esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Mass Flow Rate Estimation  
In order to reduce the mass flow rate error between the one-dimensional design and the CFD result, a simple mass flow rate model was built. If the direction of inflow is axial, according to the energy equation:  
\[h_{1}+{W_{1}}^{2}/2=H_{1}(T_{1},P_{1})+{U_{1}}^{2}/2=h_{2}(p_{2},s_{1},\eta)+ {W_{2}}^{2}/2 \tag{13}\]  
Hence, the mass flow rate can be calculated (assume that \(t_{max}\) = 0.1\(c\) and the exit section is straight):  
\[\dot{m}=[1-0.3t_{\max}/(s\cos\beta_{2})]\rho_{2}W_{2}A_{2}\cos\beta_{2}=[1-0.0 3\sigma/\cos\beta_{2}]\rho_{2}W_{2}A_{2}\cos\beta_{2} \tag{14}\]  
Deviation angle model [22]  
\[\delta^{*}=m\theta/\sqrt{\sigma},\,m=0.185+0.0002(\psi+60)+0.0000000885(\psi+ 60)^{3} \tag{15}\]  
Revised De Haller stall criterion [20]  
\[Ha=W_{2}/W_{1}\geq\frac{[(2.2/D_{eq})0.6](0.15+11t_{b}/c)/(0.25+10t_{b}/c)}{1 +0.4\{\theta\sigma/[2\sin(\theta/2)\cos\gamma]\}^{0.65}} \tag{16}\]  
It is worth noting that this criterion is most reliable for speeds greater than about 85% of the design speed. In addition, the design parameters are limited by other conditions at the same time, for example, inlet blade angle \(\beta_{b1s}<73^{\circ}\), no negative reaction, and flow coefficent \(\varphi<0.5\) is preferred. So the available range of the flow coefficient and work coefficient become quite limited.
## 3 Isotherm Models
\(R^{2}\) is calculated from the sum of residuals (_i.e._, the difference between the experimental model and model-predicted bound concentrations). Linear regression is frequently used to determine the most fitted isotherm for two-parameter isotherms. The idea is to transform the data to create a linear graph and use the method of least squares to find the parameters of the isotherm. Examples include Scatchard plots of binding data [70, 77, 78] and linearized forms of Freundlich and Langmuir equations [69, 75, 79, 80]. It is very common to express the isotherm models in linear form. A drawback resulting from linearization (\(y=\mathrm{a}+\mathrm{b}x\)) is that some assumptions of linear regression are violated. In particular, the analysis of linear regression shows that the experimental data/points located around the fitting line has a Gaussian trend regarding their distribution. Furthermore, for each value of \(x\), the analysis with standard deviation is the same. Also, in some transformations, the relationship between \(x\) and \(y\) are altered. For example, when a Scatchard plot is created, the measured value of bound template winds up on both the \(x\)-axis (which plots bound) and the \(y\)-axis (which plots bound/free). In spite of these drawbacks, linearizing methods are a good starting point for generating initial values for non-linear regression.
# Determination of Fracture Properties of Concrete Using Size and Boundary Effect Models
## 2 Size and Boundary Effect Models
### [PERSON] et al. Boundary Effect Models
#### 2.1.2 [PERSON]'s ([PERSON]'s) Boundary Effect Model  
The [PERSON]'s and [PERSON]'s boundary effect models [21; 23; 34] are developed for wedge-splitting and three-point bending tests respectively, which all assume a constant crack-bridging stress \(\sigma_{n2}\) within the partially developed fracture process zone or \(\Delta a_{fic}\) (see Figure 2a). In contrast to the local point stress value \(\sigma_{n1}\) (see Figure 1b) defined in [PERSON]'s boundary effect model, \(\sigma_{n2}\) (see Figure 2a) is associated with the fictitious crack length \(\Delta a_{fic}\), and its value is affected by the choice of this length, thus becomes a non-local stress parameter. The strain condition along the crack plane, the equilibrium conditions of bending stress and bending moment were considered to determine this constant stress \(\sigma_{n2}\)[21; 34]. It is given by [23]:  
\[\sigma_{n2}=\frac{P_{max}(3W_{2}+W_{1})}{6t}/\left(\frac{W_{1}^{2}}{6}+\frac{ \Delta a_{fic}}{6}W_{1}+\frac{W-a_{0}}{2}\Delta a_{fic}\right), \tag{13}\]  
with \(W_{1}=W-a_{0}-\Delta a_{fic}\) and \(W_{2}=W+a_{0}+\Delta a_{fic}\).  
The advancement from [PERSON]'s boundary effect model to [PERSON]'s boundary effect model is the non-local nominal stress \(\sigma_{n2}\) used in the stress criterion. Thus, the local version of the boundary effect model was improved as a non-local version. Following the same idea of [PERSON]'s boundary effect model, [PERSON] et al. proposed in [23] the way of calculating the tensile strength and fracture toughness by the following linear relation:  
\[\frac{1}{\sigma_{n2}^{2}}=\frac{1}{f_{t}^{2}}+\frac{1.12^{2}\pi}{K_{c}^{2}}a_ {e1}. \tag{14}\]  
The local stress \(\sigma_{n1}\) is replaced by the non-local stress \(\sigma_{n2}\), and \(a_{e1}\) is the same as the one in [PERSON]'s boundary effect model. It is admitted that [PERSON]'s boundary effect model (\(\Delta a_{fic}=0\)) may overestimate the tensile strength of the material, and [PERSON]'s boundary effect model can lower the predicted tensile strength when \(\Delta a_{fic}\) increases [22]. This fictitious crack length \(\Delta a_{fic}\) was assumed to be proportional to the maximum aggregate size \(d_{max}\) of concrete [23]:  
\[\Delta a_{fic}=\beta_{1}d_{max}. \tag{15}\]  
It is concluded in [23] that \(\beta_{1}=1\) is a good approximation when the maximum aggregate size \(d_{max}\) is the dominant aggregate size of a concrete mix and the \(W/d_{max}\) ratio is below 20 (or even 50).
## 2 Materials and Methods
### Signatures Classification  
A neural network model was implemented for classification using the Keras Sequential in Python (Keras version via Tensorflow: 2.9.0, Python version: 3.7.4). The model was structured to process the 444-dimensional feature vectors generated by the radial Fourier-Mellin signatures and _LBP_ histogram from each RGB channel and grayscale. This neural network was designed with multiple dense layers to capture complex relationships within the feature space.
## 6 Optimization and Simulation Results
### Economic Analysis  
The cash flow summary of the hybrid power system within the project lifetime based on the average daily solar radiation (4.0 kWh/m\({}^{2}\)) and wind speed (4.0 m/s) for South Korea as a case study is given in Figure 18. However, this analysis can be extended to other cases. The breakdown for the IC, replacement, O&M and salvage costs incurred within the project lifetime is given in the following paragraphs.  
The _IC cost_, paid once at the beginning of the project, is directly proportional to the size of the system. From Table 3, the IC cost is $25,880. The breakdown of this cost is as follows: (i) For the SPV (size 6.0 kW \(\times\) cost $1000/1 kW = $6,000); (ii) for the WTG (size 1 kW \(\times\) cost $600/1 kW = $600); (iii) for the battery units (64 unit \(\times\) cost $300/unit = $19,200); and (iv) for the inverter (size 0.2 kW \(\times\) cost $400/1 kW = $80).  
The annual _O&M costs_ of the hybrid power system amounted to $752. The breakdown of this cost is as follows: (i) for the SPV (size 6.0 kW \(\times\) $10/1 kW = $60); (ii) for the WTG (size 1 kW \(\times\) $50/1 kW = $50); (iii) for the battery units (64 unit \(\times\) $10/unit = $640); and (iv) for the inverter (0.2 kW \(\times\) cost $10/1 kW = $2).  
It is clear that the battery bank represents the bulk of both the IC and O&M costs. However, this cost depended on the number of batteries in the system. Herein, the optimal number of batteries was determined by HOMER to be 64 battery; the number of batteries can be reduced. However, the load autonomy decreases, which is considered to be an important issue, especially in remote rural areas.  
The batteries have a lifetime of 10 years, which is the same as the project lifetime; the SPV array has a lifetime of 25 years, and the WTG has a lifetime of 15 years. Thus, neither requires replacement.  
Each component has a _salvage value_ at the end of the project lifetime. The SPV array salvage value is $3600, the highest in the system, which was computed based on Equation (10), (SPV array remaining lifetime (15)/SPV array lifetime (25)) multiplied by the replacement cost of the SPV array, $6000. While,  
Figure 17: Monthly statistics of the inverter output power.  
Figure 18: Cash flow summary within the project lifetime.  
the WTG salvage value is $200 the inverter salvage value is $27. Thus, the total salvage value at the end project lifetime is $3827.  
The economic analysis described above has been conducted on the basis of the nominal system. However, Figure 19 showed the discount factor for each year of the project lifetime, which computed based on Equation (9).  
The total NPC calculates by summing up the total discounted cash flows in each year of the project lifetime, as follows: include capital costs $25,880 + O&M costs $7075 -- salvage $3645, equal $29,810. This analysis can be extended to other cases in which the cost of the system depends on the elements and the size of the components in the system. Figure 20 summarized the total discounted costs occurring within the project lifetime for the hybrid SPV/WTG system.
## 3 Classification Method
### Feature Set
#### 3.2.1 GUSSS Ratio  
In our previous work [13; 14], we developed an effective and robust feature, named the GUSSS ratio, for recognizing sEMG patterns, which was then employed to detect simulated vocal dysfunction. The basic idea in GUSSS (Guided Under-Determined Source Signal Separation) is to identify a previously learned signature representing an entire class of patterns as it may be embedded in an observed sEMG source signal. In fact, the GUSSS method can measure the degree of presence of each signature in the observed source signal by successively injecting the signatures into the same source signal and computing a set of ratios. The ratios are a measurement of the statistical independence between the sought signature and the source signal. For example, if the classification problem involves \(c\) classes, the algorithm obtains an equal number of ratios \(r_{1},...,r_{c}\), each corresponding to the degree of independence of signature \(s_{i}\), representing class \(i\), of the source signal. Indeed, if the source signal contains the signature for class \(i\), the ratio \(r_{i}\) is expected to be the smallest one among all other ratios, indicating the statistical dependency of the source signal and the class \(i\).  
As already mentioned, the GUSSS ratio has been a robust and accurate feature in all of our previous work, and it was once again employed in this study.
## 3 Results
### The Impact of Communication Mechanisms on Latency and Energy Consumption  
Figure 7 shows the average energy consumption performance of the SC-MADDPG algorithm and the MADDPG algorithm during the training process. The energy consumption of UAVs and edge servers for task offloading is proportional to the CPU frequency. As can be seen from the figure, the SC-MADDPG algorithm, which incorporates UAV communication mechanisms, has lower average energy consumption per task than the original MADDPG algorithm under different training rounds.  
Figure 6: The trajectory of UAV flight.  
Figure 7: The average energy consumption of MADDPG and SC-MADDPG algorithms.  
Figure 8 shows that in the case of different numbers of UAVs, the average task delay of the SC-MADDPG algorithm proposed in this chapter is less than that of the original MADDPG algorithm, indicating that UAV communication has a significant effect on reducing task processing delay. In addition, as the number of UAVs increases, the task processing delay first decreases and then increases. The main reason is that the communication between UAVs can cover mobile devices more evenly, and tasks generated by mobile devices can be promptly processed by UAVs, thus reducing delay. However, when the number of UAVs exceeds a certain range, the increased number of communication instances between UAVs can delay task processing time, resulting in increased delay. As can be seen, when the number of UAVs is 4, the average delay of both algorithms is minimized.  
Figure 9 shows the average delay performance of the SC-MADDPG algorithm and the MADDPG algorithm during the training process. Incorporating UAV communication mechanisms in the MADDPG algorithm allows for more even coverage of tasks generated by devices, thereby reducing the transmission delay. As can be seen from the figure, as the model converges to the optimal policy, the average delay of the SC-MADDPG algorithm decreases to 0.27, saving time cost and improving the QoS of user devices compared to the MADDPG algorithm.
## Appendix A
\begin{table}
\begin{tabular}{c|c c|c c|c c|c c|c c} \hline \hline \multirow{2}{*}{**EMSR**} & \multicolumn{2}{c|}{**Pastures (ha)**} & \multicolumn{2}{c|}{**Perm. Crops (ha)**} & \multicolumn{2}{c|}{**Shrubs / herb. (ha)**} & \multicolumn{2}{c|}{**In. Wetlands (ha)**} & \multicolumn{2}{c|}{**Woodland (ha)**} \\  & _Burnt_ & _AoI_ & _Burnt_ & _AoI_ & _Burnt_ & _AoI_ & _Burnt_ & _AoI_ & _Burnt_ & _AoI_ \\ \hline
221\_01 & n.d. & n.d. & n.d. & n.d. & n.d. & n.d. & n.d. & n.d. & n.d. & n.d. \\
371\_01 & & & 25 & 267.9 & 381.8 & 936.3 & & & & \\ \hline
290\_03 & & 13.9 & & & 18.3 & 120.1 & & 14 & & \\
298\_02 & & & & & 7.9 & 112 & & & & \\
298\_06 & & & & & 52.2 & 1626.2 & & & & \\ \hline
248\_01 & & & & & 1642.4 & 8521.42 & & & 56.1 & 1989.9 \\
248\_03 & & & & & 674.7 & 2980.1 & & & 836 & 1565.6 \\
248\_04 & & & & & 437.4 & 2353.5 & & & 47.3 & 220.6 \\
248\_05 & & & & & 588.9 & 1513 & & & 166.7 & 2457.5 \\
368\_01 & & 1708.6 & & 2896.1 & 1471.3 & 15,130.6 & & & & \\ \hline
216\_01 & & & & & 796.1 & 924 & & & 860.9 & 1482.4 \\
216\_02 & & & & & 658.2 & 1834.5 & & & 172.8 & 643 \\
216\_04 & & & & & 99.7 & 821.6 & & & & 980.1 \\
216\_05 & & & & & 236.1 & & & 148.8 & 483.82 \\ \hline
365\_01 & & & 316.8 & 6695.8 & 880.2 & 8877.5 & & 38.3 & & \\
373\_01 & & & & & 366.3 & 918.3 & & & & \\ \hline
302\_01 & 199.4 & & 499.8 & 958 & 13,469.9 & & & & & \\
302\_06 & & & & & 391.9 & 960.4 & & & & \\
302\_07 & & & & & 83.6 & 2578.9 & & & & \\ \hline
250\_01 & & & 300 & & 642 & 1172.2 & 6596.2 & & & \\
372\_04 & n.d. & n.d. & n.d. & n.d. & n.d. & n.d. & n.d. & n.d. & n.d. & n.d. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Land use details for the AoIs considered in this work. For the sake of space, this table is partial, and continues from Table 2. It reports, in hectares: Pastures areas, Permanent crop lands, Shrubs or herbaceous vegetation areas, Inland wetlands areas, and Woodlands. For each land use type, the areas affected by fire are reported (Burned).
## 3 Methods
### Ensemble Kalman Filter
\(\overline{Y}\in\Re^{n\times N}\) is defined as the set matrix of augmented state vectors and \(\overline{Y}^{T}\in\Re^{n\times N}\) is the set disturbance matrix of state variables. As such, the analytical equation is:  
\[A^{a}=A+A^{\prime}\overline{Y}^{T}\overline{H}^{\prime T}(\overline{H} \overline{Y}^{\prime T}\overline{H}^{\prime T}+R)^{-1}(D-\overline{H}\overline {Y}) \tag{18}\]  
where \(\overline{H}\) is a new observation operator. The analytical equation can solve the data assimilation problem for which the observation operator is nonlinear. In this study, we calculated the background error covariance from albedo background and field observations. We calculated observation error covariance values from TM albedo and field observations over the time series described above. At each time point, we generated N random noises with 0 as the mean and 0.01 as the variance. N is the ensemble number, which affects estimation efficiency and accuracy: A larger N has a lower calculation efficiency and higher accuracy. We set the ensemble number to 100 and added random noise values to the background field values of the current date to obtain N new background field values [52]. Finally, we calculated the standard deviation between the N background field values and the ground field observations as the background field errors of the current date. Observation errors were simulated in the same way. Figure 3 shows the error setting for the background field, TM albedo, and field observation for the AU-DaP site in 2008. The \"background error\" and \"TM error\" labels represent simulated background errors and TM albedo errors, respectively. For regional use, the error of the area was set based on the error of the center pixel; a normal distribution noise with a mean of 0 and a variance of 0.01 was added for each pixel. Surface albedo is the only model state here, so the size of the model state vector n is equal to 1. We used the albedo value derived by the direct estimation approach as the albedo observation data, so the sum of the size of the model state vector n and the number of measurement equivalents added to the original model state vector was equal to 2. The simulated albedo was included in the state variables of the dynamic model at the given observation time. We emphasize the observation error covariance R here as per its significant influence on the A\({}^{\text{a}}\) value. In our experiments, all the errors were simulated at site AU-DaP.
## 3 Automatic Demagnetization Fault Diagnosis System Based on Three PNNs
### Design of the Automatic Demagnetization Fault Diagnosis System Using Three PNNs  
#### 3.2.1 Structure of the PNN  
The PNN is widely used in pattern recognition and fault diagnosis due to, firstly, the training of the network being easy and fast [22; 23]. Second, sparse samples are adequate for network performance. Third, it involves a single-pass network training stage without any iteration for adjusting weights. In addition, the PNN is easy to retrain with new data and adapts itself to architectural changes. Thus, the proposed system is based essentially on the use of three PNNs to achieve an automatic and intelligent demagnetization fault diagnosis system, which consists in automating the detection and the location of demagnetization faults in PMSMs. This section describes the design of the PNNs used as a tool for diagnosing the demagnetization faults in PMSMs.  
The architecture of the PNN is shown in Figure 11, and is composed of four layers: an input layer, pattern layer, summation layer, and output layer. The input layer receives the signal from DFISs and further transfers the data to the pattern layer. The number of neurons in the input layer is one. The pattern layer calculates the relationships between the input feature vector and the demagnetization fault modes to be able to locate correctly the demagnetized PMs. The number of neurons in the pattern layer equals the size of training samples. The summation layer simply sums the outputs of the pattern layer neuron belonging to its own demagnetization fault mode. The output layer indicates automatically  
Figure 10: The process for automatically diagnosing demagnetization faults using three PNNs.  
the demagnetization fault mode corresponding to the fault presented at its inputs. The number of neurons in the output layer equal the number of demagnetization fault modes.
# Phase Reversal Method for Damage Imaging in Composite Laminates Based on Data Fusion
## 3 Results
### Experimental Results  
To verify the numerical-simulation results, an experimental platform for damage detection of composite materials based on the phase-reversal method was constructed, as shown in Figure 9. The experimental platform included the orthogonal anisotropic carbon fiber composite laminated plate, which was used as a research object; the signal generator, which was used to generate the signal with a center frequency of 100 kHz; the signal amplifier, which was employed to improve the signal energy; and the Doppler laser vibrometer, which was used to measure the vibration signal of structure.  
The size of the experimental plate was 800 mm \(\times\) 800 mm, and its thickness was 1 mm. The information on material parameters was unknown, which confirmed the advantage of the proposed method, i.e., that it does not require prior information on material parameters. The damage forms of a composite plate structure usually include cracks, fiber fracture, debonding, and delamination. To facilitate the extraction of scattered signals and reduce the difficulty of subsequent signal processing, the damage was replaced by a circular magnet with a diameter of 10 mm, whose positional coordinates were 50 mm and 70 mm, and which was pasted on the front and back of the plate. The measurement points were two linear arrays on the \(x\)-axis and \(y\)-axis; the quantities and positions are given in Table 3.  
The signal containing the damage scattered wave measured by the Doppler laser vibrometer is shown in Figure 10, where the red circle indicates the scattered damage signal.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \(x\)**-Axis (Positive)** & \(y\)**-Axis (Positive)** \\ \hline Sensor location & 15–35 mm & 15–35 mm \\ Number of sensors & 21 & 21 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Location and number of the four measuring points.  
Figure 9: Schematic diagram of the experimental setup.  
According to the theory of the PRM, the correlation coefficient of the reconstructed damage scattering signal is taken as the imaging index. The accuracy of imaging matrices by PRM is improved through the combined addition and multiplication method of the correlation coefficient (CAMM). As displayed in Figure 11, the actual damage coordinates were 50 mm and 70 mm, and the damage location coordinates obtained by the PRM were 51 mm and 73 mm.  
The experimental results showed that the phase-reversal method could successfully realize damage imaging under the condition of unknown material parameters of the composite plate, and the damage-imaging process did not involve the dispersion relationship  
Figure 11: Damage-imaging result obtained by the PRM.  
Figure 10: (**a**) Received signals, including the incident and reflected waves on the \(x\)-axis. (**b**) Received signals, including the incident and reflected waves on the \(y\)-axis.  
and was not affected by the anisotropy. Thus, the phase-reversal method is suitable for damage detection in composite materials.