esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
In this paper, we propose a novel Bayesian learning-based channel estimation architecture to estimate the time-varying multipath channel with colored noise for UWA-OFDM systems. Specifically, a clustered-sparse channel distribution model was constructed to characterize the multipath distribution, and a noise-resistant channel measurement model is constructed to reduce the noise disturbance. To obtain the clustered-sparse distribution, we propose the partition-based clustered-sparse Bayesian learning algorithm. To lessen the effect of colored noise, we proposed a noise-corrected clustered-sparse channel estimation algorithm to improve the estimation performance. Taking advantage of the iterative clustered-sparse distribution learning, symbol decision and noise correction, the accuracy of Bayesian channel estimation can be improved. Experiments proved the effectiveness of the proposed algorithms for channel estimation.  
In future work, channel decoding can be incorporated into the Bayesian learning-based channel estimation architecture to explore the possibility of reducing symbol decision errors. In the noise-resistant channel measurement model, the channel gains are approximately constant within one OFDM block, which may be too optimistic in some rapidly time-varying UWA channels. Therefore, some channel estimation models that deal with doubly selective fading channels, such as the popular basis expansion model, can be combined with the clustered-sparse Bayesian learning to improve the estimation performance. At the same time, channel feedback and reconstruction will be analyzed to realize adaptive modulation and coding in UWA-OFDM communication.  
Conceptualization, S.W. and D.L.; data curation, S.W.; formal analysis, S.W. and M.L.; funding acquisition, D.L.; investigation, S.W.; methodology, S.W. and M.L.; project administration, D.L.; resources, D.L.; software, S.W.; validation, S.W. and D.L.; visualization, S.W.; writing--original draft, S.W.; writing--review and editing, M.L. and D.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under Grant 61571334.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 8: The comparison of BER performance in the lake experiment.
## 1 Introduction
A conjecture-based multi-agent Q-learning scheme was presented by [PERSON] et al. [15] to execute power adaption in a partially observable environment. However, route selection in multi-hop CRN was not considered in this work. [PERSON] et al. [16] modeled the routing problem as a stochastic game (SG). Then, the SG was solved through a non-cooperative multi-agent learning method in which each secondary user (SU) speculated other nodes' strategies without acquisition of global information. The current conjecture belief was determined only by the last one in [16], which caused strong correlations between the samples. Power adaption was not considered when solving the routing problem, which would influence power efficiency and the routing decision. In our previous work [17], we designed a single-agent based intelligent joint routing and resource assignment scheme for CRN to achieve the maximum cumulative rewards. In this paper, we adopt a quasi-cooperative multi-agent learning scheme for routing and radio resource management, which is more efficient than the single-agent strategy in multi-hop CRN. The scheme tries to achieve the lowest end-to-end delay and improve energy efficiency with finite information exchange between competing SUs. Our contributions are summarized as follows:  
1. In order to jointly capture the end-to-end latency and power efficiency, a comprehensive utility function is designed to form a reasonable tradeoff between the two as well as accommodate the maximal transmission latency requirement. Queuing theory is adopted to analyze single-hop latency and provide a theoretical basis for our cross-layer routing protocol design. 2. A quasi-cooperative multi-agent learning framework is presented to solve the cross-layer design problem where every SU node speculates other nodes' strategies from finite information exchange with the previous nodes. The convergence of the quasi-cooperative learning scheme is proven. 3. For the purpose of further enhancing performance, experience replay is applied to the update of conjecture belief, which allows for greater data efficiency by using the historical conjectures and breaks the correlations to reduce the variance of updates. The remainder of this paper is organized as follows: the system model is presented in Section 2. Section 3 models the cross-layer design problem as a SG. The quasi-cooperative multi-agent learning scheme for the cross-layer routing protocol is proposed in Section 4. Section 5 demonstrates the simulation results. Finally, the paper is concluded in Section 6. In addition, summary of acronyms used in this paper is listed in Table 1.
###### Abstract  
This review paper reports the prerequisites of a monolithic integrated terahertz (THz) technology capable of meeting the network capacity requirements of beyond-5G wireless communications system (WCS). Keeping in mind that the terahertz signal generation for the beyond-5G networks relies on the technology power loss management, we propose a single computationally efficient software design tool featuring cutting-edge optical devices and high speed III-V electronics for the design of optoelectronic integrated circuits (OEICs) monolithtically integrated on a single Indium-Phosphide (InP) die. Through the implementation of accurate and SPICE (Simulation Program with Integrated Circuit Emphasis)-compatible compact models of uni-traveling carrier photodiodes (UTC-PDs) and InP double heterojunction bipolar transistors (DHBTs), we demonstrated that the next generation of THz technologies for beyond-5G networks requires (i) a multi-physical understanding of their operation described through electrical, photonic and thermal equations, (ii) dedicated test structures for characterization in the frequency range higher than 110 GHz, (iii) a dedicated parameter extraction procedure, along with (iv) a circuit reliability assessment methodology. Developed on the research and development activities achieved in the past two decades, we detailed each part of the multiphysics design optimization approach while ensuring technology power loss management through a holistic procedure compatible with existing software tools and design flow for the timely and cost-effective achievement of THz OEICs.  
Keywords:electrical characterization; compact model; heterojunction bipolar transistor; uni-traveling carrier photodiodes; high frequency; indium-phosphide; sub-millimeter wave; terahertz; TCAD and Monte Carlo simulations +
Footnote †: journal: Applied Sci.
# An Integrated DQN and RF Packet Routing Framework for the V2X Network
## 4 System Simulations and Performance Evaluations
### Different Transmission Ranges for Sparse and Congested Periods  
Figures 38-43 depict the simulation results for sparse and congested periods with four transmission ranges (300, 425, 550, and 675). The results are used to analyze the impacts on the average packet delivery ratio, average end-to-end delay, and average overhead ratio of all routing algorithms. There are more vehicles during congested periods than in sparse periods, leading to more available transmission nodes. Compared to sparse periods, the routing algorithms' average packet delivery ratios increase in congested periods, while the average end-to-end delays and overhead ratios decrease. The average packet delivery ratios for all routing algorithms rise in both sparse and congested periods. In contrast, the average end-to-end delays and overhead ratios decrease as the transmission range increases, which increases the ability to select more vehicles for packet transmission.  
Figure 39: The average packet delivery ratio with different transmission ranges for congested periods.  
Figure 38: The average packet delivery ratio with different transmission ranges for sparse periods.  
Figure 41: The average end-to-end delay with different transmission ranges for congested periods.  
Figure 42: The average overhead ratio with different transmission ranges for sparse periods.  
Figure 40: The average end-to-end delay with different transmission ranges for sparse periods.  
The results collected with different transmission ranges for sparse and congested periods show that the IDRF outperforms all other methods regarding the average packet delivery ratio with varying transmission ranges. Moreover, with different transmission ranges for congested periods, IDRF_DQN_RF exhibits a higher average packet delivery ratio than all methods except for the IDRF. The three proposed methods in this paper, IDRF_DQN, IDRF_DQN_RF, and IDRF, all demonstrate lower average end-to-end delays and average overhead ratios than traditional routing methods. According to the statistical analysis presented in Table 9, the IDRF achieves significant performance improvements over TDRL-RP and VRDRT for both sparse and congested periods. Compared to previous observations, a notable aspect here is that with larger transmission ranges, the distance covered by each hop increases, enabling faster delivery to the destination vehicles. Additionally, in congested periods, the higher number of available vehicles for relay vehicle selection contributes to the better performance of the IDRF compared to sparse periods. In conclusion, the IDRF can maintain stability with different environmental parameters and achieve better results for the three performance metrics tested in this study.
## 3 Proposed Network Traffic Predictor and Traffic Classification Agents  
In this section, we discuss the proposed NTPA and NTTA frameworks. First, let us re-define and contextualize the agents. The agent has input, output, and communication tools to observe the environment, provide the output or execute the action, and communicate with other existing agents in the MAS. Once an agent is employed in a given environment and it observes the environmental variables, it can make decisions. The decision is based on the agent's final goals, which could be to predict the incoming traffic and classify them accordingly. By measuring the incoming traffic over a given link, it could analyze and use it for load balancing, resource slicing, or resource allocation. The important characteristics of an agent are as follows:  
* Situatedness: that means agents use sensors or perception capabilities as an interaction with the environment, and it uses actuators to perform actions on the environment. In the NTPA and NTTA contexts, it means taking incoming data as input and providing prediction and classification results as output, respectively.
* Autonomy: the agents' internal state is protected from any external disturbance from other agents in the MAS. NTPA and NTTA are capable of performing their respective function autonomously without any external support.
* Inferential capability: this enables agents to work on an exact goal until it is achieved. Agents can analyze the available data for decision making, such as network traffic prediction and traffic class determination.
* Responsiveness: defined as the ability of an agent to perceive the environment and perform on it with minimal latency, which is important in real-time processes. Agents should be designed such that the task execution (prediction and classification in case of NTPA and NTTA) must be performed with a strict time delay.
* Pro-activeness: means agents should take advantage of particular opportunities that aim to improve their action performance to dynamically adapt to the changes in the surrounding environment of the network. By analyzing the historical data, it could predict the amount of traffic that comes in a given link. Using this knowledge, it pro-actively allocates the required resources, such as bandwidth.
* Social behavior: the agent's decision should not be affected by any external interference, either from human interactions or other agents in the system. NTPA and NTTA should be capable of performing independently but also could communicate and collaborate to perform a sequence of tasks, such as classification followed by prediction or vice versa. For instance, NTTA could classify traffic into various classes and provide the output to the predictor. Based on the classes, the predictor could predict the amount of incoming traffic of a given time in the coming hours or days.  
Figure 4: An illustration of the K-NN classifier algorithm in action.
# Single Image Super-Resolution Method Using CNN-Based Lightweight Neural Networks
## 4 Experimental Results
\begin{table}
\begin{tabular}{c c} \hline \hline Num. of Training Samples & 210,048 \\ \hline Input size (\(X_{LR}\)) & 25 \(\times\) 25 \(\times\) 1 \\ Interpolated input size (\(X_{ILR}\)) & 100 \(\times\) 100 \(\times\) 1 \\ Label size (\(Y_{HR}\)) & 100 \(\times\) 100 \(\times\) 1 \\ Linux version & Ubuntu 16.04 \\ CUDA version & 10.1 \\ Deep learning frameworks & Pytorch 1.4.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experimental environments. Figure 6: PSNR and MSE corresponding to the number of epochs. **(a)** PSNR per epoch. **(b)** MSE per epoch. Figure 7: Comparisons of subjective quality on test dataset. **(a)** Results on “monarch” of Set14. **(b)** Results on “zebra” of Set14. **(c)** Results on “img028” of Urban100. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Skip Connections**} & \multirow{2}{*}{**Dense**} & \multicolumn{2}{c}{**Set5 (PSNR)**} & \multicolumn{2}{c}{**Set14 (PSNR)**} & \multicolumn{2}{c}{**BSD100 (PSNR)**} & \multicolumn{2}{c}{**Urban100 (PSNR)**} \\ \cline{3-10}  & & **S**R-**ILLNN** & **S**R-**SLNN** & **S**R-**ILLNN** & **S**R-**SLNN** & **S**R-**ILLNN** & **S**R-**SLNN** & **S**R-**ILLNN** & **S**R-**SLNN** \\ \hline Disable & Disable & 31.34 & 31.15 & 27.80 & 27.62 & 27.31 & 27.20 & 25.27 & 25.01 \\ Enable & Disable & 31.35 & 31.21 & 27.81 & 27.68 & 27.33 & 27.26 & 25.31 & 25.13 \\ Disable & Enable & 31.40 & 31.18 & 27.81 & 27.65 & 27.32 & 27.23 & 25.29 & 25.07 \\ Enable & Enable & 31.41 & 31.29 & 27.83 & 27.73 & 27.33 & 27.28 & 25.32 & 25.18 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The results of tool-off tests. In terms of the network complexity, we analyzed the number of parameters, parameter size (MB), and total memory size (MB) where total memory size includes intermediate feature maps as well as the parameter size. In general, both the total memory size and the inference speed are proportional to the number of parameters. Table 9 presents the number of parameters and total memory size. Compared with SR-DenseNet, the proposed two SR-LNNs reduce the number of parameters by as low as 8.1% and 4.8%, respectively. Similarly, the proposed two SR-LNNs reduce total memory size by as low as 35.9% and 16.1%, respectively. In addition, we evaluated the inference speed on BSD100 test images. As shown in Figure 8, the inference speed of the proposed methods is much faster than that of SR-DenseNet. Even though the proposed SR-SLNN is slower than SR-CNN and AR-CNN, it is obviously superior to SR-CNN and AR-CNN in terms of PSNR improvements as measured in Tables 6 and 7.
# Radio Frequency Electromagnetic Fields Exposure Assessment in Indoor Environments: A Review
## 3 Results
### Educational Buildings
The authors concluded that the measured E\({}_{\mathrm{avg}}\) levels recorded at 92 schools were below the ICNIRP guidelines [43]. The five main electric field strength (E) sources that had the most contribution in total \(E\) were LTE800, GSM900, GSM1800, UMTS2100 and WLAN services. It was also concluded from the long-term broadband measurement result that the number of active users affected the total E, and that the measured E levels were significantly higher in daytime than those of recorded in night-time. A study by [PERSON] et al. [54] measured indoor RF-EMF levels to which children are exposed in a large number of primary schools in Amsterdam. Measurements in each classroom consisted of seven spot measurements of two minutes each, with sampling of 4 s, carried out after lessons, in the afternoon. Results showed average power density of 70.5 mW/m\({}^{2}\), (0.16 V/m). Main contributors to total RF-EMF levels were mobile phone downlink and DECT signals. Over half of detected signals (56.3%) originated from outdoor sources (e.g., mobile phone downlink, broadcast). When looking at signals that originated from indoor sources (e.g., DECT, WiFi, mobile uplink), DECT was the main contributor, followed by mobile phone uplink. Most variance between measures was explained by differences between rooms. Many other public places, such as small and large shops, shopping malls, railway stations and airports, have been investigated. However, most of the studies presenting results about the levels of exposure to RF-EMF in these facilities were not focused on a deep investigation about each single type of indoor environment, but included measurements in a high number of different places (results of these studies will be describe the \"Comparison between different types of indoor environments\" section). The only paper focusing on a large public indoor environment such a railway station was the survey by [PERSON] et al. [29], focused on the assessment of the levels of exposure in the Stockholm Central Railway Station. The authors found that total power density mean values for a walking round varied between 2.817 to 4.891 mW/m\({}^{2}\) (1 V/m and 1.36 V/m in terms of electric field, 3.6% and 4.9% of the lowest ICNIRP reference level for the considered frequency band). GSM and UMTS 900 and 2100 downlink contributed to almost half of total exposure. Other major sources were LTE 800 downlink, GSM 1800 downlink and LTE 2600 downlink, while other sources were comparatively low.
# A New Design Method of Shield Tunnel Based on the Concept of Minimum Bending Moment
## 3 The Design and Calculation of the Zero Bending Moment Shield Tunnel Cross-Section
### The Calculation of the Horizontal Diameter of the Zero Bending Moment Shield Tunnel  
The horizontal diameter at the center of the vertical diameter of the shield tunnel is known as the center horizontal diameter, represented by \(b\). From the calculating equation of the rational axis of the shield tunnel cross-section and the calculating equation of the bending moment of the corresponding simply supported beam, it can be learnt that, since the lateral earth pressure of the shield tunnel is larger at the top than at the bottom, the largest horizontal diameter of the shield tunnel does exist at the vertical diameter center but somewhere lower than the center itself. The largest horizontal diameter of the shield tunnel is represented as \(c\).  
When \(x=a/2\), the corresponding \(y=b/2\), according to Equation (8), the calculating equation of b be calculated as Equation (9).  
\[b=2\sqrt{\left(N_{1}a-\frac{1}{4}P_{2}a^{2}-\frac{1}{24}P_{3}a^{2}\right)/P_{1}} \tag{9}\]  
For the maximum of \(\beta\), the corresponding \(y=c/2\). The calculating equation of \(c\) can thus be obtained. Assume \(M_{x}\) is the derivative of \(x\) to \(x\), as shown in Equation (10).  
\[\beta=\frac{dM_{x}}{dx}=N_{1}-P_{2}x-\frac{1}{2a}P_{3}x^{2} \tag{10}\]  
From Figure 7, it can be learnt that \(x\in[0,\ a]\). When \(\beta=0\), the corresponding \(x\) will obtain the maximum \(M_{x}\). Hence, the maximum of \(y\) will be obtained based on the corresponding \(x\) when \(\beta=0\), which is \(c/2\). The value of \(x\) conditional on \(\beta=0\) can be calculated as Equation (11).  
\[\overline{x}=\frac{a}{P_{3}}\Bigg{(}\sqrt{P_{2}^{2}+\frac{2}{a}N_{1}P_{3}}-P_{ 2}\Bigg{)} \tag{11}\]  
where \(x\) is the value corresponding to the condition \(\beta=0\).  
Put \(\overline{x}\) obtained from Equation (11) into Equation (8), to obtain the calculating equation of \(c\), as shown in Equation (12).  
\[c=2\sqrt{\left(2N_{1}\overline{x}-P_{2}\overline{x^{2}}-\frac{1}{3a}P_{3} \overline{x^{3}}\right)/P_{1}} \tag{12}\]  
where \(c\) is the largest horizontal diameter of the shield tunnel.  
Name the distance difference between the position of the vertical diameter conditional on the maximum horizontal diameter and the position of the vertical diameter conditional on the center horizontal diameter as eccentricity \(\Delta\). It can be calculated as Equation (13).  
\[\Delta=\overline{x}-\frac{a}{2} \tag{13}\]  
Figure 8: Internal force analysis for a section of zero bending moment shield tunnel cross-section 2.  
where \(\Delta\) is the difference of vertical diameter position under the different conditions.
## 2 Preliminaries
### Degradation of the Original System Model  
When all the active links reach the target state and are maintained, the PMLUM converts to a PVP.  
The dynamic model of the PVP is as follows:  
\[\tilde{M}(\tilde{q})\tilde{q}+\tilde{H}(\tilde{q},\tilde{q})=\tilde{\tau} \tag{6}\]  
where  
\[\tilde{M}(\tilde{q})=\left[\begin{array}{cc}\tilde{M}_{11}&\tilde{M}_{12}\\ \tilde{M}_{21}&\tilde{M}_{22}\end{array}\right],\tilde{H}(\tilde{q},\tilde{q})= \left[\begin{array}{cc}\tilde{H}_{1}(\tilde{q},\tilde{q})\\ \tilde{H}_{2}(\tilde{q},\tilde{q})\end{array}\right] \tag{7}\]  
\[\tilde{q}=[\tilde{q}_{1}\;\tilde{q}_{2}]^{T},\tilde{q}=[\tilde{q}_{1}\;\tilde{ q}_{2}]^{T},\tilde{\tau}=[\tau_{1}\;0]^{T} \tag{8}\]  
\[\left\{\begin{array}{l}\tilde{M}_{11}=b_{1}+b_{2}+2b_{3}{\rm cos}\tilde{q}_{ 2}\\ \tilde{M}_{12}=\tilde{M}_{21}=b_{2}+b_{3}{\rm cos}\tilde{q}_{2}\\ \tilde{M}_{22}=b_{2}\\ \tilde{H}_{1}=-b_{3}\Big{(}2\tilde{q}_{1}\tilde{q}_{2}+(\tilde{q}_{2})^{2} \Big{)}{\rm sin}\tilde{q}_{2}\\ \tilde{H}_{2}=b_{3}(\tilde{q}_{1})^{2}{\rm sin}\tilde{q}_{2}\end{array}\right. \tag{9}\]  
\[\left\{\begin{array}{l}b_{1}=m_{m}L^{2}+m_{m+1}L_{m}{}^{2}+\tilde{I}_{1}\\ b_{2}=m_{m+1}L_{cm+1}^{2}+\tilde{I}_{2}\\ b_{3}=m_{m+1}L_{m}L_{cm+1}\end{array}\right. \tag{10}\]From (6), the underactuated equation is generated as follows:  
\[\tilde{M}_{21}\ddot{\tilde{q}}_{1}+\tilde{M}_{22}\ddot{\tilde{q}}_{2}+\tilde{H}_{ 2}=0 \tag{11}\]  
Define \(\ddot{\tilde{q}}_{1}=u\) as an auxiliary control input, and we obtain the following:  
\[\left\{\begin{array}{l}\ddot{\tilde{q}}_{1}=u\\ \ddot{\tilde{q}}_{2}=-\tilde{M}_{22}^{-1}\tilde{H}_{2}-\tilde{M}_{22}^{-1} \tilde{M}_{21}u\end{array}\right. \tag{12}\]  
Let \(\tilde{x}=[\tilde{q}_{1}\ \tilde{q}_{2}\ \dot{\tilde{q}}_{1}\ \dot{\tilde{q}}_{2}]^{T}\), the state space equation of the degraded PVP system can be obtained as follows:  
\[\begin{array}{l}\dot{\tilde{x}}=\left[\begin{array}{c}\dot{\tilde{q}}_{1} \\ \dot{\tilde{q}}_{2}\\ 0\\ -N\sin\tilde{q}_{2}(\dot{\tilde{q}}_{1})^{2}\end{array}\right]+\left[ \begin{array}{c}0\\ 0\\ 1\\ -(1+N\cos\tilde{q}_{2})\end{array}\right]u\\ =\tilde{f}(\tilde{x})+\tilde{g}(\tilde{x})u\end{array} \tag{13}\]  
where \(N=b_{3}/b_{2}\).