esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Establish the Short-Term Load Forecasting Model
### Main Mode Steps of EN-GMDH  
The detailed modeling steps of the short-term load forecasting model are as follows. The input data consist of multiple sets of input variables. Each set of data contains the characteristic variables influencing the load, such as temperature, holidays, etc., and the load value as the observation value. Set \(n\) rows (groups) and \(m+1\) columns (\(m\) columns are characteristic variables, and 1 column is the load value):  
Step 1: The input variables are centralized and standardized first. Then 75% of the input variables \(n\) are divided into a training set \(n_{t}\), and the remaining 25% are used as a testing set \(n_{C}\); \(n=n_{t}+n_{c}\).  
Step 2: Calculate the difference degree of the training set \(n_{t}\). Calculate the Lance-Williams distance of the daily data and calculate the weight of the daily data.  
Step 3: The 10 characteristic variables of the training set are cross-recombined. By Equation (1), the cross-recombination will produce \(k\) (calculated by Equation (1)) neurons. Each neuron contains any two characteristic variables.  
Step 4: Take a neuron and corresponding load value into Equation (3). The coefficients \(a_{0}\), \(a_{1}\), \(a_{2}\), \(a_{3}\), \(a_{4}\), and \(a_{5}\) of the K-G polynomial in this neuron are estimated by elastic net, and the obtained coefficients are assigned to this K-G polynomial.  
Step 5: Take the testing set data into the K-G polynomial obtained in Step 4 and get estimates of \(g_{j}\). Calculate the threshold \(w\) of this neuron by taking the \(g_{j}\) into Equation (4).  
Step 6: Repeat steps 4 to 5, completing the training and inspection of the remaining neurons.  
Step 7: Sort the neurons in ascending order according to the threshold \(w\). The first \(m\) neurons are retained and output. The remaining \(k\)-\(m\) neurons are removed, and the minimum \(w\) of the layer is recorded.  
Step 8: Take the output of the first layer as the input of the second layer, cross-recombine again, and repeat Steps 4 to 7, extending the next layer of the network.  
Step 9: The forecast model error will be the minimum value when the lowest regularity criterion \(\omega\) in the current layer is no longer smaller than that of the previous layer. The network stops expanding, and the model outputs the predicted results
# Vehicle Speed Estimation Based on 3D ConvNets and Non-Local Blocks
## 5 Conclusions and Future Work  
This paper proposes a deep learning-based method to the average vehicle speed estimation problem. This method has a desirable feature of not requiring manual intervention. Compared with other common vehicle estimation methods, the proposed method is not dependent on external environment factors, nor does it on vehicle detection and tracking processes. In summary, it is a self-closed end-to-end framework.  
Based on ResNet50, we propose an end-to-end IR(2+1)D50-cNLms model to estimate average vehicle speed based on video footage. The modified method IR(2+1)D50-cNLms has a global spatial-temporal perspective on the information in the video. We experiment with different 3D ConvNets on VehSpeedDataset10. The experiments suggest that optical flow is crucial information for the vehicle speed estimation problem. By using both RGB images and optical flow as inputs, our IR(2+1)D50-cNLms model performs exceedingly well.  
A limitation on our model is lack of quality data. As noted previously, deep learning-based models require extensive labeled data, which is lacking. The lack of data could lead to 3D ConvNets overfitting. In the future, we can try to expand our dataset to improve the model performance. Inspired by [39] using multi-sensor data fusion to construct the model, we can also try to integrate RGB images and optical flow for more effective features extraction with the understanding of the marked differences between the two input types.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **MAE** & **MSE** & **Automatic** & **Detection-Tracking** \\ \hline GPS & 1.64 & – & \(\surd\) & \(\times\) \\ RADAR & 1.07 & – & \(\surd\) & \(\times\) \\ FULLACC [20] & 8.59 & 104.98 & \(\surd\) & \(\surd\) \\ OPtScale [23] & 1.71 & 9.66 & \(\times\) & \(\surd\) \\ OPtScaleVP2 [23] & 15.66 & 411.67 & \(\times\) & \(\surd\) \\ OPtCalib [23] & 1.43 & 8.71 & \(\times\) & \(\surd\) \\ OPtCalibVP2 [23] & 2.43 & 15.63 & \(\times\) & \(\surd\) \\ Ours & 2.73 & 14.62 & \(\surd\) & \(\times\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of different Methods on the same dataset.  
**Supplementary Materials:** The following are available online at [[http://www.mdpi.com/1999-5903/11/6/123/s1](http://www.mdpi.com/1999-5903/11/6/123/s1)]([http://www.mdpi.com/1999-5903/11/6/123/s1](http://www.mdpi.com/1999-5903/11/6/123/s1)). This is the code for the models we built in this article.  
**Author Contributions:** Conceptualization, Z.Y.; methodology, H.D.; software, H.D. and M.W.; validation, H.D., M.W. and Z.Y.; formal analysis, H.D.; investigation, H.D.; resources, Z.Y.; data curation, H.D.; writing--original draft preparation, H.D.; writing--review and editing, H.D., M.W. and Z.Y.; visualization, H.D. and M.W.; supervision, Z.Y.; project administration, Z.Y.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 6 Results and Analysis
### Qualitative Impedance Spectrum Analysis  
In this subsection, we focus mainly on the qualitative analysis of the impedance spectrum of a single cell at a single cycle. Figure 8a shows an example impedance spectrum obtained for cell 1 at cycle 150. The general form and behavior stay the same for each cell at different cycles.  
The impedance spectrum in Figure 8a shows that not all the expected mechanisms in the battery could be measured. However, a clear pattern can be discerned depending on the region: The low frequencies (right side of the figure) show a diffusion process combined with a charge transfer pattern corresponding to one or both electrodes. Middle frequencies (middle of the figure) show a semi-circle on the Nyquist curve, corresponding to either SEI or separator. Finally, the high frequencies (left side of the figure) show a resistive-inductive part, influenced by current conduction due to cylindrical cell geometry.  
Figure 6: Impedance spectrum measurements for cell one as a function of the number of cycles.  
Figure 7: Voltage discharging profile at the 400 th cycle.  
The ECM proposed in Figure 8a is used to model the behavior of the impedance spectrum: A combined electrodes' behavior described by a modified Randles Model is used to model both electrodes. In this case, the double-layer capacitor is changed to CPE element Q\({}_{\text{dl}}\), and the Warburg element is replaced with a CPE Q\({}_{\text{diff}}\), to account for diffusions from both electrodes. The separator is combined with the SEI, and Here the SEI is modeled as an RQ element. Finally, a series resistance (R\({}_{\text{s}}\)) and an inductance (L) are used to model the conductive part inside the battery and the electrolyte resistance.  
The fitting results show high similarity between the measured spectrum and the fitted spectrum, as shown by the example fitting in Figure 8a. Furthermore, a qualitative residual analysis based on the Equation (5) shows that the fitting had less than 0.4% relative deviation.  
\[\widetilde{Z}_{\text{rel}}=\frac{|Z_{\text{meas}}|-|Z_{\text{fit}}|}{|Z_{\text{ meas}}|} \tag{5}\]  
By closely examining the residual curve (see Figure 8b), the fitting seems to be subject to systematic deviations. The quantization error affects the low frequencies due to the limited capability of the measurement device [20]. The middle frequencies and high  
Figure 8: (**a**): Fitting result of the ECM model to the impedance spectrum of cell one, cycle 150 and (**b**): The residual of the fitted ECM model to impedance spectrum of cell one, cycle 150.  
frequencies show a systematic behavior in the residuals with an oscillating pattern. The oscillation corresponds to model deviations, which principally show the need for model extensions. This effect can be neglected for the investigation, as the residual amplitude is sufficient and less than 0.4%.
# A New Resource Allocation Protocol for the Backhaul of Underwater Cellular Wireless Networks
## 3 Description of UBSI Resource Allocation Protocol
### Protocol Messages and Procedures  
The UBSI resource allocation protocol consists of two phases: the initialization phase (denoted by \"\(IP\)\") and the normal operation phases (denoted by \"\(NP\)\"). In the view of resource allocation, the backhaul capacity of all UBSs are initially determined with respect to their initial traffic information in the initialization phase, while that of all UBSs is reallocated according to their current queue status.
# Reconstruction of Epidemiological Data in Hungary Using Stochastic Model Predictive Control
## 4 Statistical Analysis for Normally Distributed Model Parameters
### Closed-Loop Control Policy  
In the literature [44], the values of the optimal control input \(u\) are often searched as functions of the states as follows:  
\[\hat{u}_{k}=\mu_{k}^{u}-K(\mu_{k}^{x})(\hat{x}_{k}-\mu_{k}^{u}), \tag{12}\]  
where \(\mu_{k}^{u}\) are free decision variables and \(K\) is (not necessarily a closed-form) function of the expected state. Thus, the control input is inherently a random variable and is normally distributed as the state (11) itself is approximated by a Gaussian. If \(\Sigma_{k}^{x\theta}=(\Sigma_{k}^{gx})^{\top}\) denotes the covariance between \(\hat{x}_{k}\) and \(\hat{\theta}_{k}\), the joint distribution of \(\hat{x}_{k}\), \(\hat{u}_{k}\), and \(\hat{u}_{k}\) is:  
\[\begin{pmatrix}\hat{x}_{k}\\ \hat{u}_{k}\\ \hat{\theta}_{k}\end{pmatrix}\sim\mathcal{N}(\mu_{k},\Sigma_{k}),\ \text{where}\ \mu_{k}= \begin{pmatrix}\mu_{k}^{x}\\ \mu_{k}^{u}\\ \mu^{0}\end{pmatrix},\ \Sigma_{k}=\begin{pmatrix}\star\\ \Sigma_{k}^{gx}\\ \Sigma_{k}^{gx}\\ \end{pmatrix}\begin{pmatrix}\Sigma_{k}^{gx}\\ \Sigma_{k}^{gx}\\ 0\end{pmatrix}\begin{pmatrix}1-K^{\top}(\mu_{k}^{x})&0\\ 0&I\end{pmatrix}. \tag{13}\]  
**Remark 2** (Nonlinear state-dependent input policy).: _When \(K=0\), the optimal tracking problem is said to be an open-loop MPC problem [67], whereas \(K\
eq 0\) results in a so-called closed-loop MPC problem [51], where the optimal input policy is parameterized by the state. A stabilizing state feedback (12) is typically useful when the predicted states are random variables, and their actual realizations may deviate from the predicted expectations. When the prediction model is stochastic, a sequence of deterministic input values (\(K=0\)) may result in a diverging sequence of state variances, and hence in a conservative (overly cautious) prediction. When the input is parameterized by the state (\(K\
eq 0\)), the adaptability of the input may reduce the uncertainty of the predicted states significantly if the feedback function (12) is determined appropriately. In this sense, the gain function quantifies the trade-off between the uncertainty of the state and the input. Unfortunately, it is not straightforward to compute a stabilizing gain function \(K\) for the nominal model (6). Later, in Section 4.5, we demonstrate that a reference state trajectory (if available) makes it possible to compute the values of \(K\) separately in each operating reference state through a classical LTI state feedback approach, e.g., a pole placement or a linear quadratic regulator (LQR) design ([68] Section 6.4.2)._
# Highly Linear 2.6 GHz Band InGaP/GaAs HBT Power Amplifier IC Using a Dynamic Predistorter
## 4 Conclusions  
In this paper, a highly linear two-stage InGaP/GaAs PAIC using a dynamic predistorter was proposed for 5G small-cell applications. The proposed predistorter is based on a diode-connected transistor placed in parallel at the inter-stage network. The AM\(-\)AM and AM\(-\)PM characteristics of the predistorter can be dynamically controlled and optimized to effectively compensate for the nonlinearities of the two-stage PA by adjusting the supply voltage to the diode and the passive elements surrounding the diode, such as a shunt capacitor at the base for degeneration and a series resistor and inductor at the emitter to couple the signal. Simulated dynamic characteristics were presented with various control parameter values. For verification purposes, a two-stage PAIC was designed and implemented using Winsemiconductor's 2 \(\upmu\)m InGaP/GaAs HBT process for the 2.55-2.65 GHz band. It was mounted on a four-layer laminate module measuring 5 \(\times\) 5 mm\({}^{2}\). Employing a 5G NR signal with a bandwidth of 100 MHz and a PAPR of 9.7 dB, the system achieved a PAE of at least 11.8% and an average output power of at least 24 dBm, with an ACLR of \(-\)40.0 dBc using a predistorter. The use of the predistorter improved the ACLR from approximately \(-\)34.4 dBc to \(-\)40.0 dBc. Furthermore, the predistorter enhanced the average output power by about 4 dB while maintaining an ACLR level of \(-\)40 dBc.  
Conceptualization, H.J.; methodology, H.J. and J.S.; software, H.J., W.C., and G.M.; validation, H.J., S.B. ([PERSON]), and Y.Y.; formal analysis, H.J., [PERSON], and [PERSON]; investigation, [PERSON], [PERSON] ([PERSON]), and [PERSON]; resources, K.C.H.; data curation, K.-Y.L.; writing--original draft preparation, H.J.; writing--review and editing, K.C.H., K.-Y.L., and Y.Y.; visualization, H.J. and S.K.; supervision, Y.Y.; project administration, Y.Y.; funding acquisition, Y.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Technological Innovation R&D Program (SCALEUP TIPS, Tech Investor Program for Scaleup) (RS-2024-00508510) funded by the Ministry of SMEs and Startups (MSS, Republic of Korea).  
Data Availability Statement: Data are contained within the article.  
Conflicts of Interest: Authors [PERSON], [PERSON] and [PERSON] were employed by the company para-PA Inc. Authors [PERSON] and [PERSON] were employed by the company Samsung Electronics Co. Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Study of Mesh Pattern for Optically Transparent Flexible Antenna with Feedline
## 3 Meshed Feedline and Antenna
### Meshed Feed Line
#### 3.1.1 Square-Meshed Feedline  
Figure 4a shows the change in the loss factor when the mesh width \(w_{s}\) was fixed at 0.1 mm and the size of the open space of the rectangular mesh increases from 0.6 to 2 mm within the range of 70 to 90% of the OT. It shows that the loss factor increases proportionally as the size of the open space of the rectangular mesh increases. Figure 4b shows the change of loss factor according to mesh width change. The square mesh was simulated by varying the mesh width from 0.05 to 0.17 mm with 70-90% OT and the open space fixed \(g_{s}\) to 1 mm. Since solid CPW has a loss factor of 0.063, meshed CPW has a higher loss factor due to the effect of higher sheet resistance than the solid CPW generated as an open space for transparency. As the open space increases, the cross-sectional area of the transmission line through which the signal propagates becomes smaller, so more conductor loss is inevitable. In terms of current path, a roundabout current path generated by open space causes more loss as the total path of current increases. A comparison of the simulated and measured results is plotted in Figure 5. A square-meshed CPW with about 83% OT with \(g_{s}=1.1\) mm and \(w_{s}=0.1\) mm was compared with solid CPW. Although the loss factors are 0.071 and 0.063, respectively, considering the transmission line length of 40 mm, there is no significant difference, so similar S-parameter results were obtained, and it was confirmed that square-meshed line works properly through the measurement results.  
Figure 3: Meshed CPW configuration: (**a**) square-meshed CPW; (**b**) diamond-meshed CPW; (**c**) side view of meshed line.
## 4 Results  
The obtained BeiDou-3 and BeiDou-2 satellite orbits in this study are assessed in terms of both internal consistency and external validation. For internal consistency, overlap comparisons are conducted. For external validation, satellite laser ranging (SLR) observations are used to independently assess the accuracy of the BeiDou-3 satellite orbits.
# Grating-Assisted Fiber to Chip Coupling for SOI Photonic Circuits
###### Abstract  
Fiber to chip coupling is a critical aspect of any integrated photonic circuit. In terms of ease of fabrication as well as wafer-scale testability, surface grating couplers are by far the most preferred scheme of the coupling to integrated circuits. In the past decade, considerable effort has been made for designing efficient grating couplers on Silicon-on-Insulator (SOI) and other allied photonic platforms. Highly efficient grating couplers with sub-dB coupling performance have now been demonstrated. In this article, we review the recent advances made to develop grating coupler designs for a variety of applications on SOI platform. We begin with a basic overview of design methodology involving both shallow etched gratings and the emerging field of subwavelength gratings. The feasibility of reducing footprint by way of incorporating compact tapers is also explored. We also discuss novel grating designs like polarization diversity as well as dual band couplers. Lastly, a brief description of various packaging and wafer-scale testing schemes available for fiber-chip couplers is elaborated.  
Keywords: grating couplers; SOI; subwavelength gratings; compact tapers +
Footnote †: journal: Applied Sciences
## 3 Fusion Prediction Based on Data Augmentation
### Fusion Method Based on Error Covariance Intersection  
Multiple models are trained in correspondence with the augmentation sets. The models can extract the different features of the original data by expanding with the random probability mechanism. In the practical prediction, multiple models are applied simultaneously, and the outputs should be fused for the final output. In the fusion, the prediction ability and performance of the models should be regarded as the pivotal factors. Then, the fusion method is introduced based on the covariance intersection of the network errors.  
The prediction errors in the networks are set as the essential information to evaluate the network performance. In the augmentation set, parts of the data train the network, while others are set as the validation reference. The prediction error can be calculated with the validation data, providing the basis for the fusion weight. The validation data are \(y_{i}^{i}\), and the predicted results are \(\hat{y}_{i}^{i}\), where \(t\) is the time step, n is n time steps, and \(i\) is the serial number of the augmentation and network. For each network, the estimation variance, \(p_{i}\), is calculated as  
\[p_{i}=\sqrt{\sum_{t=1}^{n}\left(y_{t}^{i}-\hat{y}_{t}^{i}\right)^{2}}\bigg{/}n \tag{5}\]  
and the covariance of all networks, \(p\), is  
\[p^{-1}=\sum_{i=1}^{m}\omega_{i}p_{i}^{-1} \tag{6}\]  
where \(\omega\) is the weight of the network, and \(m\) is the number of networks. The covariance is expected to be minimal, which means the estimation is balanced with the steady effect. Then, the calculation of the covariance can be converted to the optimization problem, and the model is expressed as  
\[\begin{split}&\min_{\omega_{i}}p^{-1}=\sum\limits_{i=1}^{m}\omega_{i}p_ {i}^{-1}\\ & s.t.\bigg{\{}\begin{array}{l}\omega_{i}\in[0,1]\\ \sum_{i}^{m}\omega_{i}=1\end{array} \tag{7}\]  
Various methods can be selected for the optimization problem with the existing research [38]. Sequential least square is used to optimize \(p^{-1}\) in this paper. Then, the fusion result, \(\hat{y}\), can be obtained with the optimization resolution:  
\[p^{-1}\hat{y}_{t}=\sum_{i=1}^{m}\omega_{i}p_{i}^{-1}\hat{y}_{t}^{i} \tag{8}\]  
The final prediction result can be achieved with the framework and algorithms above. The original small samples are augmented to generate the long-term time series. The augmentation should be conducted with the algorithm in Section 3.2. Then, multiple networks are trained with the decomposition of the augmentation sets and GRU models. Finally, the fusion solution is presented by assessing the prediction error with the covariance intersection optimization.
## 4 Results
### Evaluation Methods
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metrics** & **Description** & **No of Studies** & **References** \\ \hline \multirow{2}{*}{MAE} & It measures the deviation of recommendations based & & [2; 24; 49; 62; 72; 76; 81; 97; 98; 132; 141] \\  & on user-specified rating values. & & \\ \hline RMSE & It measures the accuracy of rating predictions. & 17 & [2; 3; 5, 11; 22; 24; 49; 62; 69; 78; 81; 92; 101; 128; 143; 144] \\ \hline \multirow{2}{*}{Precision} & It measures the fraction of the retrieved & & [5; 8, 12; 23; 69; 80; 84; 87; 92; 95; 99; 119; 123; 133; 135; 138, \\  & recommendations that are relevant & & 144–146] \\ \hline \multirow{2}{*}{Recall} & It measures the fraction of recommendations that & 25 & [5; 8, 9; 23; 36; 87; 62; 70; 73; 74; 78; 87; 89; 92; 95; 99; 110; 112, \\  & It measures the harmonic mean of recall and precision & & 119, 120; 124; 134; 136; 145; 146] \\ \hline \multirow{2}{*}{MAP} & It measures the mean precision values in the least & & [10, 62, 64; 67; 84; 87; 95; 124; 134] \\  & ranks for all relevant recommendations. & 3 & [70; 75; 136] \\ \hline \multirow{2}{*}{MRR} & It measures the list of possible recommendation, & & \\  & ordered based on the probability of correctness. & 5 & [12; 36; 44; 116; 119] \\ \hline \multirow{2}{*}{nDCG} & It measures the accuracy of top-K recommendations. & 11 & [4, 5, 10, 65; 87; 68; 75; 82; 99; 110; 136] \\ \hline \multirow{2}{*}{Diversity} & It measures the diversity of recommended items & 4 & [64; 7; 140; 147] \\ \hline \multirow{2}{*}{CNR} & It measures the number of recommendations & 4 & [77; 140; 147; 148] \\  & eventually clicked & & [7, 48; 108] \\ \hline \multirow{2}{*}{Robustness} & It measures the robustness of recommendation & 1 & [114] \\ \hline \multirow{2}{*}{Others} & & 9 & [12; 63; 65; 77; 80; 87; 98; 105; 145] \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Distribution of the Evaluation Methods.