esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Deep Reinforcement Learning Approach for Task Offloading in MEC Systems
## 3 System Model
### Problem Formulation  
As a classic formulation of sequential decision making, MDP is suitable for formulating this problem. The objective of our scheme is not only to minimize the total delay of all the tasks, but also to guarantee the long-term dynamic of task queuing. The total delay of our system is defined as:  
\[D_{m}(a_{m}(t))=\alpha(L_{m}^{loc}(t)-t+1)+(1-\alpha)(L_{m,n}^{deg}(t^{{}^{\prime }})-t+1), \tag{8}\]  
where \(\alpha\) can be 0 or 1; if it is equal to 0, then the task, \(u_{m}(t)\), will be offloaded to the edge service; if it is 1, then the task, \(u_{m}(t)\), will be computed at the mobile device.  
Then we use \(C_{m}(t)\) to define the overall cost associated with the delays:  
\[C_{m}(t)=\beta D_{m}(a_{m}(t))+(1-\beta)C, \tag{9}\]  
where \(\beta\) can be 0 or 1; if it equals to 0, then the task, \(u_{m}(t)\), has been dropped at the deadline, otherwise it has been completed before the deadline. \(C\) is a constant penalty for being dropped.  
The optimization problem \(\mathbf{P}\) is formulated as follows:  
\[\begin{split}\mathbf{P}:\min_{L_{m}^{loc}(t),L_{m,n}^{deg}(t)}& C_{m}(t),\forall m\in\{1,2,\dots,M\}\\ & s.t.& D_{m}(a_{m}(t))\leq\pi_{m}.\end{split} \tag{10}\]
## 5 Security Analysis
### Man-in-the-Middle Attack  
According to the definition of a man-in-the-middle attack, an attacker can disguise itself as a terminal, and each participant in the session cannot identify it as a real terminal. In fact, a man-in-the-middle attack is a mutual authentication attack. In our proposed scheme, RCRS saves the authentication data of legitimate users in a database and performs mutual identity verification. If an attacker tries to pretend to be a real terminal, then, without the RCRS authentication record, other steps cannot be performed and any information can be obtained. Therefore, our proposed TCVK scheme can resist man-in-the-middle attacks.
## 6 Conclusions  
The objective of this study was to propose a novel cross-layer routing protocol, designated as the CLCQ. The actual channel impulse response is calculated by using the winter sound velocity profile data from a specific sea area. Subsequently, the underwater acoustic channel is incorporated into the OFDM communication system, and the channel quality is evaluated by calculating the bit error rate, which is then applied to the protocol design. Furthermore, this paper proposes an optimized forwarding candidate set selection method that accelerates the convergence speed of the reinforcement learning algorithm, thereby further enhancing the overall performance and efficiency of the routing protocol.  
The CLCQ effectively integrates the information from the physical layer and data link layer to intelligently select the next hop node. To guarantee the reliability of data transmission, this study devised a reward function based on a reinforcement learning algorithm. This function considers various factors, including channel quality, buffer state, and residual energy, in order to select the optimal route for forwarding data packets. Consequently, the CLCQ circumvents congestion areas while selecting high-reliability links.  
Figure 16: PDR of different protocols.  
The results of the simulation present that the PDR of the CLCQ is markedly enhanced compared to the traditional underwater acoustic routing protocols.  
However, some unresolved issues remain in this study. For instance, the BER utilized in the protocol design is calculated under the deterministic communication system, without considering different communication environments.  
[PERSON] provided academic guidance and offered practical suggestions for the research content. [PERSON] designed the core method proposed in this paper, developed the program, conducted the relevant simulation verification, and drafted the manuscript. [PERSON] revised the manuscript, enhancing both its content and structure. [PERSON] provided theoretical guidance for the paper. [PERSON] guided the overall direction of the research and provided a research framework for the study. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# RRT*-Fuzzy Dynamic Window Approach (RRT*-FDWA) for Collision-Free Path Planning
## 3 FDWA Algorithm
### FDWA Fuzzy Distribution  
In the DWA algorithm, the percentage of weights has a very strong influence on the results of the path planning algorithm. Therefore, in order to improve the efficiency of the algorithm and be better able to adapt to the complex environment, the fuzzy inference and DWA algorithm are combined, enabling adaption of the evaluation index weights. Fuzzy inference is performed on the azimuth evaluation function weights \(\alpha\) and distance function weights \(\beta\), (\(\beta\), \(\alpha\in[0\), \(0.5]\)), and a dual-input, dual-output fuzzy controller is designed. The angle \(\theta\)d at which the actual trajectory of the robot deviates from the predetermined trajectory and the distance \(\mathrm{Od}\) to the nearest obstacle are used as dual inputs, and the azimuth evaluation function weight, \(\alpha\), and the distance function weight, \(\beta\), are used as dual outputs. The membership function of the fuzzy system uses trigonometric functions, which are suitable for describing well-defined ranges and information precisely. The input membership functions \(\theta\)d and \(\theta\)d are shown in Equation (10): (\(\mu(u)\in[0\), \(1]\)). The output affiliation function of weight \(\alpha\) is \(\mu(o)\), shown in Equation (11): (\(\mu(o)\in[0\), \(1]\)). Figure 1(a) shows the input membership function and Figure 1(b) shows the output.  
\[\mu(u)\in[O_{d},\theta_{d}],\mu(o)\in[\beta,\alpha] \tag{9}\]\[\mu(u)=\begin{cases}1-2u&0\leq u\leq 0.25\\ 2u&0.25<u<0.5\\ 2-2u&0.5\leq u\leq 0.75\\ 2u-1&0.75<u<1\end{cases} \tag{10}\]  
\[\mu(o)=\begin{cases}8o&0\leq o\leq 0.125\\ 1-8o&0.125\leq o\leq 0.1875\\ 8o-1&0.1875\leq o\leq 0.25\\ 3-8o&0.25\leq o\leq 0.2975\\ 8o-2&0.2975\leq o\leq 0.375\\ 4-8o&0.375\leq o\leq 0.5\end{cases} \tag{11}\]
# Software-Defined Vehicular Cloud Networks: Architecture, Applications and Virtual Machine Migration
## 3 The Architecture of Software-Defined Vehicular Cloud
### SDN Controller Assistance for Cloud-Based Vehicular Applications
#### 3.2.3 Cloud Vehicular Application Based on Bandwidth Resource Sharing  
Downloading from a central cloud a document by a passenger in vehicle is bandwidth consuming. In cloud vehicular network, a cooperative downloading is an option for allowing vehicles driving in the same direction to help other passengers download a large file. Vehicles in a group of V2X cloud download the file. For this scenario, a distributed active VM controller is established to control the cluster of vehicles downloading the file from the cloud. The SDN communication infrastructure controller relies on the vehicle collaboration sharing function (function at application plane) to update flow tables of the OpenFlow switch directly connected to vehicles with their vehicle Identification (ID). The vehicle collaboration sharing function enables bandwidth sharing application. In fact, this service accelerates the rate of network bandwidth by speeding up the download compared to one vehicle handling the download task. Thus, the active VM controller assists each vehicle (involved in downloading the file) to transmit its own part to the owner, which in turns merges (reassembles) all other downloaded parts to get the entire file.  
The management of cloud-based vehicular applications based on storage, computation, and bandwidth sharing are still worth discussing in the next generation vehicular network. Emergent technologies such as SDN and Network Function Virtualization (NFV) have potential to address the issue of ultra-low latency for cloud-based vehicular applications. Certainly, SDN and NFV would influence the deployment of cloud-based vehicular applications for the next generation vehicular network. Table 1 summarizes potential applications in cloud-based vehicular networks supporting SDVC. We also provide relevant SDN controllers on the control layer to assist each application. [PERSON] et al. [4] first discussed the resource sharing assistance.
## 3 The Sensor Network Calculus
### Incorporation of Network Calculus Components  
In this section, we incorporate the basic network calculus components, i.e., arrival curves and service curves, into the network system model. We make use of the simplicity of the SensorNC sink tree where each sensor has exactly one parent node. For a detailed treatment of modeling generic feed-forward networks, see [10].  
Each sensor in the network is crossed by at least one flow. Their aggregate defines the total input to sensor \(i\), \(\bar{R}_{i}\). The total input function is composed by the sensor's sensed input and the output of its child nodes:  
\[\bar{R}_{i}=R_{i}+\sum_{j=1}^{n_{i}}R_{child(i,j)}^{*}. \tag{1}\]  
We can derive an analogous description with network calculus arrival curves and operations applied to them:  
\[\bar{\alpha}_{i}=\alpha_{i}+\sum_{j=1}^{n_{i}}\alpha_{child(i,j)}^{*}. \tag{2}\]  
where \(\bar{\alpha}_{i}\) denotes the arrival curve for \(\bar{R}_{i}\). It is composed of the arrival curve for the input sensed by sensor \(i\), \(\alpha_{i}\), and the aggregation of its child nodes' output arrival curves. As an example, we could use simple token-bucket functions to model these inputs.  
Next, service curves need to be incorporated. Specifying them is subject to the packet scheduling applied by the respective server. In a WSN, an additional impact factor is the link layer characteristics. For instance, duty cycling may be applied to achieve predefined energy-efficiency targets and thus impacts the service curve by periodical unavailability of the medium access. We model such a periodic service curve in the following. Assume the full medium capacity \(C\) is periodically available after an initial delay \(T\), i.e., a TDMA medium access pattern results after this delay (see Figure 2). Let the TDMA frame duration be \(f\) and assume a sensor node receives \(s\) time units of service within a frame. From this information, we can compute \(T\), the maximum initial medium access latency that can be experienced, as \(T=f-s\). To establish independence from the start of observation, the sensor node cannot be assumed to receive service during an initial duration of observation of \(T\), and the service curve is zero. Then, the node receives service for the duration of its share of the TDMA frame. That is,  
Figure 1: Sensor network model.  
from \(T\) to \(T+s\), the service curve will have a linear segment of slope \(C\) until it reaches \(sC\). For the remainder of the TDMA frame, i.e., from \(T+s\) to \(T+f\), service is unavailable again, and the service curve remains at \(sC\). With the start of the next TDMA frame, the shape of the service curve from \(T\) to \(T+f\) is repeated; it actually repeats indefinitely with a period of \(f\). A similar service curve structure was proposed for 802.15.4 networks [11]. This curve can be approximated with a rate-latency service curve consisting of only two linear segments [12,13]: \(\beta_{R,T}(t)=\max\left(R(t-T),0\right)\) with \(R=\frac{s}{f}C\) and \(T=f-s\). In Figure 2, this fluid version of the TDMA service curve is labeled \(\beta\).
# Analysis of Human Body Shadowing Effect on Wireless Sensor Networks Operating in the 2.4 GHz Band
## 5 Conclusions  
The research presented in this paper shows that the human body shadowing effect influences the performance of wireless sensor networks operating in the 2.4 GHz band. This effect introduces additional path loss that should be considered by designers of networks that utilize low power miniature sensors with limited transmit power and a small fade margin. The shadowing effect can reduce the operational range of networks, especially in the case of indoor networks where people may be present and the relative positions of the antennas and obstacles may vary.  
Computer models that use the FDTD method are suitable only for analyzing the body shadowing effect on networks that operate across short distances. The numerical cost of the simulations makes the FDTD method impractical for analyses of networks that cover entire buildings or groups of buildings. To simulate the body shadowing effect on wireless sensor networks that operate inside buildings, a ray-based computer program can instead be used. This paper proposed a simplified human body model elaborated in the Remcom XGtd(tm) program for simulations of the body showing effect in indoor scenarios, where multiple reflections should be considered. The model can be used to estimate the fade margin that should be assumed for successful transmission across a wireless sensor network operating in the presence of human bodies.  
The proposed model of the human body was used for the analysis of the body-shadowing effect in the indoor environment with the XGtd(tm) program. The results were obtained for two scenarios with a different number of body models that were placed in a few orientations towards the transmitting and receiving antennas. The simulations showed that the presence of human bodies in the proximity of the transmitter causes additional path loss that, in the case of wireless sensor networks that operate with low power transmitters, should be considered in the link budget analysis for building interiors. It was shown that the path loss in the 2.4 GHz band in the indoor environment depends strongly on the number of people as well as on their spatial orientation in the propagation environment. This effect can be effectively studied with the proposed model of the human body.  
The simulations of path loss were followed by measurements that also identified the additional path loss introduced by the presence of a person. These measurement results correspond well with the results of simulations using ray-based software and the proposed human body model based on six-wall prisms. The small differences observed between absolute values of path loss obtained with the measurements and the simulations may be caused by the simplification of the building structure that was modeled in the XGtd(tm) program.  
Further studies are planned to investigate the possibility of reconfiguring the proposed model for use in additional frequency bands.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The author declares no conflict of interest.
# Forecasting Taxi Demands Using Generative Adversarial Networks with Multi-Source Data
## 6 Results
### Comparison with Baselines
#### 6.1.1 Performance Comparison over the Three Areas Together  
To assess the forecasting performance over the three study areas together (Jiedaokou area, Guanggu area, and Wuhan railway station area), we analyze the performance of the ARIMA, XBoost, MLP, GAN_LSTM_CNN model, GAN_LSTM, and GAN_CNN model in terms of MAE, RMSE, and sMAPE as defined in Equations (16)-(18). The comparison results are shown in Figures 19-20.  
Figure 19: RMSE Comparison Results with hours ranged from 0-23 with increasing values from 6:00 to 22:00 and all deep learning methods achieved better results.  
Figure 20: sMAPE Comparison Results during time slots. The achieved values showed deep learning methods (such as GAN_CNN, GAN_LSTM, GAN_LSTM_CNN) achieved better results comparing to ARIMA, XGBoost an MLP.  
From the three figures above, we can notice that although they are different forecasting performance metrics, there are some common patterns. For example, all metrics reach the minimum rates at three hours 3, 10, and 23 as the with lowest number of pickups wrt their neighboring time slots have. Moreover, the GAN_LSTM_CNN model presents a better performance compared to other predictors.  
Figure 18 plots the mean absolute error (MAE), which describes the prediction accuracy. There is a dramatic decrease at the early hours of a day until it reaches 1.4 (the lowest point) in the GAN_LSTM_CNN model at 3 am, following with a gradual increase until 22, and then a further sharp fell is found.  
As shown in Figures 19 and 20, sMAPE presents the mean absolute error, whereas RMSE shows the root mean squared error between the predicted demands and the real demands. The values dropped from 3 am until 10 am, in which the value becomes lower again, then a noticeable increase has emerged again. GAN_LSTM_CNN model also provides the minimum MAPEs and RSMEs, which shows the highest accuracy between the predicted models adopted in this paper.
## 5 Conclusions  
Antenna diversity techniques with wavelet transform were developed and analyzed by using two-element ULA and half-length spacing. The performance of the SIMO DWT and the MISO DWT was strong, with a gain of 2 dB compared with the SISO DWT. However, the performance of the MIMO DWT was much better than that of the SIMO and MISO DWTs, with a gain of 2 dB. We compared the MIMO diversity technique with different wavelets and found that the simplest Haar wavelet transform was better in terms of performance, with a gain of 2 dB. The numerical simulation results were in good agreement with the analytical results. For future study, the MIMO diversity technique with a four-element ULA can be investigated.  
Conceptualization, N.P. and K.A.; methodology, N.P. and K.A.; formal analysis, K.A. and M.R.I; investigation; resources, N.P.; data curation, K.A.; writing--original draft preparation N.P.; writing--review and editing, N.P., K.A. and M.R.I; supervision, K.A. and M.R.I.; project administration N.P., K.A. and M.R.I.; funding acquisition, M.A.K.A. All authors have read and agreed to the published version of the manuscript.  
The authors acknowledge the support of Prince Sultan University for paying the article processing charges (APC) of this publication.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **BER** & **SNR** & **Wavelet** & **Modulation** \\ \hline \multirow{3}{*}{**[PERSON] and Bigrade,**[30]} & \(10^{-3}\) & 12 dB & Haar & 16 QAM \\ \cline{2-5}  & \(10^{-1.6}\) & 12 dB & Db2 & 16 QAM \\ \cline{2-5}  & \(10^{-1.5}\) & 12 dB & Bior & 16 QAM \\ \hline \multirow{2}{*}{**[PERSON] and [PERSON],**[31]} & \(10^{-2}\) & 14 dB & Db2 & 16 QAM \\ \cline{2-5}  & \(10^{-3}\) & 6 dB & DWT DCT & 16 QAM \\ \cline{2-5}  & \(10^{-2}\) & 3.5 dB & DWT DCT & 16 QAM \\ \hline \multirow{2}{*}{**Present work**} & \(10^{-3}\) & 9 dB & Haar & 16 QAM \\ \cline{2-5}  & \(10^{-2}\) & 4 dB & Db8 & 16 QAM \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of presented results with existing work.  
**Acknowledgments:** The author [PERSON] would like to thank International Islamic University, Malaysia, for the support of TFW2020 scheme of Kulliyyah of Engineering.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Integrated Components and Solutions for High-Speed Short-Reach Data Transmission
## 6 Conclusions  
In this paper, we review the mainstream transmitter components (e.g., VCSEL/DML/EML/MZM/monolithic integrated transmitter) and receiver components (e.g., single-end photodetector/Kramers-Kronig receiver/Stokes vector receiver/monolithic integrated receiver). Next, a simplified detection scheme and two transceiver integration schemes under direct and coherent detection have been presented in detail. Then, we further summarize and discuss the technological and component options based on different transmission distances. We found that monolithic integrated components, especially transceiver integration, can dramatically reduce component cost. Therefore, we believe that such components would  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Tx** & **A** & **Fiber** & **Reach** & **BW** & **Chip** & **Footprint** & **Cost** \\ \hline IQ Modulator & O-/C-Band & SMF & \textless{}80 km & high & – & high & high \\ MZM & O-/C-Band & SMF & \textless{}80 km & high & – & high & high \\ EML & O-/C-Band & SMF & \textless{}80 km & high & low & moderate & moderate \\ DML & Mostly O-Band & SMF & \textless{}40 km & moderate & high & low & low \\ Partially C-Band & Mostly 850 nm & \textless{}000 m & low & high & low & Very low \\ VCSEL & Partially C-Band & MMF/SMF & \textless{}2 km & low & high & low & Very low \\ Monolithic integrated transmitter & O-/C-Band & SMF & whole range & high & – & low & moderate \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of different transmitters applied in beyond 100-Gbit/s per lane data transmission.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Rx** & **Reach** & **Footprint** & **Sensitivity** & **Cost** \\ \hline Coherent receiver & high & high & high & high \\ Stokes vector receiver & moderate & moderate & moderate & moderate \\ Kramers-Kronig receiver & moderate & moderate & moderate & moderate \\ Single-end PD & low & low & low & low \\ Monolithic integrated receiver & whole range & low & whole range & Very low \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of different receiver components applied in beyond 100-Gbit/s per lane data transmission.  
show great potential in the next-generation short-reach transmission systems. In addition, with the cost of coherent detection technology sinking, we predict that transceiver integration scheme based on coherent detection will become the mainstream scheme of beyond 400-Gbit/s short-reach data transmission in the future.  
[PERSON] and [PERSON] contributed to the idea. [PERSON], [PERSON], [PERSON], and [PERSON], contributed to the writing of the manuscript. [PERSON], [PERSON], [PERSON], and [PERSON] contributed to the reviewing and editing of the manuscript. [PERSON] supervised the project. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the National Key Research and Development Program of China (2019 YFB1803500), and National Natural Science Foundation of China (NSFC) (61860206006, 62005228).  
Data Availability Statement: Data sharing not applicable.
## 3 Methodology
### SEA Model
#### 3.2.4 Extended Solution  
In the high-frequency range, however, the behavior of a structure becomes energetic. As frequency increases, the mode shapes occur closer and closer together, which increases the modal density. Modal density is one of the main parameters in the SEA model. It quantifies the energy storage capacity of a vibration system. As the modal density increases, the calculation of mode shapes becomes a more time-intensive procedure, subsequently making the calculation of the interior sound pressure computationally expensive. In this study, the extended solution method of ACTRAN was employed for the calculation of interior noise from 1250 Hz to 4000 Hz. In this solution, SEA matrices are computed based on extrapolated quantities.