esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Investigation of Jet Angle Effect on Airfoil Stall Control
## 3 Results and Discussion
### Pitching Airfoil Simulations
#### 3.2.3 Estimate of Power Benefit with Jet Actuation  
Benefit of the current tangential jet for the dynamic stall is estimated. In this estimation, the power input for the generation of the tangential jet is computed, following [PERSON] et al. [19]: \(W_{jet}=\ln\left(\frac{P_{t}}{P_{\infty}}\right)\frac{RT_{\infty}H_{jet}}{2}\) where \(W_{jet}\) is the power required for the jet generation per unit span, \(P_{t}=290\) kPa is the total pressure in a compressor, \(P_{\infty}=56\) kPa is the current freestream pressure, \(T_{\infty}=304\) K is the current freestream temperature, \(\dot{m}_{jet}=0.24\) kg/(sm) is the mass flow rate per unit span, and \(R\) is the gas constant. A constant temperature jet supplied from a constant pressure compressor is used for this power estimation [19]. It is required to provide a power to counterbalance the aerodynamic drag of the pitching airfoil. The drag power per unit span \(W_{d}\) is computed using the formulation \(W_{d}=\int_{0}^{T_{p}}\frac{1}{2}\rho_{\infty}U_{\infty}^{3}c_{d}(\alpha)c \sin(\alpha)dt\) where \(T_{p}\) is the period of the pitch. It is expected to gain the lift power using the tangential jet, compared to the baseline. Similar to the drag power, the lift power is computed using the formulation \(W_{l}=\int_{0}^{T_{p}}\frac{1}{2}\rho_{\infty}U_{\infty}^{3}c_{d}(\alpha)c \cos(\alpha)dt\). Please note that the airfoil is projected to a plane normal to the force direction for each the drag and the lift power. The net power is defined as follows \(W_{net}=W_{l}-(W_{jet}+W_{d})\). For the baseline and the tangential jet actuation, the aforementioned powers are listed in Table 3.  
The benefit of the tangential jet for the dynamic stall is quantified as the net power increase from the baseline \(\Delta W_{net}=12\) kW/m. The power for generating the strong jet flow of \(c_{\mu}=0.06\) is significant, even higher than the power required for negating the drag in the baseline. The benefit of the tangential jet comes from the significant increase in the lift power which overcomes the additional power for the jet and the drag. It is expected because the cycle-averaged lift increases with the jet actuation (see Figure 18). If the jet actuation occurs only in a few right moments during the cycle, not throughout the whole cycle, then the jet benefit \(\Delta W_{net}\) could be maximized. Such pulsed jets are of interest in future studies.
# An Enhanced Auxiliary Information-Based EWMA-t Chart for Monitoring the Process Mean
### Aib-Ewma-t Control Charts  
Assume a corresponding auxiliary variable \(Y\) accompanies the quality characteristic \(X\) of interest. Let \((X,Y)\) follow a bivariate normally distributed process with means \((\mu_{X},\mu_{Y})\) and variances \((\sigma_{X}^{2},\sigma_{Y}^{2})\); \(\rho\) is the correlation between \(X\) and \(Y\), that is, \((X,Y)\sim N_{2}(\mu_{X},\mu_{Y},\sigma_{X}^{2},\sigma_{Y}^{2},\rho)\). Suppose \((X_{ij},Y_{ij})\), \(j=1,2,\ldots,n\), is a random sample of size \(n\) taken from the process at time \(i\), for \(i=1,2,\ldots\). Let \(\overline{X}_{i}=\sum_{j=1}^{n}X_{ij}/n\) and \(\overline{Y}_{i}=\sum_{j=1}^{n}Y_{ij}/n\) be the sample mean based on \((X_{i1},X_{i2},\ldots,X_{in})\) and \((Y_{i1},Y_{i2},\ldots,Y_{in})\), respectively. Then, following [PERSON] et al. [20], the regression estimator of the process mean \(\mu_{X}\) is given by:  
\[X_{i}^{*}=\overline{X}_{i}+\rho\bigg{(}\frac{\sigma_{X}}{\sigma_{Y}}\bigg{)}( \mu_{Y}-\overline{Y}_{i}) \tag{4}\]  
The mean and variance of \(X_{i}^{*}\) are:  
\[E(X_{i}^{*})=\mu_{X} \tag{5}\]  
\[Var(X_{i}^{*})=\frac{\sigma_{X}^{2}}{n}(1-\rho^{2}) \tag{6}\]Assume that the underlying process remains in an in-control state; then, the statistic \(T_{i}^{*}\) is defined by:  
\[T_{i}^{*}=\frac{\sqrt{n}(X_{i}^{*}-\mu_{X})}{S_{X,i}\sqrt{1-\rho^{2}}} \tag{7}\]  
As this is also a Student's \(t\)-distribution with \(n-1\) degrees of freedom, the AIB-EWMA-t statistic \(Z_{i}^{*}\) at time \(i\) can be defined as:  
\[Z_{i}^{*}=\lambda T_{i}^{*}+(1-\lambda)Z_{i-1}^{*} \tag{8}\]  
where \(\lambda\) is the smoothing constant satisfying \(0<\lambda\leq 1\). The initial value of \(Z_{i}^{*}\) is usually set to zero (i.e., \(Z_{i}^{*}=0\)). The asymptotic control limits for the well-known AIB-EWMA-t control chart based on \(Z_{i}^{*}\) are:  
\[\left\{\begin{array}{c}UCL=0+L_{ae}\sqrt{\frac{\lambda}{2-\lambda}\cdot\frac {n-1}{n-3}}\\ CL=0\\ LCL=0-L_{ae}\sqrt{\frac{\lambda}{2-\lambda}\cdot\frac{n-1}{n-3}}\end{array}\right. \tag{9}\]  
where \(L_{ae}\) is the control limit constant which is similar to that of the EWMA-t control chart. The AIB-EWMA-t control chart initiates an out-of-control signal whenever statistic \(Z_{i}^{*}>UCL\) or \(Z_{i}^{*}<LCL\); otherwise, the process remains in-control. For more details on the AIB-EWMA-t chart, see [PERSON] et al. [25].
## 3 Results
### Cooling  
Finally, we test the temperature effects on the NLI method with the NLI system of \(N=4\) in Section 3.4. Since the silica material of optical fibers can generate noise photons by the Spontaneous Raman Scattering (SpRS) process, the SFWM photon generation medium needs cooling to suppress SpRS. The lower the temperature, the less SpRS occurs [25; 26], but cooling the fiber system down to liquid nitrogen temperature is the best cost-effective way. When the optical fiber is cooled down, some properties of optical fibers, such as the zero GVD wavelength, are changed. For the DSF case, \(\lambda_{0,\text{DSF}}\) is changed by \(-4\) nm at liquid nitrogen temperature [26], and we assume that \(\lambda_{0,\text{SMF}}\) also shifts by \(-4\) nm as the DSF and SMF are made of silica. The dispersion slopes of DSF and SMF are not changed. Figure 6a,b are the theoretical predictions of the JSI at the signal/idler filter frequencies detuned by 400 GHz from the pump frequency, and (c), (d) are the measured JSI. Figure 6a,c are the cases at room temperature with \(\lambda_{p}\)= 1550.92 nm. The peak center is placed at around 399.5 GHz in the simulation results and 396 GHz in the experimental results. This peak center shifts if we cool down the temperature to liquid nitrogen temperature. \(\lambda_{0,\text{DSF}}\) and \(\lambda_{0,\text{SMF}}\) are changed from 1555.5 nm to 1551.5 nm and from 1314 nm to 1310 nm, respectively, without changing the dispersion slope. As seen in Figure 6b,d, the peak center shifts to around 393.5 GHz (\(-6\) GHz changed) in the simulation and 390.5 GHz (\(-5.5\) GHz) in the experiment, respectively. As the shifted island due to cooling is within the signal/idler filter bandwidth and the shape of JSI is almost identical, the spectral purities in the simulation and experiment have almost no change.
## 4 Experimental Results
### Results Regarding Date and Off-nadir Angle  
Figure 9 presents the relationship between the obtained results and the off-nadir viewing angle and the time of acquisition. The connection is only examined between the RMSEs at ICPs of the two GCP extraction methods. Although the figure shows the results in ascending off-nadir viewing angle sequence, the RMSEs do not reflect this and suggest that there is no correlation between the two parameters. The same conclusion can be drawn for the acquisition dates.
# Machine Learning Enabled Performance Prediction Model for Massive-MIMO HetNet System
###### Abstract  
To support upcoming novel applications, fifth generation (5G) and beyond 5G (B5G) wireless networks are being propelled to deploy an ultra-dense network with an ultra-high spectral efficiency using the combination of heterogeneous network (HetNet) solutions and massive Multiple Input Multiple Output (MIMO). As the deployment of massive MIMO HetNet systems involves a high capital expenditure, network service providers need a precise performance analysis before investment. The performance of such networks is limited because of presence of inter-cell and inter-tier interferences. The conventional analytic approach to model the performance of such networks is not trivial, as the performance is a stochastic function of many network parameters. This paper proposes a machine learning (ML) approach to predict the network performance of a massive MIMO HetNet system considering a multi-cell scenario. This paper considers a two-tier network in which the base stations of each tier are equipped with massive MIMO systems working in a sub 6-GHz band. The coverage probability (CP) and area spectral efficiency (ASE) are considered to be the network performance metrics that quantify the reliability and achievable rate in the network, respectively. Here, an ML model is inferred to predict the numerical values of the performance metrics for an arbitrary network configuration. In the process of practical deployments of future networks, the use of this model could be very valuable.  
5G; B5G wireless networks; massive MIMO; HetNet; machine learning; coverage probability; area spectral density +
Footnote †: journal: Sensors  
**Keywords:** 2021, 21, 800. [[https://doi.org/10.3390/s21030800](https://doi.org/10.3390/s21030800)]([https://doi.org/10.3390/s21030800](https://doi.org/10.3390/s21030800))
###### Abstract  
Non-invasive near-infrared (NIR) human glucose sensors have attracted great interest in managing diabetes mellitus and those with complex sensing backgrounds due to glucose absorption spectrum overlap. Here, we propose a non-invasive and highly accurate multi-wavelength light NIR glucose sensor using a multilevel metric learning-back propagation network, i.e., \"HMML-BP\", based on the narrowband multi-wavelength light NIR system. Our human glucose sensing method combines the advantages of this system and an HMML-BP network. The latter is composed of multilevel metric learning modules and a BP network to predict blood glucose concentrations. The narrowband multi-wavelength light NIR sensing system consists of six-channel NIR filters with center wavelengths of 850 nm, 940 nm, 1300 nm, 1400 nm, 1550 nm, and 1650 nm and a spectral resolution below 12 nm. The six NIR channels measured were first entered into the MML modules to build 3D multi-wavelength light data. Next, 3D multi-wavelength light data were optimized by stochastic neighbor embedding. Diffusion maps and factor analysis algorithms were used to retain effective NIR information. Finally, the optimized data were utilized as the BP network input to predict blood glucose concentrations. The predicted results showed that the factor analysis algorithm had the best performance in our HMML-BP network and that all the predicted glucose values fell into region A, with a mean absolute relative difference of 9.98%, meeting the requirements of daily glucose monitoring. Our blood glucose sensing method provides a new way of utilizing multi-wavelength light and hyperspectral information for smart human glucose monitoring.  
glucose sensing; metric learning; back propagation network +
Footnote †: journal: Applied Sciences  
Article
# Study on the Causes and Control Measures of Mg-Al Spinel Inclusions in U75V Heavy Rail Steel
## 3 Results and Discussion
### Analysis of Typical Inclusions in Billet and Rail  
The morphology and composition of typical inclusions in U75V heavy rail steel billets and rails are shown in Figure 11. The inclusion in the billet was large, it contained spinel and CaO-SiO\({}_{2}\)-Al\({}_{2}\)O\({}_{3}\) was wrapped outside, as shown in Figure 11. The CaO-SiO\({}_{2}\)-Al\({}_{2}\)O\({}_{3}\) phase with its low melting point and high plasticity was prone to deformation in the rolling process, while the MgO-Al\({}_{2}\)O\({}_{3}\) phase with its high melting point exhibited high hardness and poor deformation performance. This is because of the difference in the properties of the inclusion phases. The non-deformation of the rolling process resulted in the brittle fracture of the inclusions, resulting in bare spinel inclusions in the steel matrix. Figure 11 shows typical inclusions observed along the rolling direction.  
FactSage7.3 thermodynamic software was used to calculate the precipitation transformation of inclusions in the cooling process, and the calculation results were shown in Figure 12. The inclusions under this composition precipitated the MgO-Al\({}_{2}\)O\({}_{3}\) phase in the cooling process, and the precipitation temperature was related to the composition of the initial inclusion. Compared with the results shown in Figure 10, it was found that the precipitation temperature of the spinel phase gradually increased with increases in the Al\({}_{2}\)O\({}_{3}\) and MgO content in the inclusion, and some inclusions began to precipitate the MgO-Al\({}_{2}\)O\({}_{3}\) phase at the temperature of steel making. Therefore, to control the precipitation of MgO-Al\({}_{2}\)O\({}_{3}\) in the smelting process, the content of Al\({}_{2}\)O\({}_{3}\) and MgO in the inclusion should be reduced. Additionally, this is an effective method for controlling the precipitation of spinel inclusions in heavy rail steel to prevent the secondary oxidation of molten steel during smelting and to reduce strong slag reactions and corrosion resistance in VD refining.  
Figure 11: Morphology and composition of typical inclusions in billet (**a**) and rail (**b**).  
Figure 12: Transition of high Al\({}_{2}\)O\({}_{3}\) low CaO inclusions during cooling.
# Interference of Spread-Spectrum EMI and Digital Data Links under Narrowband Resonant Coupling
## 5 Impact of Spread Spectrum Modulations in a 4-QAM Channel under Resonant EMI Coupling
### Test Setup
Figure 12: Bit error rate of a communication channel corrupted by SS-modulated EMI under strongly resonant coupling (\(Q=100\)) vs. EMI normalized r.m.s. amplitude at different modulation depth \(\delta\).
## 3 Result and Discussion
### Identification of Fungal Growth Days by Band Math  
In both cases, for _A. flavus_ and _A. parasiticus_, mycelia started to appear clearly on day 2, which were smooth and white, while conidia on _A. parasiticus_ appeared earlier than _A. flavus_. On day 4, chartreuse conidia of _A. parasiticus_ emerged. Additionally, when the conidia increased in number and matured, they became darker in color. For _A. flavus_, a few conidia formed on day 5, and their conidia were not as obvious as those of _A. parasiticus_. In order to further discriminate fungi inoculated for different days, average spectra of all the fungi samples in each day of _A. flavus_ and _A. parasiticus_ for different incubation days were calculated and are shown in Figure 2a,b.  
For the two types of fungi, spectra of day 1 had conspicuous absorption feature at around 446 nm, while spectra of day 2-6 had absorbance peak at around 460 nm. As [PERSON] mentioned, spectral differences in VIS region are associated color changes [31]. Thus, these peak shift changes may be caused by the appearance of mycelia. The slope of day 1 between 446 and 460 nm was positive, whereas that of other days was negative for both _A. flavus_ and _A. parasiticus_. One replicate of Petri dish from day 2 to 6 of _A. flavus_ and _A. parasiticus_ was used to analyze the feasibility of the identification of fungal growth days by band math. For _A. flavus_, the band ratio image of 460/446 nm was shown in Figure 3. The average band ratio was 1.826 for day 1 and 0.958 for other days. Thus, a selected value (1.20) was used as a threshold for classifying _A. flavus_ on day 1 and other days. In a similar way, the band ratio and the selected threshold (1.20) were used to distinguish _A. parasiticus_ incubated one day from other incubation time. Using this method, the fungi on day 1 can be identified with 100% accuracy.  
For _A. flavus_, spectra looked closely similar after 460 nm. For _A. parasiticus_, spectra on day 6 showed low reflectance responses in the spectral range from 520 to 700 nm, perhaps because with the mature of conidia, its color turned dark. The image of _A. parasiticus_ at 520 nm was shown in Figure 4. Average reflectance response for day 6 at 520 nm was 0.56; it was 0.736 for other days. Thus, a threshold (0.65) can be used to divide _A. parasiticus_ on day 6 and other days.  
Figure 3: Band ratio image (460/446 nm) of _A. flavus_.  
Figure 2: (**a**) Average spectra of _A. flavus_ and (**b**) average spectra of _A. parasiticus_.  
However, using images at specific bands can only identify _A. flavus_ and _A. parasiticus_ on day 1 and _A. parasiticus_ on day 6, as fungi in those days contained distinct change compared with other days. Analysis based on full spectral region should be applied to identify the incubation time at which fungi had similar appearance.
## 3 System Resource Optimization Algorithm
### State Space  
We define the state space \(S\) as \(S=M\times L\times C\), where \(M\) is the task phase, \(L\) is the location of the UAV, and \(C\) is the network of the terminal connected to the UAV.  
Task phase \(M\) is defined as follows:  
\[M=\{0,1,2,3,4,5,6,7,8\}, \tag{34}\]  
where m (M) denotes the phase of the task. If no task exists, then m = 0. m = 1 and m = 2 refer to the situation immediately after the task occurs and when the task is in the buffer (i.e., when task processing is not in progress), respectively. Meanwhile, m = 3, m = 4, and m = 5 represent the situations wherein the task is processed in the terminal equipment, the UAV, and the G-MEC, respectively. Additionally,m = 6, m = 7, and m = 8 represent situations immediately after the task processing is completed at the central cloud, the UAV, and G-MEC, respectively.  
\(L\) can be constructed using a map segmentation technique [25]. The UAV moves randomly within the area of the segmented position. \(L\) is described as follows:  
\[L=\big{\{}L_{1},L_{2},L_{3},\cdots,L_{N_{L}}\big{\}}, \tag{35}\]  
where \(N_{L}\) represents the total number of locations the UAV can locate. \(L_{i}\) represents a vector of adjacent cases, which is given as following:  
\[L_{i}=[l_{i}^{1},l_{i}^{2},l_{i}^{3},\cdots l_{i}^{N_{L}}]. \tag{36}\]  
If position \(i\) is adjacent to position \(j\), then \(l_{i}^{j}\) is 1; otherwise, \(l_{i}^{j}\) is 0.  
Meanwhile, network connection vector C is described as follows:  
\[C=\big{\{}C_{1},C_{2},C_{3},\cdots,C_{N_{C}}\big{\}}, \tag{37}\]  
where \(N_{C}\) represents the total number of \(k\) heterogeneous network connections and \(N_{C}=2^{k}\). \(C_{\chi}\) represents the \(\chi\)th possible network connection given by \(C_{\chi}=[c_{1},\,c_{2},\,c_{3},\,\ldots,\,c_{k}]\). A connection is indicated by \(c_{\zeta}=1\), whereas \(c_{\zeta}=0\) indicates a lack of connections.
# Cost-Benefit Analysis of Small-Scale Rooftop PV Systems: The Case of Dragotin, Croatia
## 2 Materials and Methods
### Facility and Equipment  
The PV power plant is placed on the roof of a courtyard building that stands at 35\({}^{\circ}\) facing south without deviation. The roof is covered with trapezoidal sheet metal and there is no shading of the building. The courtyard building was chosen because of its south orientation and the ideal angle for placing the PV module, which is not the case with the roof of the house facing east/west. The following equipment was selected for the 3.6 kW power plant:  
Solar module Jeto 450 W JT4505 GH;  
The power converter HUAWEI SUN2000L-3.68 KTL;  
Installation equipment (roof racks, cables, connectors).  
The technical characteristics of the selected equipment are defined in Tables 2 and 3.  
Required number of modules for the 3.58 kW system:  
\[n=\frac{P_{PV}}{P_{MPP}}=\frac{3.58}{0.45}=8 \tag{3}\]  
Required surface for mounting the PV module:  
\[A_{PV}=A_{M}\times n=2.21\times 8=17.68\ \mathrm{m}^{2} \tag{4}\]  
Due to the relatively small number of modules and the shape of the roof, the modules are installed in one row. A compatibility check determined that the selected PV modules are compatible with the selected inverter. The roof area on which the PV modules is installed is 50 m\({}^{2}\) and meets the required area for the installation of the PV module, which is 17.68 m\({}^{2}\). A 3-D model of the building is shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **Label** & **Value** \\ \hline Efficiency & \(\eta_{M}\) & 20.4\% \\ Temperature coefficient of voltage change & \(\beta\) & \(-0.3\%/^{\circ}\)C \\ Temperature range of the module application & \(t_{min}\sim t_{max}\) & \(-40\)\({}^{\circ}\)C do 85 \({}^{\circ}\)C \\ Module area & \(A_{M}\) & 2.21 m\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Technical characteristics of the Jetion module 450 W JT4505 GH [28].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Feature** & **Value** \\ \hline Maximum input power & 4968 W \\ Rated output power & 3680 W \\ Output voltage range & 220–240 V \\ Maximum efficiency & 98.6\% \\ European efficiency & 97.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Technical characteristics of inverters HUAWEI SUN2000L-3.68 KTL [29].
# Mixed Reality Enhanced User Interactive Path Planning for Omnidirectional Mobile Robot
## 5 The Description of VFH* Algorithm
### The VFH* Algorithm  
The VFH* algorithm can be regarded as a path planning algorithm combining the VFH+ local obstacle avoidance algorithm and the A* path planning algorithm. The VFH+ method determines the forward direction of the robot through the following four steps [36]:  
1. Generating the polar histogram: The VFH+ algorithm divides the active region of the current robot position into multiple sectors and calculates the obstacle density in each sector. Then the density of each sector is arranged into histogram according to the sector number.
2. Binarization polar histogram: One must select the appropriate threshold according to the actual situation, and binarize the histogram generated in the previous step. Sectors above the threshold are set as impassable areas, while sectors below the threshold are set as passable areas.
3. Mask polar histogram: Considering the kinematics and the dynamics characteristics of the robot, the current inaccessible sectors are set as the impassable areas.  
Determining the direction of motion: The passable areas in the polar histogram are used as the candidate direction; the cost is calculated according to the cost function; and the cost of the passable area is sorted. The commonly used cost function is shown as follows:  
\[g(c)=\mu_{1}\cdot\Delta(c,k_{t})+\mu_{2}\cdot\Delta(c,\frac{\theta}{\alpha})+ \mu_{3}\cdot\Delta(c,k_{p}) \tag{12}\]  
where \(c\) denote the candidate direction, and \(g(c)\) is the cost value of the direction. \(\mu_{1}\), \(\mu_{2}\), and \(\mu_{3}\) are three parameters that we need to determine according to the actual situation. \(\Delta(c,k_{t})\) is the absolute difference between the candidate direction \(c\) and the target direction \(k_{t}\). \(\Delta(c,\frac{\theta}{\alpha})\) represents the associated with the difference between the candidate direction \(c\) and the robot's orientation \(\frac{\theta}{\alpha}\). \(\Delta(c,k_{p})\) represents the difference between the candidate direction \(c\) and the previous direction \(k_{p}\).  
Finally, the passable area with the lowest cost is selected as the forward direction of the robot. Since VFH+ only locally plans in real time, global optimization cannot be guaranteed. Therefore, based on VFH+ and A* algorithms, [PERSON] and [PERSON] proposed the VFH* algorithm. The A* algorithm is the most effective direct search method for the shortest path in static road network [16]. The VFH* algorithm uses the VFH+ algorithm to predict several future states of the trajectory and form a path tree. Then, the A* algorithm is used to search the path tree, to find the global optimal plan as the next movement direction of the robot's current position.  
In this paper, we modified the original VFH* algorithm via the threshold setting, candidate direction selection, and cost function to make it more suitable for the scenario of this paper. When the mobile robot deals with the virtual obstacles suddenly added to the map, it can have a better obstacle avoidance effect.