esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model
### Proposed Active RIS-UAV  
In the proposed active RIS-UAV relaying scheme, \(N\) active RIS elements are mounted on the UAV to assist the communication between BS and UE. The active RIS-UAV is responsible for amplifying and reflecting the received signal from the BS toward the UE. In this paper, we assume that the RIS elements reflect the signals independently [8]. In addition, the distance between BS and the \(n\)-th element of RIS is assumed to be approximately the same as the distance between BS and the UAV because the size of the RIS elements is much smaller than the wavelength. Similarly, assuming that the distance between the \(n\)-th element of RIS and UE is approximately the same as the distance between UAV and UE. Thus, the received signal at the UE after being amplified and reflected by the proposed active RIS-UAV is given as [26]:  
\[y_{act}=\sqrt{P_{bs}^{act}}\mathbf{h}_{rd}^{H}\mathbf{\Phi}_{act}\mathbf{h}_{sr }s+\mathbf{h}_{rd}^{H}\mathbf{\Phi}_{act}\mathbf{n}_{r}+n, \tag{12}\]  
where \(P_{bs}^{act}\) is the BS transmit power of the active RIS-UAV relaying scheme, \(s\) is the information signal transmitted by the BS, \(\mathbf{n}_{r}\sim CN\big{(}0,\sigma_{r}^{2}\mathbf{I}_{N}\big{)}\) and \(n\sim CN\big{(}0,\sigma^{2}\big{)}\) represent the thermal noise at the active elements and UE, respectively. In addition, the parameter \(\mathbf{\Phi}_{act}\) is the reflection matrix of the active elements, \(\mathbf{\Phi}_{act}=\text{diag}\big{\{}\rho_{1}e^{i\varphi_{1}},\dots,\rho_{n }e^{i\varphi_{n}}\big{\}}\), in which \(\rho_{n}\) and \(\varphi_{n}\) represent the amplification factor and the phase shift of the \(n\)-th RIS element, respectively, where \(\rho_{n}>1\) and \(\varphi_{n}\in[0,2\pi)\). For simplicity, \(\rho_{n}\) is assumed to be the same for all active elements, where \(\rho_{n}=\rho\). In general, the amplification gain is the extent to which the power amplifiers of the active elements of RIS-UAV boost the strength of a signal. Therefore, the amplification gain \(\rho^{2}\) is applied to offset the path loss between the BS and the RIS-UAV, such that the denominator is the channel coefficient and the effective noise term, while the numerator is the scaling constant. The amplification gain \(\rho^{2}\) can be formulated as follows [26]:  
\[\rho^{2}=\frac{P_{ris}^{act}}{N\big{(}P_{bs}^{act}h_{sr}^{2}+\sigma_{r}^{2} \big{)}}, \tag{13}\]  
where \(P_{ris}^{act}\) is the transmit power at the active RIS-UAV. Therefore, the SNR for the proposed active RIS-UAV relaying scheme is expressed as:  
\[\gamma_{act}=\frac{P_{bs}^{act}\mid\mathbf{h}_{rd}^{H}\mathbf{\Phi}_{act} \mathbf{h}_{sr}\mid^{2}}{\sigma_{r}^{2}\parallel\mathbf{h}_{rd}^{H}\mathbf{ \Phi}_{act}\parallel^{2}+\sigma^{2}}. \tag{14}\]  
We assume that all the reflected signals arrive in the same phase at the UE; hence, the simplified SNR is:  
\[\gamma_{act}=\frac{P_{bs}^{act}\rho^{2}N^{2}h_{sr}^{2}h_{rd}^{2}}{\rho^{2} \sigma_{r}^{2}Nh_{rd}^{2}+\sigma^{2}}. \tag{15}\]
## 3 Dual-Band Doherty Power Amplifier
### Dual-Band DPA Problems and Challenges  
The main problem for multi-band/multi-standard features is the RF-front end-stage [21; 22], where RF front-end components such as power amplifiers, filters, antennas, and switches should be designed to work properly on the specified frequency bands to fulfil each standard specification.  
According to [23], as long as there are dual-band components, it is possible to design a multiband DPA. Hence, theoretically, the DPA can be divided into sub-circuits to be analyzed and designed separately. These sub-circuits are the input power splitter, power amplifier design, output power combiner, and the offset lines.  
The input power splitter sub-circuit should be designed to achieve the same performance in both bands; a good solution is to use either a branch line coupler (BLC) or a Wilkinson divider, where both types can divide the input power equally or unequally. The main difference between the two types of splitters is the phase difference at the output; where the phase of the two output at Wilkinson will be same, for the branch line the phase difference between the outputs will be 90\({}^{\circ}\)[24]. Unfortunately, both types are narrow band solutions because they are frequency-dependent. To provide a dual-band power divider, a broadband power divider can be used to cover the dual-band and ensure that the matching condition is satisfied.  
Power amplifier design sub-circuit includes the device cell, input, and output matching networks; the device cell should be capable of working properly at each designed frequency band, which depends on the device characteristics as well as its designed input and output matching networks. In general, the input matching network is responsible for controlling the gain over the required band, whereas the output matching network is responsible for choosing the output power and efficiency. The simulation in [25] showed that the effect of the second harmonic at the input of the PA is negligible so that the input matching network can be designed only for the fundamental frequency at each band. At the same time, the third harmonic also has little effect on the output matching network; hence, the output matching design can consider only the fundamental frequency and the second harmonic at each band [25]. Nevertheless, modern design for matching networks does not depend on the fundamental frequency matching only; further harmonic effects can be included to achieve a certain performance.  
Moreover, by taking the source-pull and load-pull data with the harmonic effects of a transistor, the required impedance for the transistor can be matched so that good performance can be achieved.  
The main limitations of the DPA is the \(\uplambda\)/4 quarter wavelength (impedance inverter) and the offset lines which limit the bandwidth due to their natural characteristics [25; 26; 27; 28; 29; 30], so that to design a DPA that can work in dual-band, the quarter-wave impedance inverter and the offset line should be replaced with more complex networks which give equivalent functions in split bands.
# Continuous Latent Spaces Sampling for Graph Autoencoder
## 4 Present Work: colaGAE
### Encoder  
The colaGAE method we propose employs a series-encoder that produces a sequence of outputs, denoted as \(Z_{1},Z_{2},\cdots,Z_{n}\), where each encoder is trained with a distinct mapping function \(f_{i}\), as illustrated in Equation (1).  
\[Z_{i}=f_{i}(\mathcal{X}) \tag{1}\]  
The low-dimensional latent representations, denoted by \(\mathbf{Z}\), are subsequently inputted into the decoder to reconstruct the original graph, as described in Equation (2).  
\[\hat{G_{i}}=Dec(\mathbf{Z}_{i}) \tag{2}\]  
In this research, we discovered that the decoder's design plays a critical role in learning expressive and informative node representations. Because the goal of feature reconstruction is to learn such representations, rather than simply matching the encoded embeddings to the input node features, we opted for an MLP decoder. This type of decoder is more likely to reconstruct a node's original feature from its encoded embedding.  
The encoder in colaGAE consists of five different feature extractors, including GCN, which are widely used in GNN research and have demonstrated effectiveness in various graph-related tasks. Additionally, we included four linear mapping layers that produce node representations of the same shape. Although our framework can accommodate different encoder architectures, such as GraphSAGE and GAT, we chose GCN because of its simplicity, efficiency, and ability to address the scalability issue in pretraining large GNNs.
# Evaluation and Improvement of Internet Printing Protocol Based on HCPN Model Detection Method
## 7 Conclusions  
Guided by the theory of colored Petri nets and the improved Dolev-Yao attack method, this study used the model-detection tool, CPN Tools, to focus on the 0-RTT authentication process in the PSK mode of IPP. Through formal modeling and by introducing attacker models, the loophole of the original protocol was discovered. We then proposed a targeted improvement plan based on this vulnerability. Finally, the same attacker model was introduced again to analyze and verify the new scheme's security properties. We proved that, as compared to the original protocol scheme, the improved scheme resisted replay attacks by the adversary. In the future, we will consider using a stronger adversary model to verify the protocol process, try to find vulnerabilities not found in the current adversary model, and help protocol designers avoid potential security risks in advance.  
Conceptualization and methodology, T.F.; software, original draft preparation, formal analysis and investigation, data curation, writing and editing, H.-R.B.; review and supervision, T.F. All authors have read and agreed to the published version of the manuscript.  
This paper is supported by the National Natural Science Foundation of China (Grant No. 61762060).  
Not applicable.  
Data Availability Statement: Data are contained within the article. Original program data are available upon request: [PERSON]DRESS].  
We acknowledge the material support given by [PERSON].  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AEAD & Authenticated Encryption with Associated Data \\ CUPS & Common UNIX Printing System \\ ECDHE & Elliptic Curve Diffie-Hellman Ephemeral \\ ED & Early Data \\ EDK & Early Data Key \\ GSSAPI & Generic Security Service Application Program Interface \\ HCPN & Hierarchical Colored Petri Nets \\ HKDF & HMAC-Based Key Derivation Function \\ IPP & Internet Printing Protocol \\ ISTO & Industry Standards and Technology Organization \\ LDP & Line Printer Daemon protocol \\ MAC & Message Authentication Code \\ PSK & Pre-Shared key \\ PWG & Printer Working Group \\ RTT & Round Trip Time \\ \end{tabular}
## 2 Cooperative V2X Traffic Surveillance: Background and Related Work
### Related Work
To obtain appropriate and valid results with such a protocol, two assumptions are typically adopted: (i) a _closure assumption_ dictates that the population should remain stable while the estimation samplings are performed; and (ii) a _homogeneity assumption_ requires that the probability of being counted is equal for every node in the observed population. In the VANET scenario, the first assumption can be considered satisfied if the time between two samplings is small enough, while the second assumption is not accurately met because the message reception probability is significantly affected by the distance between sender and receiver, by their position, and by the road topology, among the others. Given the above limitations, to obtain a valid estimation even when there are variations in the capture probability, [17], studies the performance of another protocol, characterized by a non-parametric estimation model designed to work under various capture probability distributions, which is based on the Jackknife estimators. In particular, the estimation of the population size is obtained through the collection of the identifiers of the captured vehicles (for example, their MAC addresses) in independent sets over multiple independent sampling. After \(t\) collection rounds the protocol computes the total number of different sampled nodes, i.e., \(S\). This parameter is biased, with its bias decreasing with the increase of \(t\). To remove the bias of \(S\), a series of estimators are used, which take into account the number of times the vehicles are captured in \(t\) successive rounds. Different approximations are possible, depending on the specific estimator used, called \(N1\) to \(N5\), which have increasingly less bias but higher mean square error. Additional details about the associated algorithm can be found in [17; 19].
# Analysis of Fine Crack Images Using Image Processing Technique and High-Resolution Camera
## 4 Measurement Results and Analysis
### Analysis of the Causes of Crack Measurement Errors
At a working distance of 470 mm, the actual pixel values at focal lengths of 50 mm and 100 mm should be 12.69 and 28.08, respectively, but the values obtained through measurement were 16 and 36, respectively. For example, in the case of a certain section that is from 3000 mm to 4000 mm, two-pixel values were measured at 50 mm focal length in the entire section. As can be seen in Figure 15b, the number of pixel values was maintained at two even though the \(GSD\) continued. This is judged to be the cause of the changes in the crack value measured in the section. Given these results, it is considered that the errors occur because of the crack value measured in the form of a positive integer rounded to the nearest integer of the actual crack value, the \(GSD\) that increases proportionally as the working distance increases, and the repeating number of pixels. Although the resolution, thresholding process during image processing, and mixed pixels are important elements in crack measurement, measured pixel values are considered to have the greatest effects as they affect the entire section, and they are judged to be a limitation in measurement using imaging equipment. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Classification**} & \multicolumn{2}{c}{**Working Position (Error = 0)**} & \multicolumn{1}{c}{**Unit: mm**} \\ \cline{2-4}  & \multicolumn{2}{c}{**Width (mm)**} \\ \cline{2-4}  & **0.1 mm** & **0.3 mm** & **0.5 mm** \\ \hline \multirow{4}{*}{Focal length} & \multirow{2}{*}{50 mm} & 490, 710, 1370 & 410, 490, 710, 1370, 2030, 4010 & 490, 600, 650, 710, 1150, 1700, 2250, 3350, 6650 \\ \cline{2-4}  & & & 2030, 4010 & 1150, 1700, 2250, 3350, 6650 \\ \cline{2-4}  & \multirow{2}{*}{100 mm} & 540, 760, 980, 1420, 2740 & 540, 760, 820, 980, 1090, 1420, 2080, 2740, 4060, 8020 & 540, 650, 700, 760, 980, 1200, 1300, 1420, 1750, 2300, 2740, 3400, 4500, 6700, 13,300 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Position of shooting with zero error of measurement width value. Figure 15: (a) The actual number of pixels of a 0.5 mm wide virtual image and the measured width value based on the measured pixel value; (b) \(GSD\) value at 50 mm and 100 mm focal lengths according to working distance.
# Enhancing PAPR and Throughput for DFT-s-OFDM System Using FTN and IOTA Filtering
## 1 Introduction
[22] proposes a deep learning-aided FTN signal detection method for an uplink multi-user transmission scenario. Despite its throughput gain, the major drawback of FTN is that the accelerated symbols will introduce additional inter-symbol interference (ISI), which degrades the transmission reliability [23]. The proposed method provides a candidate solution for jointly enhancing the performance of PAPR and throughput. We first increase the spectral efficiency by integrating FTN with the low-PAPR DFT-s-OFDM. At the same time, we design an IOTA shaping filter to reduce the ISI. The proposed method is simple but efficient, which can improve the performance of PAPR and throughput simultaneously. In this paper, to enhance the performances of PAPR and throughput simultaneously, we consider the incorporation of FTN with DFT-s-OFDM and aim to tackle the problem of additional ISI caused by FTN. Specifically, we propose an FTN-DFT-s-OFDM waveform based on an isotropic orthogonal transform algorithm (IOTA) filter, which can lower the ISI while keeping the spectral efficiency enhanced. Good focusing ability on both time and frequency domain can be found in IOTA filters, which is vital for reducing the ISI and ICI [24]. Besides, experimental results have shown that IOTA filters help in obtaining higher computational efficiency [25]. By combining the above-mentioned ingredients, the detailed design of the proposed transmission waveform is presented in the following sections. The low ISI characteristic also makes the low-complexity frequency-domain equalization-based method available for reliable detection. Monte Carlo simulation is performed to validate that the joint benefits of deploying IOTA and FTN technologies in DFT-s-OFDM can be obtained with respect to PAPR, bit error rate (BER) and throughput. Different modulation coding schemes (MCSs) have been simulated, where up to 3.5 dB PAPR gain, 50% throughput gain in the high SNR region, can be achieved by the proposed scheme, compared to conventional waveforms. The rest of this paper is organized as follows. Section 2 of this paper illustrates the signal model and performance metrics used in the simulation process. Next, the proposed IOTA-based FTN-DFT-s-OFDM Waveform Design is introduced in Section 3, including the transmission waveform design and receiver design. Then in Section 4, extensive simulation results of the proposed waveform are shown, with respect to PAPR, BER, and throughput under various MCSs. Finally, Section 5 concludes this paper.
## 3 Methodology  
### BiLSTM  
Numerous studies have demonstrated that recurrent neural networks (RNNs) can be used for handling time-series prediction tasks [23; 24; 25]. However, in tasks involving long time series, it is difficult for an RNN to effectively capture the long-term dependencies among data due to the gradual decay or infinite increase of the error signal during back-propagation. To solve this challenge, LSTM, an improved version of the RNN, introduces memory cell and gating mechanism to significantly alleviate the gradient explosion and explosion gradient vanishing problems of RNNs. Figure 5 provides a detailed view of an LSTM cell.  
Here, Xt, Ct, and ht represent the input, memory cell, and output at time t, respectively. The forward propagation process of each cell at moment t is as follows:  
\[f_{t}=\sigma(W_{xf}X_{t}+W_{hf}h_{t-1}+b_{f}) \tag{3}\]  
\[i_{t}=\sigma(W_{xi}X_{t}+W_{hi}h_{t-1}+b_{i}) \tag{4}\]  
\[O_{t}=\sigma(W_{xo}X_{t}+W_{ho}h_{t-1}+b_{o}) \tag{5}\]  
\[C_{t}=f_{t}*C_{t-1}+i_{t}*\tanh(W_{xc}X_{t}+W_{hc}h_{t-1}+b_{c}) \tag{6}\]  
Figure 4: The sliding window.  
Figure 5: LSTM cell structure.  
\[h_{t}=O_{t}*\tanh(C_{t}) \tag{7}\]  
where \(\mathbf{f_{t}}\), \(\mathbf{i_{t}}\), and \(\mathbf{O_{t}}\) denote the forget gate, input gate, and output gate, respectively; \(\mathbf{W_{t}}\), \(\mathbf{W_{i}}\), and \(\mathbf{W_{o}}\) represent the weights of these gates; \(\mathbf{b}\) is the bias; \(\sigma\) refers to the sigmoid activation function; and tanh serves as the activation function for the memory cell and hidden state. Compared to RNN, LSTM decides whether to retain or update memories using a combination of three gates. The sigmoid function is used to implement the gate selection mechanism with an output range of [0, 1], with 0 representing a closed door and 1 a fully open door. The tanh maps the data into the range of [\(-\)1, 1]. Furthermore, this function provides normalization and nonlinear transformation, facilitating smooth regulation of state updates and outputs.  
In this study, BiLSTM is proposed for the preliminary estimation of the SOE. Compared to LSTM, the network introduces two independent LSTM layers to realize forward and backward processing of time-series data, respectively. The forward LSTM layer captures dependencies from past data up to the current moment, while the backward LSTM layer extracts information from future information back to the present [26]. The bidirectional recurrent structure of BiLSTM enhances the accuracy of SOE estimation by leveraging the data from the past, the current, and the future. The framework of BiLSTM is shown in Figure 6.
# Research on a Collaborative Control Strategy of an Urban Expressway Merging Bottleneck Area
## 4 An Illustrative Example
### Effect Evaluation  
In Figure 17, the simulation results are output after the cooperative control strategy is turned on at the two merging areas on the VISSIM. Comparing the changes of the expressway traffic flow state under the uncontrolled state shown in Figure 14, it can be seen that the traffic flow operation of each segment under the cooperative control is obviously more stable, and the space-time spread range of congestion is significantly reduced. From the time point of view, the speed of the Cell6 segment decreased to about 40 km/h at about 41 min, and the state began to spread to the upstream segment. At 83 min, each segment has returned to a smoother operating state, and the duration of congestion is significantly reduced compared with the uncontrolled state (36 min\(\sim\)100 min). From the spatial point of view, the flow distribution of each segment is more uniform during the peak period. Compared with the uncontrolled state, the traffic flow speed of the initial segment Cell1 remains above 50 km/h throughout the peak period, indicating that the spatial propagation range of congestion is reduced under coordinated control. In terms of the merging bottleneck control effect, the minimum traffic flow speed is 37.3 km/h (27.6 km/h uncontrolled), which is smaller than that of the uncontrolled state, indicating that the cooperative control strategy proposed in this paper has a good effect in controlling the merging bottleneck.  
Figure 17: VISSIM output results after the implementation of collaborative control. (**a**) Traffic flow; (**b**) Traffic flow speed; (**c**) Control parameter output.
# A Hybrid Wind Power Forecasting Model with XGBoost, Data Preprocessing Considering Different NWPs
## 3 Assessment Indices for Deterministic and Probabilistic Forecasts  
### Root Mean Squared Error (RMSE)  
A variety of statics have been proposed and employed to evaluate the forecast performance, but no single one is accepted as the universal standard [22]. There are many criteria for rating network effectiveness's assessment, such as Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), and Mean Absolute Error (MAE). All of those indexes have been utilized for deterministic forecasts. For a short-term wind power forecast, RMSE is generally chosen to be the comparison index among different methods, which can be illustrated by:  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(P\ -\ P_{0})^{2}} \tag{1}\]  
where \(n\) is the number of data, \(P_{0}\) is real-time power, and \(P\) is forecasting wind power.