esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Weather Forecasting Integrating LSTM and GA
## 4 Results and Discussion
### Forecasting with Regression Models
\[T_{MED}=-0.1887+1.0190\cdot T_{MED1}-0.0032\cdot RG_{TOT1} \tag{12}\]  
\[DD_{MED}=96.7312+24.9277\cdot FF_{MED1} \tag{13}\]  
\[FF_{MED}=-7.2055+0.0124\cdot DD_{MED1}-7.7472\cdot FF_{MED1} \tag{14}\]  
\[FF_{MAX}=-5.3412+7.277\cdot FF_{MED1}+1.6848\cdot FF_{MAX1} \tag{15}\]  
\[RG_{TOT}=-22.3257+1.1333\cdot T_{MED1}+0.0957\cdot DD_{MED1}+0.4822\cdot RG_{TOT1} \tag{16}\]  
where \(T_{MED}\) is the predicted average temperature, \(DD_{MED}\) is the predicted average wind direction, \(FF_{MED}\) is the predicted average wind speed, \(FF_{MAX}\) is the predicted maximum instant wind speed, and \(RG_{TOT}\) is the predicted solar radiation for Quinta de Santa Barbara, and \(TD_{MED1}\), \(DD_{MED1}\),\(FF_{MED1}\), \(FF_{MAX1}\), and \(RG_{TOT1}\) are the correspondent weather variables observed in Pinhao. Finally, the input dataset was divided into training, validation, and testing sets. The main purpose of selecting these datasets is to assess the accuracy of the models' performance and also to ensure that they can be generalized to new data. For the variables under study, the values were divided into training, validation, and testing sets, with a split of 70%, 15%, and 15%, respectively, as shown in Figure 5.
## 4 Review of Microgrid Control and Design
### Conventional and Advanced Control Techniques in Microgrids
#### 4.6.4 Model Predictive Control (MPC)  
MPC algorithms are improved to anticipate reference signals. One of the key benefits of these control methods is their capacity to lower tracking errors. In their study [118], [PERSON] et al. introduced a voltage predictive control strategy for autonomous MGs, utilizing an estimator for each energy resource to acquire a voltage response independently of a communication link. The proposed control strategy involves both an estimator and a predictive controller for voltage control. Employing small-signal analysis, this approach can effectively execute offset voltage control without relying on any communication link. Furthermore, it demonstrates resilience against parametric uncertainties such as disturbances in line parameters, variations in load parameters, disruptions in output impedance, and faults in the energy resources. Another notable contribution in the field of autonomous MGs is the optimal predictive control scheme presented by [PERSON] et al. [119]. This scheme employs a nonlinear predictive control algorithm to manage a dataset encompassing the state of charge of batteries, energy resource production, and predicted load. The methodology preserves an appropriate equilibrium between power generation and consumption and maintains the voltage amplitude within the allowable deviation range.
# Predicting Freeway Travel Time Using Multiple-Source Heterogeneous Data Integration
## Data Collection and Preprocessing
### Data Preprocessing  
Many abnormal data items were found in the data, which need to be preprocessed before use.  
Data sharing the same entry and exit toll  
On the freeway, some drivers turn around in the service area or other sections to avoid the charges and even exchange the toll tags, which is likely to make the entry and exit of the vehicles at toll stations consistent. Therefore, it is necessary to determine whether a data item is consistent with the toll gates and eliminate invalid data items.  
If a toll station's time system fails to synchronize or has a system failure, the time of accessing the toll station can be earlier than the time of exiting the toll station. In addition, there are other factors that can lead to long travel time, such as the breakdown of a vehicle on the road, accidents, and the situation where a driver may take a long rest in a service area. All of these situations will result in unusual time record data. During data preprocessing, abnormal time data records can be eliminated through screening.  
Figure 1: Layout of Freeway G5513 and toll stations.  
There are two main reasons for missing data: on one hand, it can be caused by equipment problems or the road environment, including a detector's unstable scanning frequency, faulty transmission equipment, and traffic jams; on the other hand, deleting unwanted data items may also erase accurate data. Lack of data will cause the road's real traffic conditions to change directly or indirectly. Therefore, it is essential to make up for the missing data. Because of the strong continuity of the traffic flow travel time parameter, the trend in the change of the traffic flow travel time parameter with time is consistent, although its fluctuation will change as the collection period changes. Therefore, the following data fill formula is obtained, as given in Equation (1).  
\[data(t)=\frac{2n}{n(n+1)}\times data(t-1)+\frac{2(n-1)}{n(n+1)}\times data(t-2) +\cdots+\frac{2}{n(n+1)}\times data(t-n) \tag{1}\]  
where, \(n\) is the number of data value taken to make up for missing data, \(data(t)\) represents the current missing data; \(data(t-1)\), \(data(t-2)\), and \(data(t-n)\) are the traffic flow travel time data of the past period, two cycles; and the \(n\)-th cycle are respectively represented. This method does not require the use of a historical database to extract historical trend data.
## 4 The Performance Parameters and Application Scenarios of 6G Networks
### Application Scenarios
#### 4.2.8 Wireless Tactile Network  
Perception and connection are the main areas of interest for the IoT in the current 5G network. In the future, the 6G network connection's aim will change to a single, intelligent purpose. Rather than only focusing on perception, the 6G network's connection and communication dynamic will also include real-time control and reaction, resulting in the \"tactile Internet\". This is the name given to a communication network that can send real-time control, touch, and sensing/driving data.  
The tactile Internet is described as a real-time network or virtual object network intended for remote access, perception, or control by the IEEE P1918.1 standard working group. The tactile Internet has a larger function than the standard Internet, which mainly enables the exchange of data and information. Along with handling information transfer remotely, it also has mechanisms for remote control and reaction that are matched to the transmission of data and information. With this, there will be a major change from content distribution to remote skill delivery.  
Three primary components define the tactile Internet: ultra-real-time response infrastructure and network for remote control, integration of control and communication, and physical real-time interaction (allowing real-time access to people and machines, operating and controlling objects in a sensed manner).
###### Abstract  
The logic structures implemented in Field Programmable Gate Arrays (FPGAs) are often critical and their correct operation is vital. FPGA devices are often used in areas where there is increased ionising radiation (space, medical diagnostics, aviation or nuclear power). There is therefore a need for mechanisms to correct radiation-induced errors. A common approach is the redundant implementation of particularly critical parts of the logic structure. By triplicating selected fragments, it is possible not only to detect potential errors but also to correct them. Such an approach is called triple modular redundancy (TMR), and its essence lies in the use of specialised voting circuits called voters, which allow the erroneous results of individual subcircuits to be eliminated by voting. The triplicate circuit under consideration, together with the voter, constitutes the mitigation structure. It becomes necessary to develop a test environment to assess the correct operation of these circuits. Also key is the efficiency of the implementation of these structures, which can be related to the occupation of logical resources or the power consumption of a given implementation. This paper demonstrates the essence of implementing a test environment to test the correctness of the mitigation of logic structures using TMR voters. An error injector mechanism using the Pseudo-Random Bit Sequence (PRBS) register is proposed, which introduces an element of randomness into the testing process. The aim of this research is to determine the implementation efficiency of the proposed test environment. In the experimental part, the implementation costs of the proposed solution were examined. The results indicate that between 66 and 109 LUT blocks were required to implement the error injector, corresponding to a relatively small increase in dynamic power consumption: by 22% for combinational circuits and by 37% for sequential circuits.  
TMR; voter; mitigation; technology mapping Article
# Optimal Position and Target Rate for Covert Communication in UAV-Assisted Uplink RSMA Systems
###### Abstract  
With the explosive increase in demand for wireless communication, the issue of wireless communication security has also become a growing concern. In this paper, we investigate a novel covert communication for unmanned aerial vehicle (UAV)-assisted uplink rate-splitting multiple access (RSMA) systems, where a UAV adopts the rate-splitting (RS) strategy to increase the total transmission rate while avoiding deteriorating the covert transmission of a ground user. In the proposed system, a ground user and a UAV adopt the RSMA scheme to simultaneously communicate with a base station surveilled by an evil monitor. The UAV acts as both the transmitter and the friendly jammer to cover the ground user's transmission with random power. To maximize the expected sum rate (ESR), we first study the RS strategy and obtain the optimal power allocation factor. Then, the closed-form of minimum detection error probability (DEP), ESR, and optimal target rate of the UAV are derived. Constrained by the minimum DEP and expected covert rate (ECR), we maximize the ESR by optimizing the position and target rate of the UAV. Numerical results show that the proposed scheme outperforms the traditional NOMA systems in terms of ESR with the same DEP and ECR.  
covert communication; RSMA; UAV; sum rate +
Footnote †: journal: Information Letters  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON] Optimal Position and Target Rate for Covert  
**Key words:** covert communication; RSMA; UAV; sum rate
## 4 Performance Evaluation
### Comparison of Results with State-of-the-Art Models  
This section provides a comparison between the performance of our proposed model and previous research on multiclass classification, utilizing the \(D1\) and \(D2\) datasets. The  
Figure 7: Multiclass confusion matrix for \(D2\). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifier** & **Category of Attack** & **Precision** & **Recall** & **F1 Score** \\ \hline \multirow{8}{*}{**Proposed model (MSE)**} & Benign & 99.86 & 99.79 & 99.83 \\  & Bot & 92.92 & 73.91 & 82.33 \\  & Ddo & 99.98 & 99.93 & 99.95 \\  & DoS GoldenEye & 99.85 & 99.46 & 99.65 \\  & DoS flukl & 99.78 & 99.93 & 99.86 \\  & DoS Slowhtpttest & 99.09 & 99.09 & 99.09 \\  & DoS slowloris & 99.65 & 99.82 & 99.74 \\  & FTP-Patator & 99.93 & 99.87 & 99.90 \\  & Heartbleed & 100 & 100 & 100 \\  & Infiltration & 100 & 71.42 & 83.33 \\  & PortScan & 97.48 & 98.56 & 98.01 \\  & SSH-Patator & 99.40 & 99.74 & 99.57 \\  & Web Attack—Brute Force & 73.27 & 81.06 & 76.97 \\  & Web Attack—SQL Injection & 80 & 100 & 88.88 \\  & Web Attack—XSS & 42.45 & 34.61 & 38.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: _Cont._evaluation metrics are outlined in Tables 9 and 10. Table 9 illustrates the multiclass classification results of our model alongside those of recent IDS research for \(D1\), while Table 10 presents the performance for \(D2\). The results in Table 9 underscore the effective overall performance of our proposed model relative to previous studies. For multiclass classification on \(D1\), our model achieves an accuracy of 90.29%, a recall of 90.29%, a precision of 90.45%, and an F1 score of 89.87%. Moving to \(D2\), the multiclass classification accuracy reaches an impressive 99.70%, with the precision, recall, and F1 score all at 99.70%. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & \multirow{2}{*}{**Dataset**} & \
\begin{tabular}{c} **Dimensionality** \\ **Reduction** \\ **Techniques** \\ \end{tabular} &
\begin{tabular}{c} **Classification** \\ **Techniques** \\ \end{tabular} & **Accuracy (\%)** & **Recall (\%)** & **Precision (\%)** & **F1 Score (\%)** \\ \hline
[86] & D2 & N/A & Stacking (CNN, DNN, LSTM, MLP) & 98.7 & 98.7 & 98.7 & 98.6 \\
[80] & D2 & CNN & BLSTM & 98 & 98 & 99 & 98 \\  & & Global and local & & & & \\  & & subnet parallel & & & & & \\
[87] & D2 & Greedy-RFECV & Deep Neural & 85 & N/A & N/A & 86 \\  & & Network & & & & \\  & & (CGL-DNN) & & & & & \\  & & Triple-layered & & & & \\
[88] & D2 & Chi-squared & hybrid (weighted & & & & \\  & & DNN, CNN+LSTM, XGBoost) & & & & & \\
**This study** & **D2** & **LogitBoost-ResNet** & **MSE** & **99.69** & **99.69** & **99.69** & **99.69** \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Performance metrics comparison between our proposed model and recent IDS models for multiclass classification on the CICIDS2017 dataset (bold indicates best results).
# An Improved CASA Model for Estimating Winter Wheat Yield from Remote Sensing Images
## 3 Study Methods
### Determination of Absorbed Photosynthetically Active Radiation  
The absorbed photosynthetically active radiation by actual vegetation (APAR) is mainly related to two factors: the total solar radiation SOL and the fraction of absorption by vegetation to APAR. This is calculated as follows:  
\[\text{APAR}(x,t)=\frac{1}{2}\times SOL(x,t)\times\text{fPAR}(x,t), \tag{2}\]  
where \(SOL(x,t)\) [MJ m\({}^{-2}\)] is the total solar radiation of pixel \(x\) at time \(t\), fPAR(\(x\), \(t\)) is the fraction of absorbed photosynthetically active radiation, and the factor \(\frac{1}{2}\) is the ratio of effective solar radiation that can be used by vegetation to the total solar radiation.
At the microscopic level, these changes are manifested as the intercalation and deintercalation of lithium ions between the cathode and anode. At the macroscopic level, they are reflected as the relative expansion between the cathode and anode. This expansion is primarily governed by the anode [19; 20]. For LFP batteries, the change in force during the discharge test is significant due to contraction at low and high SOE values and expansion at moderate SOE values. The flat open-circuit voltage in traditional methods reduces the accuracy of SOE estimation. This problem can be effectively addressed by integrating force signals with deep learning approaches. Considering the above studies, this study proposes a force-based fusion model. This model integrates the bidirectional long short-term memory (BiLSTM) network, particle swarm optimization (PSO), and Kalman filter (KF) algorithm for the estimation of the SOE of LFP batteries. The main contributions of this study include the following. 1. A sliding window is proposed to make full use of past measurements containing voltage, temperature, current, and mechanical force. 2. A BiLSTM with four multiphysics feature inputs is used to obtain preliminary estimation of SOE. The PSO algorithm is introduced to optimize the noise covariance of the KF, thereby minimizing manual parameter adjustments. The optimized KF algorithm then smooths the outputs of BiLSTM, achieving enhanced estimation precision and effectively mitigating noise interference. 3. A test bench is established to acquire multiphysics features, including current, voltage, temperature, and mechanical force. The force signal is introduced as a model input feature, which significantly improves the estimation accuracy of SOE during the voltage plateau period. 4. The validity of the proposed model and mechanical force signals in high-precision SOE prediction is verified by applying the proposed method to different operating conditions and temperatures. In addition, the comparison of BiLSTM, PSO, and KF (BiLSTM-PSO-KF) with other networks and the prediction results under different preload forces further validate the robustness and generalization performance of the proposed model. The remainder of this paper is organized as follows. Section 2 presents the experimental procedure, the test bench, and the analysis of the measurements. Section 3 explains the fundamental principles of BiLSTM, PSO, and KF. The hyperparameters' design and evaluation criteria of the model are also described in detail in this section. Section 4 presents the SOE estimation results under various conditions. Section 5 summarizes the content of this study.
## 3 Method
The noise due to this etalon can be made lower than that caused by other sources by precision adjustment of the light path. Therefore, the effect of the etalon is not considered in this paper. The 2f signal obtained by modulating light absorbed by gas includes the background part independent of absorption, which is related to the nonlinear tuning coefficient \(K_{2}\), the nonlinear modulation phase difference \(\varphi_{2}\), and the absorption part, which relies on the gas absorption and laser characteristics. The 2f signal can be expressed as follows:  
\[S_{2}(v_{1},\delta_{v})=S_{A}(v_{1},\delta_{v})+S_{B}(v_{1},\delta_{v}) \tag{5}\]  
\[S_{A}(v_{1},\delta_{v})=\beta I_{0}K_{2}[\delta_{v}]^{2}\text{cos}\ \varphi_{2} \tag{6}\]  
\[S_{B}(v_{1},\delta_{v})=-\beta\alpha_{0}I_{0}\left[g_{2}+0.5(g_{1}+g_{3})K_{1 }\delta_{v}\text{cos}\ \varphi_{1}+0.5(2g_{0}+g_{4})\right]K_{2}[\delta_{v}]^{2}\text{cos}\ \varphi_{2}] \tag{7}\]  
where \(S_{A}(v_{1},\delta_{v})\) is the absorption part; \(S_{B}(v_{1},\delta_{v})\) is the background part; \(g_{0}\) is the lineshape function; and \(g_{1}\), \(g_{2}\), \(g_{3}\), and \(g_{4}\) are the first, second, third, and fourth derivatives of the lineshape function, respectively. The 2f signal is composed of four parts, which includes a background signal independent of absorption, a waveform distortion term caused by linear intensity modulation of the laser, a baseline shift term caused by the nonlinear intensity modulation of the laser, and the 2f main term which is proportional to the second derivative of the lineshape. The broadening, symmetry, and baseline levels of the 2f signal have a great impact on overlapping absorption lines, leading to measurement errors. To explain the effect of gas temperature on the full width at half-maximum (FWHM), the Gaussian function was chosen as the lineshape function in this paper. It can be expressed as follows [23]:  
\[g(v)=1/\gamma_{\text{D}}\cdot\text{sqrt}\cdot\text{exp}\left[-\text{ln}2\left( v-v_{0}\right)^{2}/\gamma_{\text{D}}{}^{2}\right] \tag{8}\]  
where \(\gamma_{\text{D}}\) is FWHM and \(v_{0}\) is the center frequency of the absorption line. \(\gamma_{\text{D}}\) is expressed as:  
\[\gamma_{\text{D}}=7.16\times 10^{-7}\ v_{0}\ \text{sqrt}(T/M) \tag{9}\]  
where \(M\) is gas molecular mass. The FWHM depends only on \(T\) under low-pressure conditions. The 2f waveform is recovered using the above principle. Finally, the principle of fast multi-peak fitting is introduced. According to the position of the absorption line and the actual 2f waveform, the interference of overlapping spectral lines is removed directly.
## 3 Dataset and Network Training
### Analysis of Experimental Results  
We used the UA-CAR data sets to perform the testing experiments, analyze the experimental data, and compare the experimental results to verify the effectiveness of the optimization. In the process of traffic flow monitoring, there were missed detections and false detections, so we used the precision and recall rate evaluation model. The precision rate was defined as the number of correctly detected statistical vehicles divided by the total number of detected statistical vehicles. The recall rate was defined as the correct number of statistical vehicles divided by the number of vehicles in the data set. The increase in the statistics of correct vehicle detection could improve the recall rate, but, as vehicle misdetection rate increased, the detection's precision would be decreased. On the contrary, the reduction in the number of false vehicle detection was conducive to improving the detection's precision. However, the algorithm could only reduce the number of false vehicle detection when strict judgment conditions were set, but at the same time, it also led to a reduction in the correct statistics of the vehicle, resulting in a lower recall rate. For that, the precision and recall rates showed opposite trends.
# Cascaded Frequency Selective Surfaces with Matryoshka Geometry for Ultra-Wideband Bandwidth
## 4 Results Analysis
### Comparisons of Experimental and Simulated Results
The simulated and experimental frequency responses of cascaded FSS comparison can be analyzed by observing Figures 27-29 for the gaps of 4 mm, 8 mm, and 12 mm between the layers, respectively. In Figure 27, we observe that the bandwidths below \(-10\) dB align from 2.0 GHz to 3.2 GHz between the measured and simulated results, allowing a large bandwidth of 1.2 GHz with 4 mm between the layers. Although the measured results did not reach the simulation intensity peak at \(-40\) dB, this was expected since the simulation has ideal values and an infinite plane. The frequency response values in the range from 2.0 GHz to 3.2 GHz remained around \(-20\) dB for more than half of the frequencies. Observing Figure 28, the frequency responses between 1.8 GHz and 2 GHz for the \(x\) polarization are close to \(-10\) dB, especially for the peak at 2.5 GHz. This makes it difficult to determine the bandwidths. The \(y\) polarization starts to show the possibility of a 2 GHz range, but because its peaks are too low, using this spacing would not present a stable bandwidth. In Figure 29, there is a clear ultra-bandwidth for the \(y\) polarization, with the range between 1.8 GHz and 3.8 GHz being below \(-10\) dB and up to \(-15\) dB at most of its frequencies. The range from 1.8 GHz to 3.0 GHz in the \(x\) polarization is also more distinct because there is a shift of about \(-5\) dB between the 8 mm and 12 mm spacings, which made 2 GHz UWB possible. Compared to the simulated results, the bandwidth below \(-10\) dB in the measured results was expected to start at 1.6 GHz. This shift can be explained by analyzing the responses of single-layer FSSs, where the first resonance frequencies were shifted to the right at the first resonance. There were discrepancies, and although the cascaded FSS maintains similar resonance frequencies in experimental and simulated frequency responses, the measured values of \(\mid\)S(2,1)\(\mid\) at these frequencies do not show a decay as sharp as the simulated values. These differences in intensity are due to the fact that the ANSYS simulation uses numerical methods and does not take into account factors such as antenna limits, connector and cable wear, and edge effects. Further experiments and simulations showed that most of the discrepancies were due to the alignment of the FSSs. By creating overlaps between the geometries of the layers, the resonance frequencies affected each other and shifted from the intended ones. Even with the non-ideal parallelism of the layers, it was possible to obtain a bandwidth of 2 GHz.