esa-sceva/satcom-chunk-collection huggingface dataset:
# When Intelligent Transportation Systems Sensing Meets Edge Computing: Vision and Challenges
## 3 ITS Sensing
### Infrastructure-Based ITS Sensing
#### 3.2.1 Traffic Flow Detection  
One of the fundamental functions of infrastructure-based ITS sensing is traffic flow detection and classification at certain locations. Vehicle counts, flow rate, speed, density, trajectories, classes, and many other valuable data can be available through traffic flow detection and classification. [PERSON] et al. [94] proposed a traffic flow detection method using optimized YOLO (You Only Look Once) for vehicle detection and DeepSORT (Deep Simple Online and Realtime Tracking) for vehicle tracking and implemented the method on Nvidia edge device Jetson TX2. [PERSON] et al. [95] proposed a method by applying machine learning (KNN and SVM) to radio attenuation signals and were able to achieve success in traffic flow detection and classification. If processed with advanced signal processing methods, traditional traffic sensors, such as loop detectors and radar, can also expand their detection categories and performance. [PERSON] and [PERSON] [96] applied Fast Fourier Transform (FFT) to radar signals to detect traffic flow at the roadside. [PERSON] et al. [97] developed a method for traffic flow bottleneck detection using Wavelet Transform on loop detector data. Distributed sensing with acoustic sensing, traffic flow outlier detection, deep learning, and robust traffic flow detection in congestion are examples of other state-of-the-art studies in this sub-field [98; 99; 100; 101].
# A Random Subcarrier-Selection Method Based on Index Modulation for Secure Transmission  
[PERSON]  
1 National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China; [EMAIL_ADDRESS] (T.Z.); [EMAIL_ADDRESS] (J.C.)  
[PERSON]  
1 National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China; [EMAIL_ADDRESS] (T.Z.); [EMAIL_ADDRESS] (J.C.)  
[PERSON]  
2 China Academy of Space Technology (CAST), Beijing 100192, China; [EMAIL_ADDRESS] 2  
[PERSON]  
1 National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China; [EMAIL_ADDRESS] (T.Z.); [EMAIL_ADDRESS] (J.C.)  
Footnote 1: email: {Laianan, 2022, 2022, 2022, 2022, 2022, 2022, 2022, 2022, 2022,  
division multiplexing (OFDM) system with an FDA for secure transmission. Unfortunately, as the result of the coupling effect, there were positions besides the legitimate receiver where the BER was low as well. If eavesdroppers were located at these positions, the transmission was not secure. In order to mitigate the coupling effect of a conventional FDA, the authors of [15] proposed a random FDA (RFDA) by employing random frequency increments across the array elements. Furthermore, a multi-beam wireless communication scheme was proposed in [16] with RFDA, which was security-enhanced, spectrum-efficient and power-efficient.  
However, for medium-scale and large-scale DM systems, the receiver structure of RFDA dramatically increases the circuit cost, motivating the authors of [17] to propose a random subcarrier-selection array based using the OFDM technique instead of RFDA. Additionally, [PERSON] et al. [18] gave two practical random subcarrier-selection methods: a quadratic subcarrier set plus randomization procedure (QSS Plus RP) and a prime subcarrier set plus randomization procedure (PSS Plus RP), both of which could select the subcarriers randomly and achieve secure precise wireless transmission.
Resilient Sensor Networks with Spatiotemporal Interpolation of Missing Sensors: An Example of Space Weather Forecasting by Multiple Satellites  
[PERSON] 1,* [PERSON] 1 and [PERSON] 2  
1 Department of Electrical and Control Engineering, National Institute of Technology, Yonago College,  
Hikonacho 4448, Yonago 683-0854, Japan; [EMAIL_ADDRESS]
2 Department of Computer Science and Engineering, Toyohashi University of Technology, Hibarigaoka 1-1,  
Toyohashi 441-8580, Japan; [EMAIL_ADDRESS]
* Correspondence: [EMAIL_ADDRESS]; Tel.: +81-859-24-5045; Fax: +81-859-24-5135 Academic Editor: [PERSON]  
Received: 31 December 2015; Accepted: 1 April 2016; Published: 15 April 2016  
[PERSON] Abstract  
This paper attempts to construct a resilient sensor network model with an example of space weather forecasting. The proposed model is based on a dynamic relational network. Space weather forecasting is vital for a satellite operation because an operational team needs to make a decision for providing its satellite service. The proposed model is resilient to failures of sensors or missing data due to the satellite operation. In the proposed model, the missing data of a sensor is interpolated by other sensors associated. This paper demonstrates two examples of space weather forecasting that involves the missing observations in some test cases. In these examples, the sensor network for space weather forecasting continues a diagnosis by replacing faulted sensors with virtual ones. The demonstrations showed that the proposed model is resilient against sensor failures due to suspension of hardware failures or technical reasons.  
sensor networks; dynamic relational networks; spatiotemporal interpolation; self-recognizing networks; profiling +
Footnote †: journal: Sensors
# Pore Water Pressure Prediction Based on Machine Learning Methods--Application to an Earth Dam Case
## 2 Materials and Methods
### Random Forests Combined with Simulated Annealing  
#### 2.1.1 Random Forest  
Random forest (RF) is an ML technique that groups decision trees for tasks such as classification or regression. In a random forest, each single tree makes a class prediction, and the class with the most calls is chosen as the model's prediction. The architecture of an RF for regression is established through the four steps [37] stated below and shown in Figure 1.  
Step 1: Generate a dataset with a sample size of \(N\) by replacement. Utilize these selected \(N\) samples to train a decision tree, which serves as the sub-sample at the root node of the tree;  
Step 2: At each node of the decision tree, randomly pick \(m\) attributes from the total M attributes of each sample, where \(m\) is considerably smaller than \(M\). Next, employ a specific tactic, such as information gain, from these \(m\) attributes to choose one attribute as the splitting attribute for the node;  
Step 3: Throughout the decision tree's construction, each node must undergo splitting as described in step 2 until it reaches a point where further division is not possible;  
Step 4: repeat steps 1 to 3 to generate a random forest with a significant number of decision trees.  
Since its introduction by [PERSON] [37], RF has become a highly popular method for establishing prediction models. The RF model offers several advantages for prediction: RF modeling can handle high-dimensional data without the need for dimensionality reduction or feature selection. It avoids overfitting issues, has fast training speed, and can be easily combined with parallel calculation methods. Even if a large number of features are missing, RF can still maintain accuracy. This method has been well applied in the prediction of PWP [34; 35].
###### Abstract  
The dynamic stall characteristics of rotor airfoil are researched by employing unsteady Reynolds-Averaged Navier-Stokes (RANS) method under oscillating freestream velocity conditions. In order to simulate the oscillating freestream velocity of airfoil under dynamic stall conditions, the moving-embedded grid method is employed to simulate the oscillating velocity. By comparing the simulated dynamic stall characteristics of two-dimensional airfoil and three-dimensional rotor, it is indicated that the dynamic stall characteristics of airfoil under oscillating freestream velocity reflect the actual dynamic stall characteristics of rotor airfoil in forward flight more accurately. By comparing the simulated results of OA209 airfoil under coupled freestream velocity/pitching oscillation conditions, it is indicated that the dynamic stall characteristics of airfoil associate with the critical value of Cp peaks (i.e., the dynamic stall characteristics of OA209 airfoil would be enhanced when the maximum negative pressure is larger than \(-1.08\), and suppressed when this value is smaller than \(-1.08\)). By comparing the characteristics of vortices under different oscillating velocities, it indicates that the dissipation rate of leading edge vortex presents as exponent characteristics, and it is not sensitive to different oscillating velocities.  
rotor; dynamic stall; oscillating freestream velocity; airfoil; RANS equations +
Footnote †: journal: Applied Sciences
# Timing and Frequency Synchronization Using CAZAC Sequences for OFDM Systems
## 5 Results and Discussion  
In this section, the performance of the proposed CCPD and ACPD timing synchronization algorithms is simulated and compared to previous algorithms.  
The simulation system model adopted the OFDM system shown in Figure 1. The main system parameters are summarized in Table 1.  
The amplitude variation of the correlation value \(Corr(n)\) is simulated under different values of \(m\) and \(N\), as shown in Figure 9. The number \(m\) values of ZC sequences are 2, 4, 8, 16, and 32, and the corresponding length \(N\) values of the ZC sequences are 2048, 1024, 521, 256, and 128, respectively, so that they satisfy \(m*N=N_{\text{FFT}}=4096\).  
In the case that \(N_{\text{FFT}}\) remains constant, as the number \(m\) of ZC sequences increases, the length \(N\) of the ZC sequences decreases accordingly. This, in turn, leads to a decrease in the correlation value \(Corr(n)\). This trend of change is clearly shown in Figure 9.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline signal bandwidth & 40 MHz \\ sampling frequency & 100 MHz \\ modulation method & QPSK \\ length of OFDM symbols & 4096 \\ CP length & 290 \\ simulation times & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters of the simulation system.  
Figure 9: (**a**) The amplitude of the correlation value \(Corr(n)\) when \(m=2\), \(N=2048\), and SNR \(=-5\) dB. (**b**) The amplitude of the correlation value \(Corr(n)\) when \(m=4\), \(N=1024\), and SNR \(=-5\) dB. (**c**) The amplitude of the correlation value \(Corr(n)\) when \(m=8\), \(N=512\), and SNR \(=-5\) dB. (**d**) The amplitude of the correlation value \(Corr(n)\) when \(m=16\), \(N=256\), and SNR \(=-5\) dB. (**e**) The amplitude of the correlation value \(Corr(n)\) when \(m=32\), \(N=128\), and SNR \(=-5\) dB.
## 2 System Models and Definitions
### Performance Asymmetric Multiprocessor Platform  
Let \(\pi=(p_{1},p_{2},p_{3},\ldots,p_{m})\) be a performance asymmetric multiprocessor platform. The processing speed of the processors increases from small to large; that is, the processing speed of \(p_{i}\) is equal to or slower than that of \(p_{i+1}\). The processor speed of the processor is denoted by \(s_{i}\). For ease of understanding, the slowest processor speed is set to 1, which is \(s_{1}=1\). The other, faster processors \(p_{i}\) are multiples of \(p_{1}\), such as \(s_{i}=3\). The sum of the speeds of the first \(k\) processors in \(\pi\) is denoted as \(S_{k}\), i.e.,  
\[S_{k}(\pi)\stackrel{{\mathrm{def}}}{{=}}\sum_{i=1}^{k}s_{i}. \tag{5}\]  
The \"lambda\" parameter was defined by [PERSON] et al. in their research on a uniform multiprocessor [13; 14]:  
\[\lambda(\pi)\stackrel{{\mathrm{def}}}{{=}}\max_{1\leq i<m}\frac {\sum_{j=i+1}^{m}s_{j}}{s_{i}}. \tag{6}\]  
The value range of \(\lambda\) is \((0,m-1]\). When all processors are the same, \(\lambda\) takes the maximum value \((m-1)\).  
The main symbols used in this article and their meanings are summarized in Table 1.
# The Impact of Internal Structure Changes on the Damping Properties of 3D-Printed Composite Material
## 5 Comparison of Results
### Calculation of Mechanical Properties  
The analytical calculation of the mechanical properties is based on the values of the first natural frequency. To calculate the stiffness of each specimen, we used relation (7). For  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**Variant 2**} & \multicolumn{3}{c}{**Carbon Fiber—\(\zeta\)**} & \multicolumn{3}{c}{**Fiberglass—\(\zeta\)**} & \multicolumn{3}{c}{**Kevlar Fiber—\(\zeta\)**} \\ \cline{2-9}  & **Fn1** & **Fn2** & **Work.2** & **Fn1** & **Fn2** & **Work.2** & **Fn1** & **Fn2** & **Work.2** \\ \hline
0\({}^{\circ}\) & 0.033 & 0.023 & 0.015 & 0.037 & 0.037 & 0.027 & 0.038 & 0.038 & 0.032 \\ \hline
45\({}^{\circ}\) & 0.040 & 0.043 & 0.033 & 0.091 & 0.049 & 0.046 & 0.074 & 0.055 & 0.035 \\ \hline
90\({}^{\circ}\) & 0.057 the calculation of elastic modulus, we used the derived relation for the calculation of the first natural frequency of the free-loaded beam (Equations (8) and (9)).  
\[k=m\;\omega^{2} \tag{7}\]  
where \(k\) is the stiffness of the sample, \(m\) is the mass of the sample and \(\omega\) is the natural circular frequency.  
\[f_{1}=\frac{\lambda_{1}^{2}}{2\;\pi\;L^{2}}\sqrt[2]{\frac{E\;I}{\rho\;A}} \tag{8}\]  
\[E=\frac{\left(2\;\pi\;f_{1\;L^{2}}\right)^{2}\rho\;A}{\lambda_{1}^{4}\;I} \tag{9}\]  
where \(f_{1}\) is the first natural frequency, \(\lambda_{1}\) is the eigenvalue for the beam with free ends, \(L\) is the length of the beam, \(E\) is the flexural modulus of elasticity, \(I\) is the quadratic moment, \(\rho\) is the density of the specimen and \(A\) is the cross-sectional area of the specimen [23].  
All stiffness and modulus of elasticity values are shown in Tables 5 and 6.  
The highest values of stiffness and modulus of elasticity are achieved by the specimens with the least damping, i.e., the specimens with fiber configuration 0\({}^{\circ}\) and 0\({}^{\circ}\).90\({}^{\circ}\). The carbon fiber reinforced specimen in configuration 1 of variant 2 has the maximum flexural modulus (12.07 GPa). Conversely, the Kevlar-fiber-reinforced specimen in configuration 2 of variant 1 has the lowest modulus of elasticity (1.62 GPa).
## 5 Conclusions  
Under the background of the \"dual carbon\" strategy, with the application of distributed PV systems and the increasing electrification of buildings, facing the high level of application of DC appliances in the future, it is important to solve the matching problem between solar PV power generation and user load effectively with microgrid design for the application of solar energy in buildings. This project investigates the use of domestic DC loads in the Qingdao area, proposes a PV-based design of a domestic DC microgrid with local solar resources, and conducts practical tests on the system. The results show that the proposed DC microgrid system can accurately provide the voltage required for small household DC appliances, such as 24 V, 12 V, 5 V, 3.3 V, etc., and the direct supply of DC appliances using solar photovoltaics can currently reduce about 20% of power consumption. The payback period of the equipment is about 4.8 years. The project can be widely used in all kinds of buildings including small DC equipment, such as residential, office, laboratory, and so on. The innovation points of this project are as follows:  
1. Realizing a single circuit to supply different household DC loads, and reducing the number of converters at the equipment end, with fast response and stable circuits;
2. It enhances the direct and efficient use of PV with DC loads in buildings without changing the original grid circuit.  
This study only considers the power consumption of existing small DC electrical equipment, and does not consider that the proportion of DC electrical equipment will increase in the future, which may lead to the fact that the existing photovoltaic power supply equipment will not generate enough power to supply the new DC electrical equipment in the future. In the subsequent research, photovoltaic power supply equipment can be installed according to the proportion of annual increase in DC electrical equipment.  
Conceptualization, W.P. and Q.L.; methodology, W.P. and M.W.; software, W.P. and Z.L.; validation, W.P.; investigation, W.P. and Q.L.; data curation, W.P. and W.J.; writing--original draft preparation, W.P. and Q.L.; writing--review and editing, Y.Z. and Q.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Shandong Provincial Natural Science Foundation, grant number ZR2021 ME051, and the National Natural Science Foundation of China, grant number 51608290.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.
# Multidimensional Assessment of Lake Water Ecosystem Services Using Remote Sensing
## 2 Materials and Methods
### Geospatial and Statistical Datasets
#### 2.2.1 Lake Bathymetric Data  
Lake bathymetric data were acquired from the Minnesota Geospatial Information Office [66], which contains lake bathymetric contours, lake bathymetric digital elevation model (DEM), and lake bathymetric outline. The lake bathymetric DEM has a 5 m spatial resolution with one attribute representing lake depth in (negative) feet, covering approximately 1900 lakes. An example of the lake bathymetric DEM is shown in Figure 3.
## 4 Results and Discussion
### Modified Gompertz Model  
The modified Gompertz model was fitted to the experimental data to predict biogas production under various conditions like mono-digestion and co-digestion with different inoculum-to-substrate ratios. Figure 3 shows a comparison of the predicted and experimental cumulative biogas production. The actual biogas (Y) production values from the co-digestion of WH and FW were 1655, 1140, and 1400 mL/g VS for ratios of 1:1, 2:1, and and 1:2, respectively. Under mono-digestion conditions of CD and WH, the maximum biogas yields were 925, 905, and 850 mL/g VS for the 1:1, 2:1, and 1:2 ratios, respectively. Furthermore, in the case of the mono-digestion of CD and FW, the biogas yields were 945, 950, and 970 mL/g VS for the 1:1, 2:1, and 1:2 ratios, respectively. The fitted model yielded maximum biogas yields (Ym) of 1683, 1154, and 1422 mL/g VS for WH and FW co-digestion at 1:1, 2:1, and 1:2 ratios, respectively. Similarly, in the case of CD and WH mono-digestion, the predicted maximum biogas yields were 937, 903, and 846 mL/g VS for the 1:1, 2:1, and 1:2 ratios. In the CD and FW mono-digestion experiments, the corresponding Ym values were 945, 950, and 970 mL/g VS for the 1:1, 2:1, and 1:2 ratios. The ultimate biogas yield (asymptotic values) denoted by K were determined as 0.223, 0.259, and 0.205 for the 1:1, 2:1, and 1:2 ratios, respectively, in the WH and FW co-digestion. In the CD and WH mono-digestion, the corresponding K values were found to be 0.234, 0.320, and 0273 for the ratios 1:1, 2:1, and 1:2. In the CD and FW WH mono-digestion studies, the measured K values were 0.248, 0.355, and 0.334 for the 1:1, 2:1, and 1:2 ratios, respectively. The coefficient of determination (R\({}^{2}\)) was determined to be 0.998, 0.998, 0.995, 0.999, 0.999, 0.999, 0.998, 0.996, and 0.999 for all samples. Notably, all R\({}^{2}\) values approached unity (1), indicating a high degree of conformity between the experimental findings and simulated values. Moreover, it signifies the precision and dependability in the correlation between experimental observations and model predictions. The CD and FW mono-digestion studies exhibited a lag phase of approximately 1 day across three different ratios. In the case of WH and FW, co-digestion in a 1:2 ratio also exhibited a lag phase of 1 day. In contrast, the CD and WH mono-digestion mixture displayed comparatively longer lag phases, specifically 3.55, 4.49, and 3.75 days for the 1:1, 2:1, and 1:2 ratios, respectively. The WH and FW co-digestion at 1:1 and 2:1 demonstrated moderate lag phases of 2.02 and 2.39 days, accompanied by the highest biogas production. It can be observed that from the model prediction, the higher amount of FW plays a crucial role in reducing the lag phase and enhancing the biogas production (Supplementary Table S3).
## 3 Results
### ANN Model  
#### 3.1.1 Feature Selection  
In Figure 5, the error metrics for the models described in Table 1, using ANN, are presented.  
By analyzing Figure 5, only based on MAE, C3 is the most accurate in forecasting cooling energy demand. However, as RMSE is more sensitive to highest errors, this was the  
Figure 5: Error metrics for cooling energy demand using artificial neural networks (ANN).  
main criteria in the selection of the best set of inputs. Therefore, based on this latter error metric, C2 (only based on exogenous variables) provided the most accurate predictions for cooling energy demand, closely followed by C3 and C6.  
In Figure 6, error metrics for the sets of inputs tested for ANN models on forecasting heating energy demand are presented.  
In the heating period, as in the cooling period, different assumptions can be taken by analyzing both MAE and RMSE. Considering MAE, H3 is the most accurate in forecasting heating energy demand but keep following the criteria considered in the cooling period, by analyzing RMSE, H4 provided the most accurate predictions for heating energy demand, with models H3, H5, and H6 also being very accurate in forecasting energy demand. The importance of using historical load data and outdoor air temperature can also be concluded, while direct normal solar radiation is a recommendable feature in this case. Another detail that stands out is that, by comparing Figure 5 (cooling period) with Figure 6 (heating period), both error metrics tend to be lower in the latter, meaning that it is easier for ANN models to predict energy demand in the heating season.
# Integrated Optical Phased Arrays for Beam Forming and Steering
## 8 Applications & Potential Alternatives  
Large-scale OPAs with fast manipulation and dynamical steering provide a low-cost chip-scale solution for many high-performance applications. Some other structures function as potential alternatives to integrated OPAs. Applications of OPA will be discussed in this section. Some potential alternatives will be discussed.
# Aircraft Track Anomaly Detection Based on MOD-Bi-LSTM
###### Abstract  
In order to ensure flight safety and eliminate hidden dangers, it is very important to detect aircraft track anomalies, which include track deviations and track outliers. Many existing track anomaly detection methods cannot make full use of multidimensional information of the relevant track. Based on this problem, an aircraft track anomaly detection method based on the combination of the Multidimensional Outlier Descriptor (MOD) and the Bi-directional Long-Short Time Memory network (Bi-LSTM) is proposed in this paper. Firstly, track deviation detection is transformed into the track density classification problem, and then a multidimensional outlier descriptor is designed to detect track deviation. Secondly, track outliers detection is transformed into a prediction problem, and then a Bi-LSTM model is designed to detect track outliers. Experimental results based on real aircraft track data indicate that the accuracy of the proposed method is 96% and the recall rate is 97.36%. It can detect both track deviation and track outliers effectively.  
anomaly detection; Multidimensional Outlier Descriptor (MOD); Bi-directional Long-Short Time Memory (Bi-LSTM); track outliers; track deviation +
Footnote †: journal: 2021  
1007. [[https://doi.org/10.3390/electronics10091007](https://doi.org/10.3390/electronics10091007)]([https://doi.org/10.3390/electronics10091007](https://doi.org/10.3390/electronics10091007))