esa-sceva/satcom-chunk-collection huggingface dataset:
# Model Updating Using Frequency Response Functions Based on Sherman-Morrison Formula
## 5 Conclusions  
A model updating method using FRFs based on Sherman-Morrison sensitivity is proposed in this paper. The sensitivity of FRFs is calculated using Sherman-Morrison formula which avoids recalculation of FE dynamic analysis. The simulation examples of truss and solar wing and the experiment of aluminum solar panel frame are implemented to verify the accuracy of the proposed method. The following conclusions can be drawn:  
1. Only the initial FRFs and parameter perturbations are employed for the acquisition of sensitivity, which avoids recalculation of FE dynamic analysis, and improves the efficiency of analysis and  
Figure 18: Comparison of MAC before and after model updating: (**a**) Before updating; (**b**) After updating.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Mode No.** & **Experimental** & **Before Updating** & **After Updating** \\ \hline
1 & & \\
2 & & \\
3 & & \\
4 & & \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison of Modal Shapes Before and After Model Updating.  
calculation at the same time. Only a small amount of FRFs data is needed to complete the model updating process.
2. With the effect of 5% random white noise, the parameter error after updating is within \(\pm\)1.00%, which shows that the proposed method has a certain anti-noise capability. The experiment study shows that the proposed method can effectively identify the structural parameters.
3. Selecting appropriate frequency range and points can effectively improve the stability and efficiency of model updating. In addition, the method can consider multiple parameters simultaneously and has great potential for engineering application.  
Conceptualization, D.J.; Funding acquisition, D.J.; Methodology, R.Z. and Z.C.; Resources, Y.L.; Software, R.Z.; Validation, Y.W. and Z.C.; Writing--original draft, Y.W.; Writing--review & editing, R.Z., Z.C., Y.L. and D.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 11602112), the Postgraduate Research and Practice Innovation Program of Jiangsu Province (KYCX19_0062), the Qing Lan Project, and the Scientific Research Foundation of the Graduate School of Southeast University.  
The authors declare that there is no conflict of interest regarding the publication of this paper.
## 3 Performance Optimization Using Genetic Algorithm
### Optimization Procedure  
In this paper, the genetic algorithm (GA) will be used to optimize the solution. Figure 2 presents the flowchart of the optimization algorithm, and the detailed steps are presented as follows:  
Begin  
_Step1:_ Initialize GA parameters; the population size is set to 20, the cross probability is 0.8, the mutation probability is 0.2, the max iteration is 200, and the elitist preservation proportion is 0.1.  
_Step2:_ Define the optimization fitness function \(F=1/G\) and the range of the design parameters:  
\[l_{d}\in[0.2,\;0.4]\;\text{m},k_{d}\in[5000,\;10,000]\;\text{N}/\text{m}, \varphi_{d0}\in\left[\frac{\pi}{6},\frac{\pi}{3}\right],a\in[0.1,\;0.4]\;\text {m},b\in[0.1,\;0.3]\;\text{m}\]  
_Step3:_ Generate a random initial population and calculate the initial fitness of all individuals.  
_Step4:_ Initialize the count number \(gen=1\).  
_Step5:_ Perform selection, crossover, and mutation operations.  
_Step6:_ Update the count number \(gen=gen+1\).  
_Step7:_ Go back to step 4 until \(gen=200\). When \(gen=200\), go to _Step 8_.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Description** & **Values** \\ \hline \(m_{u}\) & Control object mass & 2 kg \\ \(l_{xx}\) & Moment of inertia about axis \(x\) of \(m_{u}\) & 1 kg\(\cdot\)m\({}^{2}\) \\ \(l_{yy}\) & Moment of inertia about axis \(y\) of \(m_{u}\) & 1 kg\(\cdot\)m\({}^{2}\) \\ \(L_{u}\) & Length of \(m_{u}\) & 0.1 \\ \(B_{u}\) & Width of \(m_{u}\) & 0.1 m \\ \(k_{u}\) & Stiffness of the linear isolator & 1000 N/m \\ \(c_{u}\) & Damping of the linear isolator & 1 N\(\cdot\)s/m \\ \(m_{d}\) & Intermediate mass & 10 kg \\ \(l_{dx}\) & Moment of inertia about axis \(x\) of \(m_{d}\) & 10 kg\(\cdot\)m\({}^{2}\) \\ \(l_{dy}\) & Moment of inertia about axis \(y\) of \(m_{d}\) & 8 kg\(\cdot\)m\({}^{2}\) \\ \(L_{x}\) & Distance between bio – inspired isolators along axis \(y\) & 0.5 m \\ \(L_{y}\) & Distance between bio – inspired isolators along axis \(x\) & 0.4 m \\ \(c_{d}\) & Damping of the bio-inspired isolator & 5 N\(\cdot\)s/m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters for BI-DSVI.
## 3 Results and Discussion
### Lateral Deformation of Specimens  
The lateral deflection curves of each specimen under different load levels are shown in Figure 9. At the beginning of loading, the growth rate of the lateral deflection of each specimen was slow and accelerated when the load was increased to 0.8 \(N_{u}\). The mid-span deflection of the specimen was always at maximum during loading. The lateral deflections of the specimens were symmetrically distributed, with the high center of the column as the boundary. The column height lateral deflection curves of the compression columns were all similar to the sinusoidal function curves. The lateral deformation curve of the eccentric compression specimen conformed to a sinusoidal half-wave curve.  
Observing contrasting specimen Z1 and reinforced specimen Z2-1 in Figure 9, the lateral deflection of the specimen grew at a slower rate at the beginning of loading. When the load was increased to 0.8 \(N_{u}\), the lateral deflection deformation of the specimen steeply  
Figure 8: Span deflection curves.  
increased, and the mid-span deflection of reinforced specimen Z2-1 was larger than that of contrasting specimen Z1. This indicates that, after the core concrete was crushed, the peripheral reinforcement layer UHPC was gradually compacted, and the specimen gained load-bearing capacity again, increasing the ductility and ultimate load bearing capacity of the reinforced specimen.
## 5 Conclusions  
In this paper, aiming to compensate for the intermodulation components brought by adjacent strong signals, we offer the best delay searching-based nonlinearity mitigation method to enhance the SFDR of the RF front-end and improve the reliability of spectrum sensing in CR-WSN. To summarize, the advantages of the proposed method are as follows:  
1. There are plenty of non-ideal linear characteristic modules bringing in nonlinear distortions in the RF front-end of a sensor. Moreover, when the group delay distortion coming from the time delay electronic components joins with nonlinearities, it is necessary to design this blind identification criterion of nonlinearity mitigation at the best delay. Here, the best delay means the sampling interval between the output of the nonlinear behavior model and the distorted signal which corresponds to the minimal power residual after optimization.
2. Based on the proposed identification criterion, the mitigation architecture with best delay searching is presented. In this architecture, an odd-order generalized Hammerstein model is utilized as the nonlinear behavior model to compensate for the intermodulation components of the strong signals. Additionally, a flexible band-split method with low complexity is provided in the frequency domain to save both amplitudes and phases contained in the original signals. Finally, to solve the system of linear equations in the complex field which, in most cases, is ill-conditioned, the WII method is employed.  
In conclusion, the proposed method achieves satisfactory results when tested both on the simulation signals and signals from real-world measurements. If applying it to the RF front-end in the sensor, the performance of spectrum sensing can be improved predictably. Our next stage is to find a better solution for modeling the group delay distortion instead of using only sampling interval changes. This further development is beyond of the main scope of this paper, but offers an interesting and relevant topic for future studies.  
This work was supported by the National Natural Science Foundation of China (Grant No. 11475067), the Innovative Research Program of Huazhong University of Science and Technology (Grant No. 2015 ZDTD017, Grant No. 2017 KFXKJ012), the Experimental Apparatus Research Project of Wuhan Pulsed High Magnetic Field Center (Grant No. 2015 KF17) and Joint Fund for Equipment Pre-research of Ministry of Education of China (Grant No. 6141A02022308).  
**Author Contributions:** [PERSON] proposed the original algorithm and wrote this paper; [PERSON], [PERSON] and [PERSON] revisited the paper and supervised the entire process; [PERSON] gave some valuable suggestions; [PERSON] offered the RF-front and for testing the proposed method performance of signals from real-world measurement and suggested some constructive advises during revision.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 11: The constellations of the 16 QAM signals in the polluted regions: (**a**) distorted weak 16 QAM; (**b**) weak 16 QAM after mitigation without delay; and (**c**) weak 16 QAM after mitigation at the best delay.
# AOA-Based Three-Dimensional Multi-Target Localization in Industrial WSNs for LOS Conditions
## 6 Testbed Development and Field Experiments
### Experiment Scenario  
The localization experiment was performed on a square. The location of the Rx (emulate an AN) was fixed, while the two Tx's were mounted on two tripods and moved along a line. The experiment setup is illustrated in Figure 10c. The AOA of the LOS paths from the two Tx's were measured when they were placed at five positions. The azimuth AOAs were \(\phi_{i,k,1}=\{80^{\circ},85^{\circ},90^{\circ},95^{\circ},100^{\circ}\}\) and the elevation AOAs were always \(\theta_{i,k,1}=90^{\circ}\). The threshold for the ML iteration convergence is still \(\xi=10^{-3}\) degree.
available [6; 14]. In this regard, activation energies of diffusion are useful for the prediction of the migration at elevated temperatures.  
In some cases, PS is used as a functional barrier in order to reduce the migration of polymer additives or dyes [15; 16]. Prediction of barrier properties towards organic compounds also needs knowledge about the diffusion behavior of PS. In addition, prediction of diffusion coefficients might be useful for the evaluation of post-consumer recyclates for reuse in food packaging applications as well as in cleaning efficiency simulation of the corresponding recycling processes. This had been successfully shown for polyethylene terephthalate (PET) [17; 18].  
To the knowledge of the author, systematic studies on diffusion coefficients \(\mathrm{D_{P}}\) and activation energies of diffusion \(\mathrm{E_{A}}\) for GPPS and HIPS are not available in the scientific literature. Most of the diffusion coefficients available in the scientific literature are derived from migration experiments in food or food simulants. As mentioned above, GPPS is not suitable for fatty food which interacts with the polymer, resulting in increasing migration and consequently, higher diffusion coefficients. HIPS might also show strong interactions with food simulants. Food simulants like ethanol/water mixtures, \(n\)-heptane or _iso_-octane show strong interactions with polystyrene leading to an over-estimation of the migration which results into too high diffusion coefficients due to swelling and stress cracks compared to the pure diffusion of organic molecules. On the other hand, most of the typical applied foods do not show such strong interactions with PS. Therefore, diffusion coefficients based on migration measurements into food simulants will lead to prediction parameters which are too conservative. In addition, most of the diffusion coefficients which are used for the prediction models are derived from single point measurements, e.g., after storage for 10 d at 40 \({}^{\circ}\)C or 60 \({}^{\circ}\)C [9]. From single point measurements, it is not possible to show if the diffusion process is following Fickian laws of diffusion. Parametrization of prediction parameters based on migration kinetics appears to be a more suitable approach for the determination of diffusion coefficients in PS or in polymers in general.  
The aim of this study was to determine the diffusion coefficients of organic molecules in GPPS and HIPS over a broad temperature range below and above the glass transition temperature of 100 \({}^{\circ}\)C. Diffusion coefficients were determined from desorption kinetics into the gas phase using spiked GPPS and HIPS sheets as well as from permeation kinetics through a thin GPPS film. From the temperature dependency of the diffusion coefficients the activation energies of diffusion \(\mathrm{E_{A}}\) and the pre-exponential factor \(\mathrm{D_{0}}\) were determined. From the results, parameters for the prediction of diffusion coefficients for organic molecules in GPPS and HIPS below and above the glass transition temperature were derived.
## 2 Wastewater Treatment by Microalgae
### Agricultural Wastewater  
#### 2.2.1 Physicochemical Properties  
A potential alternative growing medium for microalgae has been presented as the wastewater produced by agricultural activities. The properties of this type of wastewater are determined by the effluent's source. There are four primary sources of agricultural wastewater (Figure 3), which are livestock, aquaculture, digested agricultural waste and drainage agricultural wastewater [55]. Organic carbons, volatile fatty acids, nutrients and metal ions are among the substances found in these sources [55]. Table 5 summarizes the physicochemical properties of agricultural wastewater used as a growth medium for microalgae that have been found in the literature. As described in this table, raw poultry litter extract had a pH of 7.45, chemical oxygen demand of 482.2 mg L\({}^{-1}\), 13.25 mg L\({}^{-1}\) of NO\({}_{3}\)-N and 108 mg L\({}^{-1}\) of NH\({}_{4}\)-N [42]. This composition is significantly different from anaerobi  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algae Used** & **Wastewater Type** & \begin{tabular}{c} **Growth Rate or** \\ **Volumetric** \\ **Productivity** \\ \end{tabular}  & \ \begin{tabular}{c} **Final Biomass** \\ **Concentration** \\ \end{tabular}  & \begin{tabular}{c} **Production of Target** \\ **Metabolites** \\ \end{tabular}  & **References** \\ \hline \multirow{3}{*}{_Desmodesmus_ sp.} & Municipal & & Proteins: 97\%; & \\  & (Ultrasound & nr & 75 g L\({}^{-1}\) & Carbohydrates: 89\%; & [52] \\  & pretreatment) & & Lipids: 73\%; & \\  & Municipal (Ozone & nr & 25 g L\({}^{-1}\) & Carbohydrates: 85\%; & \\  & pretreatment) & nr & 25 g L\({}^{-1}\) & Lipids: 48\%; Proteins: 25\%; & \\  & Untreated Municipal & nr & nr & Lipids: 3.8\%; Proteins: \\  & \  
cally digested poultry litter wastewater, which is characterized by a high concentration of ammonium (2000 mg L\({}^{-1}\) to 3000 mg L\({}^{-1}\) of NH\({}_{4}\)-N) and total nitrogen (2900 mg L\({}^{-1}\) to 3200 mg L\({}^{-1}\) of TN) [56]. Swine wastewater contained a high concentration of chemical oxygen demand (419.88 mg L\({}^{-1}\) to 85,600 mg L\({}^{-1}\)), total nitrogen (163.40 mg L\({}^{-1}\) to 5685 mg L\({}^{-1}\)) and total phosphate (26.2 mg L\({}^{-1}\) to 284 mg L\({}^{-1}\)), and the pH was reported at 6.83-8.1 [30; 57; 58; 59; 60; 61; 62]. The mass cultivation of fish necessitates a large volume of freshwater and produces a large amount of aquaculture wastewater [5]. Bioremediation of aquaculture wastewater by microalgae was reported in the literature [63; 60; 64; 65]. The pH was at 7.82-8.5, COD was at 30.30 mg L\({}^{-1}\) to 367.39 mg L\({}^{-1}\), TN was at 4.12-60 mg L\({}^{-1}\) and the TP was at 0.16 mg L\({}^{-1}\) to 6.8 mg L\({}^{-1}\) for aquaculture wastewater physicochemical composition [63; 60; 64; 65]. In addition, Table 5 showed that agricultural wastewater contains a high content of suspended particles (32.15 mg L\({}^{-1}\) to 32,951.5 mg L\({}^{-1}\)) and different metal ions such as Zn, Cu, Mg, Ca and K, with concentrations depending on the wastewater source. Figure 3: Agricultural wastewater types.
## 3 Results and Discussion
### PPF Model's Results
#### 3.1.2 FNN Model  
As explained in Section 2.1.2, when a FFNN forecaster is developed, the following set of parameters must be fit: the model's number of layers, each layer's number of neurons, each layer's neurons' activation functions and iterative training algorithm (see Figure 1). With regard to the training step's algorithms, a review of literature [15; 45; 53] agreed on the Levenberg-Marquardt (LM) algorithm being the most suitable choice for developing their forecasters, so the LM training algorithm was used in this study based on those previous studies' accurate results. Moreover, [PERSON] et al. [57] concluded through the development of several ANN structures that two hidden layers structures do not necessarily improve forecasters' accuracy and increases the possibility of getting stuck in a local minima, so a single hidden layer PPF structure was developed in our study. Although there are different activation functions that can be chosen for the neurons in each layer of the forecaster [52], in this forecaster, we chose the configuration usually suggested in the literature: a logarithmic sigmoid transfer function in the hidden layer and a linear transfer function in the output layer [42; 43; 44; 45]. Therefore, the last parameter that needed to be select was the hidden layer's number of neurons.  
Related literature shows that the only available methodology for fixing the number of neurons in each layer relies on a sensitivity analysis [42; 43; 44; 45]. In addition, the use of MATLAB(r) and its random initialization in the training step increases the risk of non-convergence. Therefore, to reduce this risk, each analyzed structure was repeated five times and the results from the training and validation steps were averaged. To fix the FFNN's hidden layer's number of neurons, the RMSE index was computed; then, the R\({}^{2}\) index was calculated for the best structure. Table 2 shows the averaged results for the FFNN structures analyzed.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**RMSE Train. (MW)** & **RMSE Val. (MW)** & **R\({}^{2}\) Train. (-)** & **R\({}^{2}\) Val. (-)** \\ \hline
0.1254 & 0.1259 & 0.9897 & 0.9889 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Computed RMSE and R\({}^{2}\) indexes’ values for the persistence model.  
The results in Table 2 suggest that there is little variation between analyzed structures' results when the hidden layer's neurons were modified. Based on the fact that forecasters with lower RMSEs are more accurate, the 10-neuron structure was the most accurate structure for the training database, whereas the 15-neuron structure was the best with the validation database. Taking into account that our goal was to maximize the forecaster's generalization capacity, i.e., obtain the most accurate predictions through previously unseen values, we chose to compare the 15-neuron structure against the results from the other PPFs. Table 3 summarizes 15-neuron configuration's error metrics and their improvement in percentage against the benchmark model.
# Regional Adaptability Analysis of Solar Roof Utilization Technologies in China
## 3 Case Analysis  
### Basic Information of the Case  
A case analysis of an office building was carried out to verify the findings of the paper. The office building in Beijing is a slab-type building. It includes eight floors, with each floor measuring a height of 3 m. The roof area is 88 m \(\times\) 22 m and the shape coefficient is 0.11.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Zones** & **Name of City** & **Solar Thermal** & **Solar PV** \\ \hline A & Shanghai & 2.45 & 2.0 \\ B & Chongqing & 1.88 & 0.83 \\ C & Guangzhou & 2.29 & 1.79 \\ D & Kunming & 2.64 & 1.76 \\ E & Lijiang & 2.79 & 1.78 \\ F & Xingyi & 2.17 & 0.84 \\ G & Beijing & 2.50 & 1.70 \\ H & Lanzhou & 2.65 & 1.72 \\ I & Lhasa & 3.06 & 2.2 \\ J & Yinchuan & 2.8 & 1.76 \\ K & Urumqi & 2.45 & 1.51 \\ L & Xining & 2.80 & 1.79 \\ M & Changchun & 2.53 & 1.54 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Average adaptability index of solar technologies in different zones.  
Figure 12: Adaptability index difference between the solar thermal technology and the PV technology. (**a**) Residential buildings. (**b**) Office buildings. (**c**) Commercial buildings. (**d**) Hotel building.  
These buildings are in service from 8:00 a.m. to 19:00 p.m., with high load in occupants, lighting, and equipment.  
Beijing in cold zone is classified into Grade 4 in terms of solar energy resources.  
Beijing provides policy support for the roof utilization technologies and encourages roof utilization. A certain subsidy is available for solar PV technology.
# Aerospace
## 9 Additional Mission Concepts
### Standalone SmallSat Orbiter Missions Using Small Launch Vehicle  
The study results have so far assumed the small satellite(s) are secondary payloads on a host mission. However, large missions still do not launch frequently and may not be willing to accommodate the demands of small spacecraft. Low-cost launch vehicles, such as the Electron with the Photon upper stage, are capable of launching about 30 kg on Earth escape trajectories [46]. With aerocapture, a 15 kg spacecraft can be inserted into low-circular orbit with such a launch vehicle. The 320 kg wet mass Photon can also be launched as an ESPA secondary payload to GTO [47]. Following orbit raising and an Earth escape maneuver, it can serve as a cruise stage, which can carry four 50 kg ADEPT aerocapture vehicles, each of which can deliver a 25 kg spacecraft to a different orbit at Mars or Venus.
# An Accurate Direction Finding Scheme Using Virtual Antenna Array via Smartphones
## 3 Basic Antenna Array Simulation  
Figure 2 shows that an antenna array is able to locate the acoustic source by steering its beam spatially. Now, we treat these antenna elements as several smartphones that have the same model and process the target signal in the same way. Then, we can hold only one smartphone in our hand and move it through the same positions of these antenna array elements. The smartphone receives acoustic signals at successive locations at short distances. At each point in time, we capture a single measurement from the whole sampling data as if we had a receive antenna array at each of these sampling points. We assume that the moving velocity of the smartphone is approximatively constant. Thus, if we choose a fixed sampling interval, these points will be evenly distributed on the smartphone's moving trajectory. Under the above assumption, we can leverage a normal smartphone to imitate an virtual uniform linear antenna array (VULA).  
Then, we filter the signals with BPF (Band Pass Filter) to get rid of the interference of the noise. In addition, in order to facilitate the calculation, we leverage AGC (Automatic Gain Control) to eliminate variational amplitude of the target signal to a constant one. Eventually, we utilize a smoothed MUSIC algorithm to estimate the DOA of the target acoustic signal.  
After deploying the VULA system in practice, some drawbacks are discovered. Due to the limit of the measuring range, VULA does not function well when the azimuth is over \(180^{\circ}\). Moreover, the existence of elevation between the user and the acoustic source will also cause estimate errors of direction finding.