the other hand, chickpea moisture content increased it [12; 20]. As highlighted in an earlier work [12], these results are due to the fact that dry raw materials are more brittle and less resistant than humid and conditioned ones, which show higher plasticity and are more difficult to be milled. Similar results were obtained for the other operative milling parameters. With respect to energy consumption, the results of the ANOVA found a significant individual effect of chickpea moisture content (\(p\) < 0.001), while this was not the case for stone rotational speed or their interaction. The sample with the highest moisture content (i.e., 12%) milled at the lowest stone rotational speed (i.e., 120 RPM) showed the highest energy consumption (in addition to the highest milling time). The significant effect of moisture content can also be observed in this case, where chickpea with highest moisture content (i.e., 12%) have a more plastic behavior during milling, leading to a higher strain of the electric motor with the subsequent increase in energy consumptions [12; 20]. Regarding average power, the two-way ANOVA highlighted individual main effect for stone rotational speed (\(p\) < 0.001) but not for moisture content or the factor interaction. As a result, average power significantly increases as the stone rotational speed increases. Tukey HSD test highlighted a significantly lower average power in the samples milled at the lowest stone rotational speed (i.e., 120 RPM). Since average power is the ratio between total work and the time for which the work is performed, and, moreover, given that the work has the dimensions of an energy (\(\mathsf{J}\)), as the stone rotational speed of the mill increases, the energy transferred (and consequently the work transferred) over time increases, resulting in a rise in average power, a result consistent with those of other authors [21; 22]. Finally, regarding specific milling energy, chickpea moisture content comes back to the fore. In particular, the two-way ANOVA found individual main effect for chickpea moisture content (\(p\) < 0.001), while this was not the case for stone rotational speed or factor interaction. Specific milling energy represents the energy required to produce 1 kg of chickpea flour. The results reported in Table 1 clearly highlight that specific milling energy significantly increases as chickpea moisture content increases. This result is consistent with milling time and with energy consumption, where dry raw materials are more brittle and less resistant than humid and conditioned ones, which show higher plasticity and are more difficult to be milled [12; 20].
# Frequency Contour-Strip Method for Characterization of Damage in Structures under Noisy Conditions
## 5 Experimental Validation
### Crack Identification Using Freequncy Contour-Strip Method
#### 5.2.1 Case 1  
Case 1 is related to a crack, \(x\) = 3.3% and \(y\) = 10% in the cantilever beam (Figure 11b). Equation (8) with the parameters specified with the specification values in Table 2 forms the analytical model for characterizing the transverse vibration of the cracked cantilever beam. A comprehensive model calibration procedure is carried out for the experimentally measured eigen-frequencies considering all possible crack cases (not only Case 1), giving valid parameter values instead of specification parameter values to form an updated analytical model.  
The function isosurface is used to solve the analytical model to produce the eigen-frequency panorama (Figures 7 and 8). Considering noise effect on eigen-frequencies, multiple tests are conducted to acquire a set of first three eigen-frequencies. The averages  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Length** & **Thickness** & **Width** & **Density** & **\(E\)** & **G** & **\(\
u\)** & **\(k\)** \\
**(mm)** & **(mm)** & **(mm)** & **(kg/m\({}^{3}\))** & **(GPa)** & **(GPa)** & **\(\
u\)** & **\(k\)** \\ \hline
500 & 19 mm & 12 mm & 7758 & 184 & 86 & 0.3 & \(10(1+\
u)/(12+11\
u)\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specification parameters of material and geometry of steel plate.  
Figure 12: Setup of dynamic testing.  
of this set of first 3 eigen-frequencies are 59.38 Hz, 372.27 Hz, and 1043.55 Hz, with standard variances 1.1, 1.8, and 3.7 that denote the noise effect. In terms of Equation (10), the frequency contour strips can be obtained as shown in Figures 10 and 11. From the frequency contour strips, the resulting frequency projection strips on the \(x\)-\(y\) plane are presented in Figure 11, in which the intersection region R of frequency projection strips can be observed in Figure 11b. According to Equation (11), the dimensionless crack parameters, \(x\) and \(y\) can be estimated from the intersection region R as \(\mu_{x}=2.95\%\) and \(\mu_{y}=9.75\%\), close to the genuine location and severity of the crack.
###### Abstract  
In this study, the Boltzmann equation with electric acceleration term is discretized and solved by the unified gas-kinetic scheme (UGKS). The charged particle transport driven by electric field is included in the electric acceleration term. To capture non-equilibrium distribution function, the probability distribution functions of gas is discretized in a discrete velocity space. After discretization, the numerical flux for distribution function is computed to update the microscopic and macroscopic states. The flux is decided by an integral solution of Boltzmann equation based on characteristic problem. An electron-ion collision model is introduced in the Boltzmann Bhatnagar-Gross-Krook (BGK) equation. This finite volume method for the UGKS couples the free transport and long-range interaction between particles. For simplicity, the electric field induced by charged particles is controlled by the Poisson's equation, which is solved using the Green's function for two dimensional plasma system subjected to the symmetry or periodic boundary conditions. Two numerical cases, linear Landau damping and Gaussian beam, are carried out to validate the proposed method. The linear electron plasma wave damping is simulated based on electron-ion collision operator. Comparison results show good accuracy and higher efficiency than particle based methods. Difference between [PERSON]'s equation and complete electromagnetic Maxwell equation is presented by numerical results based on the two models. Highly non-equilibrium and rarefied plasma flows, such as electron flows driven by electromagnetic field, can be simulated easily. The UGKS-Poisson model is proved to be promising in plasma flow simulation.  
plasma; Boltzmann equation; unified gas kinetic scheme; Poisson's equation; finite volume method  
35Q83; 82D10; 82C40; 74S10; 34B27