esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Principle Analysis of the Proposed JSG-DPD Technique
### OFDM Technique  
Conventional digital modulation is performed on a single carrier, such as QPSK, 16 QAM, etc. These single-carrier modulation methods are susceptible to inter-symbol interference (ISI), deteriorating the BER. It is subject to Rayleigh fading in a multipath propagation environment, which can cause burst BER. While orthogonal frequency division multiplexing (OFDM) divides the channel into several orthogonal subchannels in the frequency domain and adopts a subcarrier transmitted in parallel for modulation on each subchannel. In addition, a cyclic prefix is usually inserted between each OFDM symbol as a guard interval. Therefore, the OFDM system offers excellent anti-fading  
Figure 2: The system architecture of the proposed JSG-DPD technique.  
and anti-ISI capabilities, high spectrum efficiency, and is applicable to high-speed digital communication. The mathematical expression for the continuous OFDM baseband signal is  
\[x(t)=\frac{1}{\sqrt{M}}\sum_{u=-\infty}^{+\infty}\sum_{m=0}^{M-1}\mathbf{X_{u,m}}g(t -uT_{s})e^{\beta 2\pi m\frac{(t-uT_{s})}{T_{s}}}, \tag{1}\]  
where \(M\) represents the number of subcarriers, \(\mathbf{X_{u,m}}\) denotes the information carried on the \(m\)-th subcarrier in the \(u\)-th OFDM symbol, \(T_{s}\) is the duration of an OFDM symbol, and \(g(t)\) is the window function. The discrete OFDM baseband signal is derived by sampling the continuous OFDM baseband signal at the Nyquist sampling rate \(t=(w+uM)T_{s}/M\), denoted as  
\[x_{u,u}=\frac{1}{\sqrt{M}}\sum_{u=-\infty}^{+\infty}\sum_{m=0}^{M-1}\mathbf{X_{u,m }}g\bigg{[}\frac{wT_{s}}{M}\bigg{]}e^{\beta 2\pi\frac{wT}{M}}, \tag{2}\]  
where \(w=0,1,\ldots,M-1\). In order to enhance the LEO satellite-to-ground data transmission rate, this paper adopts such an OFDM signal with a high PAPR of 11.69 dB  
\[\text{PAPR}=\frac{\text{max}\{|x_{u,w}|^{2}\}}{E\{|x_{u,w}|^{2}\}}. \tag{3}\]  
Given that OFDM symbols are composed of several independently modulated subcarrier signals, when each subcarrier is in the same or comparable phase, the superimposed signal will be modulated by the same beginning phase signal, resulting in a huge instantaneous power peak, thus bringing a high PAPR. Since the limited linear operating range of a typical PA, OFDM signals with large PAPR are likely to get into the nonlinear region of the PA, causing serious degradation of the overall system performance.
## 1 Introduction
The upstream link between the V-N and the MBS or LOS/NLOS-LAP uses a distinct spectrum range for communication, whereas the downstream link between the MBS or LOS/NLOS-LAP and V-N uses a distinct spectrum range. The V-N's upstream connectivity for UAV-assisted C-V2X links uses shared channel transmissions with MBS, LOS-LAP, or NLOS-LAP via the Uu link. In contrast, transmission in the direct method for UAV-assisted C-V2X links connects transmitting V-N to a receiving V-N via a sidelink platform. Jamming signals as well as 3D beam-width variations can seriously degrade a UAV-assisted C-V2X framework's efficiency that uses shared V-N to V-N messages (including LOS-LAP, NLOS-LAP, and MBS) or direct V-N to V-N messages;thus, assessing the influence of jamming along with varying beam-width position while examining the reliability associated with a VN in vehicular networks is of vital importance.
## 2 System Model
### Content Request, Caching and Fetching  
In the considered system, each user randomly requests content following a certain probability distribution. We denote the probability of content \(k\) being requested as \(p_{k}\), \(k\in\mathcal{K}\) and \(\sum\limits_{k=1}^{K}p_{k}=1\). For convenience, it is assumed that the content has been sorted by popularity, which means that content \(k\) is the \(k\)th most popular content of all content. Content popularity is modeled using a Zipf distribution, and the probabilities of popular content being requested by a user can be described as \(P_{K}\sim\text{Zipf}\left(\alpha,K\right)\), and the probability of requesting the \(k\)th most popular content is  
\[p_{k}=\frac{1}{k^{\alpha\sum\
olimits_{n=1}^{K}n^{-\alpha}}}. \tag{2}\]  
In (2), \(\alpha\) is called the Zipf parameter, and \(\alpha\in[0,1]\). Zipf distribution indicates the popularity of the content, and \(\alpha\) affects the differences in content popularity. The smaller the parameter \(\alpha\) is, the smaller the popularity differences among content will be, and the converse is also true. In extreme cases, if \(\alpha=0\), all content will have the same probability of being requested, or if \(\alpha=1\), the probability of content being requested will be in an inverse ratio to the ranking of the content.  
The MBS is considered to be available for all users in the system and cached all contents. Differently, due to the limited power and capacity, each UAV can only connect some of the users and cache some of the content. Every UAV could choose to cache a complete content or just a part of it. The size of the \(k\)th content is denoted by \(L_{k}\), and proportion of the \(k\)th content cached in UAV\({}_{i}\) is denoted by \(z_{ik}\), \(z_{ik}\in[0,1]\), \(\forall i\in\mathcal{M}\), \(\forall k\in\mathcal{K}\). UAVs are able to implement the caching strategy through the high-capacity backhaul link with the MBS, which means that \(z_{ik}\) is changeable and that limited storage of UAV could be reasonably allocated.  
As for content fetching, UAVs and MBS cooperatively provide content for users. When the user initiates a data file request, the UAV serving the user only transmits the part of the file cached by the BTS, and the user reconstructs the file data from every connected UAV to obtain a complete file. If the requested file is not cached in the related UAVs, the missing file or part will be provided by MBS.
# Unsupervised SAR Imagery Feature Learning with Median Filter-Based Loss Value
## 4 Loss Value Calculation  
For image generative neural networks, two main types of Per Pixel Loss Function values were used, namely the Mean Absolute Error (MAE) and Mean Square Error (MSE). They are defined in Equations (1) and (2).  
\[\text{MAE}=\frac{\sum_{j=1}^{n}\sum_{i=1}^{n}|x(i,j)-y(i,j)|}{n^{2}} \tag{1}\]  
\[\text{MSE}=\frac{\sum_{j=1}^{n}\sum_{i=1}^{n}(x(i,j)-y(i,j))^{2}}{n^{2}} \tag{2}\]  
where: \(n\)--image height and width, \(x(i,j)\,y(i,j)\)--pixels in positon \((i,j)\) of generated and target image.  
MSE is more popular, but it focuses on outliers (single high error pixels), which is undesirable in the case of heavily speckled SAR imagery. Additionally, MSE works the best for images with normally distributed values, which requires further image processing. As a result, the MAE loss function was chosen for DNN training.  
A different class algorithm of loss value calculation is SSIM. It is a neural network-based algorithm for common feature detection (image similarity). Due to the unusual image type, it does not apply well to SAR imagery. It can be seen in Tables 1 and 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Neural Network**} & \begin{tabular}{c} **Inference** \\ **Time [s]** \\ \end{tabular} & \begin{tabular}{c} **Unfiltered** \\ **MSE** \\ \end{tabular} & \begin{tabular}{c} **Filtered** \\ **MAE** \\ \end{tabular} & \begin{tabular}{c} **Noised Unfiltered** \\ **MSE** \\ \end{tabular} & \
\begin{tabular}{c} **Noised Filtered** \\ **MAE** \\ \end{tabular} \\ \hline \multirow{2}{*}{Compressing CNN} & Without filters & 0.237 & 0.0382 & 0.1493 & 0.0030 & 0.0410 & 0.0408 & 0.1552 & 0.0407 & 0.1549 \\ \cline{2-10}  & With 7 \(\times\) 7 Filter & 0.234 & & 0.0018 & 0.0305 & & & 0.0367 & 0.1480 \\ \hline \multirow{2}{*}{Fully CNN} & Without filters & 0.246 & 0.0333 & 0.1380 & 0.0025 & 0.0372 & 0.0352 & 0.1424 & 0.0353 & 0.1424 \\ \cline{2-10}  & With \(\times\) 7 Filter & 0.249 & & 0.0003 & 0.0128 & & & 0.0344 & 0.1430 \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Compressing CNN \\ with SSIM metric \\ \end{tabular} } & Without filters & 0.238 & 0.1924 & 0.3557 & 0.1575 & 0.3548 & 0.1925 & 0.3559 & 0.1925 & 0.3559 \\ \cline{2-10}  & With \(\times\) 7 Filter & 0.235 & & 0.0306 & 0.1441 & & & 0.0569 & 0.1802 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of Neural network performance over test set. Green background determines the better results, while gray font means that the neural network was not trained for this task (filtered versus unfiltered output).
# Modeling of Satellite-to-Underwater Integrated FSO-PON System Using NOMA-VLC
###### Abstract  
In recent years, optical wireless communication has promised several benefits over radio frequency communication in atmospheric, deep space and underwater communications. Satellite-to-underwater communication technology can be applied to commercial, naval, scientific and engineering operations because of its high data rate, high security, long-reach and low cost. In this paper, a high-speed, long-reach integrated free space optics (FSO)-passive optical network (PON) system using non-orthogonal multiple access visible light communication (NOMA-VLC) is proposed. It poses a 10/2.5 Gbps per channel bit rate for satellite-to-underwater applications. Numerically calculated results provide the splitter power budget of \(-35\) dBm in the downlink and \(-32\) dBm in the uplink. Additionally, a receiver sensitivity of 23 dB in the downlink and 10 dB in the uplink direction can be obtained in the system using a modified new zero cross-correlation (MNZCC) code under clear environment conditions. Again, the simulative analyses indicate that the suggested system supports 290 underwater devices successfully and offers a high 10 dBm signal-to-noise ratio over 10 km FSO, 100 km fiber and 5 m VLC range. Moreover, it provides a signal-to-noise ratio of 39 dB, with \(-9\) dBm received optical power at 300 fields of view under fiber-wireless channels' impairments. We argue that the suggested system is a symmetric system adapted to different link distances and which offers improved receiver sensitivity and high received optical power at a \(10^{-9}\) bit error rate (BER). The comparative analysis shows the advantages of the suggested system over previously reported works.  
optical code division multiple access (OCDMA); passive optical network (PON); time and wavelength division multiplexing (TWDM); wavelength division multiplexing (WDM); visible light communication (VLC); under optical wireless communication (UOWC)
# Connecting the Brain with Augmented Reality: A Systematic Review of BCI-AR Systems
## 4 Results
### Active BCI
#### 4.3.3 Classification  
For the classification process, CCA and FBCSP were employed by [51], SVM was utilized by [54], and ref. [53] used their own proposed algorithm. Each classifier was selected based on its effectiveness in recognizing specific features within the EEG signals.
###### Abstract  
Index modulation (IM) is considered a promising approach for fifth-generation wireless systems due to its spectral efficiency and reduced complexity compared to conventional modulation techniques. However, IM faces difficulties in environments with unpredictable channel conditions, particularly in accurately detecting index values and dynamically adjusting index assignments. Deep learning (DL) offers a potential solution by improving detection performance and resilience through the learning of intricate patterns in varying channel conditions. In this paper, we introduce a robust detection method based on a hybrid DL (HDL) model designed specifically for orthogonal frequency-division multiplexing with IM (OFDM-IM) in challenging channel environments. Our proposed HDL detector leverages a one-dimensional convolutional neural network (1D-CNN) for feature extraction, followed by a bidirectional long short-term memory (Bi-LSTM) network to capture temporal dependencies. Before feeding data into the network, the channel matrix and received signals are preprocessed using domain-specific knowledge. We evaluate the bit error rate (BER) performance of the proposed model using different optimizers and equalizers, then compare it with other models. Moreover, we evaluate the throughput and spectral efficiency across varying SNR levels. Simulation results demonstrate that the proposed hybrid detector surpasses traditional and other DL-based detectors in terms of performance, underscoring its effectiveness for OFDM-IM under uncertain channel conditions.  
Orthogonal frequency-division multiplexing (OFDM); wireless communication; index modulation (IM); deep learning (DL); imperfect channel conditions; bidirectional long short-term memory (Bi-LSTM); convolutional neural network (CNN); hybrid deep learning (HDL)  
30 October 20241 November 11 November 20241 November 15 November 20241 November 15 November 20241 November 15 November 20241 November 15 November 20241 November 12, 3583. [[https://doi.org/10.3390/math12223583](https://doi.org/10.3390/math12223583)]([https://doi.org/10.3390/math12223583](https://doi.org/10.3390/math12223583))
## 3 Magnetic Field Analysis and Calculation of the Eddy Current Braking Force
### The Eddy Current Braking Force  
According to previous analysis, the normal flux density in the cooling plate can be written as  
\[B_{\mathrm{y}}=C_{1}m_{1}\Big{[}e^{-\alpha y}\mathrm{cos}\Big{(}\omega t- \frac{\pi}{\tau}x-\beta y+\theta_{1}\Big{)}\ +e^{\alpha y}\mathrm{cos}\Big{(}\omega t-\frac{\pi}{\tau}x+\beta y+ \theta_{1}\Big{)}\Big{]} \tag{13}\]  
At any time, differential form of the tangential eddy current braking force is  
\[dF_{\mathrm{x2}}=B_{\mathrm{y}}J_{\mathrm{z}}l_{\mathrm{w}}dxdy \tag{14}\]  
where \(l_{w}\) is the horizontal width of the cooling plate.  
Figure 3 shows the analytical model of eddy current braking force. From \(t=0\), the expression of the eddy current braking force induced in the cooling jacket can be written as follows  
\[\begin{array}{l}F_{\mathrm{x2}}\ \ =\iint dF_{\mathrm{x2}}dxdy\\ \ \ =\frac{2C_{1}2^{2}\pi\alpha\beta l_{\mathrm{w}}}{\mu_{0}\tau}\int_{-\frac{1} {2}-vt}^{\frac{L_{\mathrm{p}}}{L_{\mathrm{p}}}-vt}\int_{-\frac{1}{2}}^{\frac{ L_{\mathrm{p}}}{L_{\mathrm{p}}}}\big{[}e^{-\alpha y}\mathrm{cos}\big{(}-\frac{\pi}{ \tau}x-\beta y+\theta_{1}\big{)}\ +\ e^{\alpha y}\mathrm{cos}\big{(}-\frac{\pi}{\tau}x+\beta y+ \theta_{1}\big{)}\big{]}^{2}dxdy\\ \ \ =\frac{2C_{1}2^{2}\pi l_{\mathrm{w}}}{\mu_{0}\tau}\big{[}\beta \mathrm{sinh}\big{(}\alpha l_{\mathrm{p}}\big{)}+\mathrm{asin}\big{(}\beta l_{ \mathrm{p}}\big{)}\big{]}L_{\mathrm{p}}+\frac{C_{1}2C_{2}\alpha\beta l_{ \mathrm{w}}}{\mu_{0}}\mathrm{sin}\big{(}\frac{\pi}{\tau}L_{\mathrm{p}}\big{)} \mathrm{sin}(2\omega t+\theta_{2})\end{array} \tag{15}\]  
where  
\[\left\{\begin{array}{l}C_{2}=\sqrt{C_{3}{}^{2}+4l_{\mathrm{p}}{}^{2}+2C_{3} \mathrm{sin}(\theta_{3}-\varphi)}\\ \ \ \theta_{2}=2\theta_{1}+\arctan\frac{C_{3}\mathrm{cos}(\theta_{3}-\varphi)+2l_ {\mathrm{p}}}{C_{3}\mathrm{cos}(\theta_{3}-\varphi)+2l_{\mathrm{p}}}\\ \ \ \ C_{3}=\sqrt{2\mathrm{cosh}\big{(}2\alpha l_{\mathrm{p}}\big{)}-2\mathrm{ cos}\big{(}2\beta h_{\mathrm{p}}\big{)}}/\gamma\\ \ \ \theta_{3}=\arctan\big{(}\mathrm{coth}\big{(}\alpha h_{\mathrm{p}}\big{)} \mathrm{tan}\big{(}\beta l_{\mathrm{p}}\big{)}\big{)}\end{array}\right.\]  
From Equation (15), it can be noticed that the eddy current braking force includes two parts, the constant component and the volatile component. The amplitude and fluctuation of the eddy current braking force are determined by the size of the cooling jacket and the motor's speed.  
Figure 3: The analytical model of eddy current braking force.
# Integrated Evaluation of Reliability and Power Consumption of Wireless Sensor Networks
## 7 Related Work
### Sensitivity Analysis  
The use of sensitivity analysis in WSNs evaluates the factors that impact on the location algorithms (identifying the position of the sensor nodes) [41; 42] and the routing protocol.  
[PERSON] [41] studied the WSN for agricultural precision. He focuses on the protocol stack (link and physical layers) and shows the importance of the location algorithms (identifying the position of the sensor nodes) for this type of application. In particular, he used the sensitivity analysis to determine the amount of error (output) according to the parameters utilised by the location models (input). In particular, it defines that the location based on the arrival time is more precise than one based on the received signal strength.  
Similarly, [PERSON] and [PERSON] [42] evaluated the factors that influence the location in a variant of WSN to the aquatic environment (Underwater Wireless Sensor Array Networks--UWSAN). A UWSAN has many sensor nodes equipped with an array of sensors to collect and process electromagnetic or acoustic signals. In this case, acoustic signals are used by location algorithms to determine the position of the sensor nodes. More specifically, they studied a relation between the Direction of Arrival (DoA), that is angular of reception, to the location based on the Least-Square Method. Among several findings, they define that the error of the source location and the distance between the source and the sensor node increase when the number of the sensor nodes and DoA (angular of reception) are keeping.  
Different from previous papers, our work does the sensitivity analysis of the WSN, while they evaluated variants of the WSN; and we show the impact of the each parameter on the power consumption and reliability of the WSN, while they demonstrate the influence on the location algorithms.  
Our work is close to [43]: both perform the sensitivity analysis of the WSN and showed the impact of the parameters on the WSN power consumption. However, they are different in many aspects. For example, he evaluated the power consumption of the WSN using NS-2 and we used formal models. He considered that sensor node could move and used the radio propagation model as a parameter, while we studied a static network and only one radio propagation model. On the other hand, we considered the routing protocol as a parameter and evaluated the impact of each factor on power consumption and reliability of WSN. Besides that, we proposed a methodology for development and evaluation a WSN that is fully supported by the implemented tooling.
## 5 Performance Evaluation
### Simulation Results
#### 5.3.3 Number of Backup Flow Rules  
Figures 11 and 12 show the numbers of backup flow rules required by the three schemes. The number of backup flow rules is the number of forwarding rules that need to be configured in switches before link failures happen, including flow table and group table entries, which cause extra consumption of switches' TCAMs. As can be seen from Figure 11, the proactive scheme requires the most backup flow rules, and the reactive scheme requires the least. It can be explained as the proactive scheme needs to allocate a backup path for each flow, so the number of backup paths increases as the number of flows increases, and a backup path may correspond to multiple flow table and group table entries. The reactive scheme does not need to install any backup flow rules in advance. It only temporarily calculates backup paths and installs backup flow rules after a link failure, which is basically unrelated to the number of flows. No matter how the number of flows increases, the reactive scheme keeps the number of backup flow rules small and almost unchanged. The FAPR scheme combines the ideas of proactive and reactive schemes, and the number of backup flow rules needed lies somewhere in between. On one hand, FAPR adopts pro-VLAN to prepare backup paths before link failures, which can keep the number of backup flow rules independent from the number of flows. On the other hand, FAPR needs to calculate three backup paths for each link, and stores essential information into switches in advance, which leads to a slight increase of backup flow rules as the number of flows increases. However, the number of backup flow rules required by FAPR is always much less than the proactive scheme. When the number of flows is two, the backup flow rules required by the FAPR scheme are about 61% of those required by the proactive scheme. Moreover, as the number of flows increases, this gap between FAPR and the proactive scheme becomes more pronounced. When the number of flows increases to 4, 6, 10, 16, 24, and 34, the corresponding ratio between FAPR and the proactive scheme is reduced to about 36%, 28%, 21%, 17%, 15%, and 14% between FAPR and the proactive scheme.  
In addition, Figure 12 shows the trend of the numbers of backup flow rules required by the three schemes as the number of flows increases. The proactive scheme needs the most backup flow rules, and the number of backup flow rules increases with exponential growth as the number of flows increases. It can be inferred that, when the number of flows increases further, the number of backup flow rules required by the proactive scheme may even exceed the number of primary flow rules, which is unacceptable. The reactive scheme keeps the number of backup flow rules basically stable with low volatility. The FAPR  
Figure 10: Flow interruption rates using three schemes.  
scheme also lies somewhere in between, and the number of backup flow rules increases with linear growth, which is applicable in practice.
## 4 Results and Discussion
### Dataset Description and Assessment
#### 4.1.2 Qualitative Analysis  
Figure 6 shows the qualitative experiment results of some areas selected from the Daxing Airport loop verification set. The road scene is divided into streetlights, signs, lane lines, pavement, and guardrails. We used MLS road field scenic spots such as Beijing-Taiwan Expressway, Beijing-Guangzhou Expressway, and Beijing South Sixth Ring Road to train our network model. The section of Daxing Airport loop was taken as a verification test to ensure the generalization of the fusion network model.  
Table 1 shows the quantitative results of the fusion network model in the Daxing Airport loop section. From an overall perspective, the integrated network model achieved an OA of 99.1% and a mIOU of 72.6 in the MLS Daxing Airport section dataset, which verified the effectiveness of the new deep learning network integrated with two modules in MLS road point clouds scene segmentation. At the same time, the fusion network has a certain effect on the five road features. Among them, the pavement and guardrail  
Figure 6: Global view of the qualitative results of the Daxing Airport loop validation set, where the global display of region 1, region 2, and region 3. The blue color in the segmentation results represents the road surface, the orange color represents the guardrail, the cyan color represents the streetlight, the green color represents the signage, and the white color represents the lane line.  
segmentation accuracy reached 99.1% and 99.9%, in all categories of the best performance. The segmentation effect of streetlamps is average, only 38.5%. The main reason for this result is that in the road field scenic spot cloud collected by MLS, the amount of streetlamp features is less than other features, and the point clouds data are incomplete and have other problems.  
Figure 7 shows a detailed description section of some sections of the segmentation result of the fusion network. Integrates the two modules of the deep learning network can correctly segment most of the MLS road field scenic spot cloud data, which has a remarkable segmentation effect on the road scene and noticeable geometric features. In previous research, MLS usually drives at a low speed in the process of collecting road point clouds data to increase the density of the point clouds, improve the features of ground object point clouds data, or combine with images to increase the richness of feature information. However, it is limited by weather, environment, time, and other conditions. In contrast, when this method only relies on MLS point clouds data, it can accurately segment road point clouds data collected by MLS at high speed. Three-dimensional point clouds data can be used to efficiently collect road point clouds data at night when the traffic flow is low, improving the utilization efficiency of MLS.