esa-sceva/satcom-chunk-collection huggingface dataset:
# Study of Self-Heating and High-Power Microwave Effects for Enhancement-Mode p-Gate GaN HEMT
## 2 Device Structure and Simulation Model
The effect of piezoelectric polarization is simulated by adding the divergence of the piezoelectric polarization vector as an additional charge term. Traps can be coupled to nearby interfaces and contacts through tunnels. Sentaurus models non-local tunneling to the trap as the sum of inelastic, phonon-assisted process and elastic process [30; 31]. The Barrier-tunneling model and traps model are activated. The interface defect traps density of AlGaN/Nittride(donor-type), AlGaN/Oxide(acceptor-type), and GaN/AlGaN (acceptor-type) were set as \(5\times 10^{13}\) cm\({}^{-2}\), \(5\times 10^{13}\) cm\({}^{-2}\), and \(6\times 10^{12}\) cm\({}^{-2}\), respectively. In addition, that of the buffer region (acceptor-type) is \(1\times 10^{15}\) cm\({}^{-2}\). The thermionic model is activated since the conventional transport equation is no longer valid at the heterojunction interface, and the current and energy flux at the abrupt interface between the two materials were used to define the interface conditions of the heterojunction [32]. The Shockley Read Hall recombination model and recombination-generation-heat model are activated to calibrate the recombination effects. Besides, the thermodynamic is activated to simulate the electrothermal characteristics by extending the drift diffusion under the assumption that carriers are in thermal equilibrium with the lattice. The change of lattice temperature and current density was affected by the above thermal models sensitively. The boundary of the device was set as the thermal contact surface and the substrate bottom was set to be 300 K.
## 4 Discussion
However, evidence suggests that for partially vegetated landscapes with dark soil backgrounds (such as those occurring in the study area), the spatial resolution has an important impact on NDVI. Therefore, NDVI at different spatial scales, such as between ArborCam and PlanetScope, may not be directly comparable [57], as was the case here. Despite the difference in the dynamic range of NDVI values between the sensors, the strong linear relationship between the PlanetScope NDVI and the ArborCam NDVI provides confidence for the use of the PlanetScope constellation to capture seasonal trends in saltmarsh vegetation. The airborne ArborCam sensor was found to be capable of capturing the fine-scale patchiness of saltmarsh. The two sensors in combination have the potential to improve the mapping and monitoring of coastal saltmarsh and blue carbon storage potential.
# Urban Traffic Signal Control under Mixed Traffic Flows: Literature Review
## 5 Intersection State Estimation
### Model-Driven Methods
The obtained results confirmed the Support Vector Machine-based (SVM) model applicability for short-term traffic speed prediction. The authors of [53] also researched short-term traffic flow prediction. Although their work focused on improving short-term traffic flow predictions, the authors proposed an SVM-based method with an adaptive multi-kernel function and spatio-temporal correlation. The benefit of such an approach is that the SVM has conventional features while using different kernel functions is more suitable for time-varying traffic flow assessments. Conducted experiments imply that this method can adapt to dynamic traffic flow characteristics on urban roads. Obtained results also revealed certain delays between predicted traffic flow compared to the real values used for comparison. The authors of [49] proposed a Graph Convolutional Network (GCN) model for evaluating the potential of estimating congestion areas. This method relies on road street-view imagery and Point of Interest datasets, which are used as input for the GCN to detect urban areas with a high probability of congestion. The proposed method reveals the potential of exploiting open data to solve conventional challenges in urban traffic and applying this method in different cities could give different results. To evaluate the method, the authors used ground truth data derived from GPS data collected by taxi vehicles, and GPS data have their limitations, as mentioned earlier.
## 3 Results and Discussion
### Infrared Spectrum  
The infrared spectrum was used to identify the polysaccharides by detecting the stretching vibration of related groups, which allows identification of a variety of functional groups, the type of sugar and glycosides. Figure 2 shows the FTIR spectra with the characteristic absorptions of the beverages before and after fermentation.  
Substantial differences between the spectra were, indeed, observed, and the intensity of the peaks was higher before the fermentation (Figure 2a) and decreased in the fermented beverages (Figure 2b), indicating an almost total degradation of sugars.  
In particular, the infrared spectrum analysis of the polysaccharides, recorded for wavelengths 500-4000 cm\({}^{-1}\), showed an intense peak between (3415-3395 cm\({}^{-1}\)), representative of the vibrations of the hydroxyl groups (-OH), followed by a weak vibration of the C-H band at 2930 cm\({}^{-1}\)[54]. Peaks with vibration bands between (3415-2930 cm\({}^{-1}\)) are unidentified organic groups (Figure 2a). The peak at 1612 cm\({}^{-1}\) is attributed to the vibration of the C = O group in its protonated carboxylic form. The peak at 1415 cm\({}^{-1}\) is attributed to the vibration of the COO- group in its deprotonated carboxylic form polysaccharide extract [55]. Moreover, the peaks detected at 1200-1000 cm\({}^{-1}\) indicate that the fractions are pyranose forms of carbohydrate [56]. The peaks with vibration bands at 992 cm\({}^{-1}\) and 942 cm\({}^{-1}\) (Figure 2b) derive from the antisymmetric stretching of tetrahydropyran, suggesting the presence of an arrangement in the carbohydrate units [57]. The peak at 605 cm\({}^{-1}\) is related to D-glucopyranoside. Additionally, the vibration at 766 cm\({}^{-1}\) is related to rhamn poset in the polysaccharide. The functional groups, such as -OH, -COOH  
Figure 2: Infrared spectrum of polysaccharides, recorded for wavelengths 500–4000 cm\({}^{-1}\). D refers to the polysaccharides detected in not fermented beverage (**a**). A, B, and C refer to the polysaccharides detected in the beverages fermented with the selected cultures FB-sp, FB-lcsp, and FB-lc, respectively (**b**).  
and C=O, largely found in carbohydrate in addition to anionic and cationic groups, have a good ability to counteract oxidative stress, which is expressed by excessive production of reactive oxygen species [58].
# WISCANet: A Rapid Development Platform for Beyond 5G and 6G Radio System Prototyping
## 3 System Design
### FPGA Acceleration and Integrated Testing  
WISCANet applications can also leverage the embedded FPGAs in the USRP radios via the RFNoC interface. Custom IP cores can be wrapped in a simple RFNoC block and embedded in the FPGA image. These custom blocks can then be exposed to the baseband application through WISCANet for testing and valiation of the FPGA block. This approach allows the IP cores to be validated with real RF samples and tests the block's interaction with other algorithmic components, enabling partial integration, progressive development and FPGA employment without requiring full integration of the FPGA into a real-time system.
# Research and Application of Semi-Supervised Category Dictionary Model Based on Transfer Learning
## 2 Materials and Methods
### Common Data Symbols  
In alignment with the notation utilized in sparse classification of the relevant category dictionary, \(X^{s}=\left[X^{s}_{1}\ldots X^{s}_{m}\ldots X^{s}_{M}\right]\in\mathcal{R}^{d \times N^{s}}\) is employed to denote the source domain data matrix, where \(N^{s}\) represents the total number of samples within the source domain. The term \(X^{s}_{m}=\left[x^{s}_{1,m},\ldots,x^{s}_{s,m},\ldots,x^{s}_{N^{s}_{m},M} \right]\in\mathcal{R}^{d\times N^{s}_{m}}\) corresponds to the data matrix of class m in the source domain, with \(x^{s}_{i,m}\) denoting individual sample data. The notation \(X^{t,l}=\left[X^{t,l}_{1}\ldots X^{t,l}_{m}\ldots X^{t,l}_{M}\right]\in \mathcal{R}^{d\times N^{t,l}}\) is used to signify the data matrix composed of the labeled samples in the target domain. \(X^{t,l}_{m}=\left[x^{t,l}_{1,m}\ldots x^{t,l}_{j,m}\ldots x^{t,l}_{N^{t,l}_{m},m}\right]\in\mathcal{R}^{d\times N^{t,l}_{m}}\) represents the data matrix of class m in the target domain, where \(x^{t,l}_{j,m}\) stands for individual sample data. The notation \(X^{t,u}\in\mathcal{R}^{d\times N^{t,u}}\) corresponds to the data matrix consisting of unlabeled samples in the target domain. \(I_{m}\) is used to represent the number of dictionaries corresponding to each class of samples. For any given test sample, \(D_{m}\) is employed in lieu of \(X_{m}\) as the category dictionary.
_J. Sens. Actuator Netw._**2013**, \(2\), 509-556; doi:10.3390/jsan2030509  
_Journal of Sensor and Actuator Networks_  
**ISSN 2224-2708**  
www.mdpi.com/journal/jsan  
_Article_  
**Wireless Sensor Network Operating System Design Rules Based on Real-World Deployment Survey**  
**[PERSON] \({}^{1,2,\ast}\), [PERSON] \({}^{1,2}\), [PERSON] \({}^{1,2}\) and [PERSON] \({}^{1,2}\)**  
\({}^{1}\) Institute of Electronics and Computer Science, 14 Dzerbenes Street, Riga, LV 1006, Latvia;  
E-Mails: [EMAIL_ADDRESS] (A.E.); [EMAIL_ADDRESS] (K.N.); [EMAIL_ADDRESS] (L.S.)  
\({}^{2}\) Faculty of Computing, University of Latvia, 19 Raina blvd, Riga, LV 1586, Latvia  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +371-6755-8224; Fax: +371-6755-5337.  
_Received: 1 June 2013; in revised form: 13 July 2013 / Accepted: 24 July 2013 / Published: 16 August 2013_  
**Abstract:** Wireless sensor networks (WSNs) have been a widely researched field since the beginning of the 21 st century. The field is already maturing, and TinyOS has established itself as the _de facto_ standard WSN Operating System (OS). However, the WSN researcher community is still active in building more flexible, efficient and user-friendly WSN operating systems. Often, WSN OS design is based either on practical requirements of a particular research project or research group's needs or on theoretical assumptions spread in the WSN community. The goal of this paper is to propose WSN OS design rules that are based on a thorough survey of 40 WSN deployments. The survey unveils trends of WSN applications and provides empirical substantiation to support widely usable and flexible WSN operating system design.  
**Keywords:** wireless sensor networks; deployment; survey; operating system; design rules
# The Wind and Photovoltaic Power Forecasting Method Based on Digital Twins
## 3 Simulation Experiments
### Analysis of Simulation Experiment Results
#### 3.4.1 Experimental Analysis of Power Load Forecasting Model
The thick blue line represents the ground truth data, while the other colored lines represent the predictions of each algorithm. Figure 8a,b represent the PV power generation data for Flanders and Wallonia regions, respectively, while Figure 8c,d represent the wind power generation data for Flanders and Wallonia regions, respectively. The black boxes in Figure 8a-c indicate that the predictions of the Transformer model have significant deviations from the actual results. In the black box of Figure 8d, the predictions of both CNN and Transformer do not match the actual results. Overall, the performance of LSTM, Attention, and the proposed method WPNet is highly stable and accurate. However, compared to the predictions of LSTM and Attention, the proposed method WPNet exhibits the closest resemblance to the ground truth across all four datasets. In summary, the above analysis results demonstrate that the proposed wind and PV power load prediction model WPNet in this paper can accurately forecast the renewable energy generation power for the next time step. For the complete training process, see Figure A1 in Appendix A.
# Soil Moisture Retrieval by Integrating SAR and Optical Data over Winter Wheat Fields
## 3 Methods
### Parameterization of Soil Backscattering Model  
The AIEM model can accurately simulate the backscattering coefficient of the soil surface under a wide range of conditions. The root mean square height (S) and the correlation length (L) were used to represent soil surface roughness in the AIEM model. For the case of agricultural soil, an exponential correlation function will generally give a good fit to the majority of experimental surfaces [23]. The parameters listed in Table 4 were the inputs of the AIEM model to simulate the soil backscattering coefficients.  
In this study, we use an effective roughness parameter \(Z\) with \(Z=\frac{s}{\sqrt{L}}\) to mix the effects of roughness on the soil backscattering coefficients. Based on the statistical analysis of the simulated data set, the soil backscatter was parameterized in Formula (7).  
\[\sigma_{pq}^{\text{S}}=(a_{spq}\cdot Z^{2}+b_{spq}\cdot Z-c_{spq})\cdot\text{SM }^{d_{spq}} \tag{7}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Value Range** & **Interval** & **Units** \\ \hline S & 0.25–2.5 & 0.05 & cm \\ L & 8–15 & 0.5 & cm \\ SM & 0.05–0.45 & 0.01 & cm\({}^{3}\cdot\)cm\({}^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: The main input parameters of AIEM model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Value Range** & **Interval** & **Units** \\ \hline LAI & 0.5–5.0 & 0.1 & — \\ Leaf radius & 1.0 & — & cm \\ leaf thickness & 0.2 & — & mm \\ Leaf water content & 75–85 & 5 & \% \\ Stem length & 30–40 & 2 & cm \\ Stem radius & 0.1–0.2 & 0.05 & cm \\ SM content & 0.05–0.45 & 0.025 & cm\({}^{3}\cdot\)cm\({}^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The main input parameters of MIMICS model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Polarization** & \(\mathbf{a_{1}}\) & \(\mathbf{b_{1}}\) & \(\mathbf{a_{2}}\) & \(\mathbf{b_{2}}\) & \(\mathbf{a_{3}}\) & \(\mathbf{b_{3}}\) & \(\mathbf{k}\) \\ \hline VV & \(-1.73\) & \(-0.0530\) & 12.3 & 0.214 & 11.3 & 0.157 & 0.292 \\ VH & \(-0.807\) & \(-0.129\) & 6.29 & \(-0.0533\) & 5.51 & 0.0216 & 0.225 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The empirical coefficients for Equation (4).  
The empirical coefficients of \(a_{s}\), \(b_{s}\), \(c_{s}\) and \(d_{s}\) are obtained in Table 5 with R of 0.98 for both VV and VH polarization.
## 3 Results  
In this paper, the regression coefficient (R2), the average relative average error (MAE), and the absolute root-mean-square error (RMSE) of the test set are selected for error evaluation. The three errors of the different calculation models are shown in Table 5. In order to reflect the randomness of NOx data occurrence in practical applications and to calculate the prediction effect of the models, a dotted line plot is used to compare the true and predicted values. The results of the comparisons between the predicted and true values of the three models on the test and training sets are shown in Figures 8-10.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **R2** & **MAE** & **RMSE** \\ \hline BPNN & 0.90922 & 0.05425 & 107.0553 \\ GA–BPNN & 0.9378 & 0.04678 & 83.8131 \\ GMDH–BPNN & 0.95042 & 0.02296 & 61.9985 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of evaluation parameters of different models.  
It can be seen from Table 5 that although the error indicators of the three models are not very obvious, the errors are all lower than 10%, and the R2, MAE, and RMSE of the GMDH-BPNN model are all smaller than the other two models. It shows that the fitting degree between the GMDH-BPNN prediction curve and the original data curve is better, the error between each prediction value is also smaller, and the error between the obtained prediction sample and the test sample is smaller.  
From the prediction curves of the above three models on the training and test sets, it can be seen that all models can fit the trend of the real NOx values better when changes occur, but the prediction on the extreme value point is still not satisfactory when large changes occur.  
Figure 8: (**a**) GMDH–BPNN predictions on the training set; and (**b**) GMDH–BPNN predictions on the test set.  
Figure 10: (**a**) GA–BPNN predictions on the training set; and (**b**) GA–BPNN predictions on the test set.  
Figure 9: (**a**) BPNN predictions on the training set; and (**b**) BPNN predictions on the test set.  
abrupt changes occur. This may be due to the small volume of the sample data and the overfitting phenomenon on the training set, resulting in unsatisfactory results on the test set. Meanwhile, since the accuracy of the GMDH-BPNN is slightly higher than that of GA-BPNN, it indicates that for the current data, the structure of the BPNN neural network has slightly more influence on its accuracy than the influence of weights and biases.
# A Machine Learning-Based Method for Modeling TEC Regional Temporal-Spatial Map
## 2 Modeling
### Modeling Method
where \(\lambda\) is the geographical latitude, \(\varphi\) is the geographical longitude, \(p\) is the average solar radio flux of 10.7 cm in 12 months (\(F10.7_{12}\)), \(r\) is the average number of sunspots in 12 months (\(R_{12}\)), \(m\) is the month, \(t\) is the given universal time, \(N\) is the number of ionospheric observation stations used for modeling, \(\widehat{\text{TEC}}(p,r,m,t)\) is the temporal map of TEC, and \(W_{n}(\lambda,\,\varphi)\) is the weight coefficients of the spatial map of TEC (details are provided in Section 2.3). 1. How to determine the parameters of the model? ML is a supervised learning method [44] that obtains the target model and the corresponding algorithm through training. The least-squares (LS) regression analysis algorithm is one of the criteria used to determine the parameters of the regression model. Due to its advantages of simple calculation, short modeling time, easy understanding, and being highly sensitive to outliers, this algorithm has been widely used in parameter prediction and time series modeling in many fields. And LS shows good performance; therefore, this is also the reason why we choose it for modeling. 2. How to evaluate the performance of the model? First, we must develop a unified standard to determine the optimal model and its correlation coefficients from the hypothesis space. Therefore, we calculate the following parameters as the evaluation standard: (a) Root mean square error (RMSE): \[\text{RMSE}=\sqrt{\frac{1}{H}\sum_{h=1}^{H}\bigl{(}\text{TEC}_{o}{}^{h}-\text{ TEC}_{p}{}^{h}\bigr{)}^{2}},\] (2) (b) Relative root mean square error (RRMSE): \[\text{RRMSE}=\sqrt{\frac{1}{H}\sum_{h=1}^{H}\left(\frac{\text{ TEC}_{o}{}^{h}-\text{ TEC}_{p}{}^{h}}{\text{TEC}_{o}{}^{h}}\right)^{2}}\times 100,\] (3) where \(\text{TEC}_{o}{}^{h}\) is the \(h\)th observed value of TEC, \(\text{TEC}_{p}{}^{h}\) is the \(h\)th predicted value of TEC, and \(H\) is the total statistical amount.
# On the Fault Detection and Diagnosis of Railway Switch and Crossing Systems: An Overview
### Actuation Mechanism  
Upon receiving the command from the signaling block, the switch rails are actuated by the actuator mechanism which varies depending on the switch system used. The most commonly used switch systems are linear actuators which consist of mechanical linkages, whereas new actuation mechanisms are now developed which actuate the switch in the vertical plane.  
The length of an S&C system varies depending on its type, and bending of the switch rails is important to satisfy the requirements of the system and security of the passing vehicle. For example, the minimum flange-way clearance is 56 mm for a vertical shallow C-switch (CVS [19]). To ensure this minimum clearance at the back of the switch, and to ensure the required bending of the switch rails, an additional drive, known as the backdrive, is often used. Some switch systems also use supplementary drives at the rear end to ensure clearance. The fastest switch used by Network Rail in the UK is installed with a clamplock actuator and Hy-Drive hydraulic supplementary drives [19]. In traditional switches, mechanical linkages are often connected between the front and rear stretcher bars to transfer the actuation force [15]. In high-performance switch systems (HPSS), this traditional backdrive is replaced by a torque tube called a Powerlink backdrive system, which is a torsional tube connected to the front and rear stretcher bar [34].
# A Scheduling Scheme for Improving the Performance and Security of MU-MIMO Systems
## 5 Numerical Results and Discussions
### Bit Error Rate  
Figure 5 shows the mean BER as a function of \(E_{b}/N_{0}\), parameterized by \(K\) and \(U_{t}\) considering \(A=4\), \(U=4\), BPSK modulation, \(e=0.1\). The results show that, as the \(E_{b}/N_{0}\) increases, the mean BER decreases. However, it is observed that the curves do not decay as quickly or linearly as is usually observed in scenarios with perfect channel estimation [8]. Thus, it is observed that the mean BER decays slowly as the \(E_{b}/N_{0}\) increases. This behavior occurs due to the imperfect channel estimation at the BS receiver. Nevertheless, as \(K\) increases, the BER decreases, since the power of the LOS component of the signals increases. Additionally, for a given value of \(E_{b}/N_{0}\), it is observed that when \(U_{t}\) increases, then the mean BER decreases. Specifically, when \(U_{t}=4\), the system is an ordinary MU-MIMO system in which the scheduling cannot be applied. On the other hand, when \(U_{t}=6\), the proposed scheme is applied, and therefore performance improves. Additionally,  
Figure 4: OP as a function of \(U_{t}\), parameterized by \(K\) and the threshold \(T\), considering BPSK modulation, \(A=2\), \(U=2\), and \(e=0.1\).  
Figure 3: OP as a function of \(E_{b}/N_{0}\), parameterized by \(U_{t}\) and by \(e\), considering BPSK modulation, \(A=4\), \(U=4\), \(T=0\) dB, and a channel with Rician factor \(K=1\).  
when \(U_{t}\) increases, it is observed that the inclination (slope) of the BER curves changes. Specifically, they decay faster as the \(E_{b}/N_{0}\) increases, which signifies an increase in diversity despite the imperfect channel estimation and despite the system being fully loaded.  
Figure 6 shows the mean BER as a function of \(e\), parameterized by \(U_{t}\) considering BPSK modulation, \(A=6\), \(U=6\), \(E_{b}/N_{0}=10\) dB and a fading channel with a Rician factor \(K=2\). The results show that as \(e\) approaches to one, the BER increases due to imperfections in the channel estimation. This event may cause some users to not be chosen properly in the receiver with the proposed scheduling scheme. However, when \(U_{t}\) is increased, the system performance improves because the diversity also increases. These results corroborate that the proposed scheduling scheme also allows reducing the undesirable effects produced by the imperfect channel estimation. Thus, the higher \(U_{t}\), the lower the BER of the system. Obviously, in exchange for an increase in the number of complex operations carried out by the receiver at the BS, but which can be performed in practical systems.