esa-sceva/satcom-chunk-collection huggingface dataset:
# ARNS: Adaptive Relay-Node Selection Method for Message Broadcasting in the Internet of Vehicles
## 5 Results and Analysis  
To prove the effectiveness, simulations were conducted on a real-world map shown in Figure 1, which is a part of the urban map in Zhangjiajie city, Hunan Province, China. In addition, to reflect the real-time advantages with the black-burst, ARNS was compared with a beacon-based method that uses RTAD [20] to select relay-nodes in urban scenarios and the GPSR method [19] on the curved road combined with the adaptive mechanism proposed in this paper. Additionally, a black-burst-based method was also used for comparison, which substitutes EPBP with 3P3B in ARNS (called the 3P3B-based method), to verify ARNS' improvement. These results and analysis are presented in Section 5.2.  
In addition, to demonstrate the advantages of considering obstacles in curved road scenarios, we compared ARNS with the complete relay-node selection method [25], which is an EPBP-based relay-node selection method well-qualified in the curved road scenarios without considering obstacles. These results will be discussed in Section 5.3.
# Quantum PSO-Based Optimization of Secured IRS-Assisted Wireless-Powered IoT Networks
###### Abstract
The authors in [8] analyzed and optimized the achievable secrecy capacity, revealing that artificial noise (AN) can enhance secrecy performance by designing it to disrupt the eavesdropping channel, with the AN vector placed in the null space of the legitimate channel. In [9], a joint optimization of the AN vector and beamforming vector was proposed to minimize transmit power. Moreover, they addressed the issue of imperfect channel state information (CSI) for the eavesdropper (Eve) and incorporated this uncertainty into the optimization process.
## 4 Centralized Algorithm
### CE-Based Scheduling Algorithm
#### 4.1.3 Removing Unqualified Transmissions  
The algorithm proposed above cannot ensure that the data rate of each transmission is as high as the given threshold \(R_{\text{thres}}\). Since we put our purpose on improving energy efficiency, it is reasonable to close those transmissions that are estimated to be unqualified.  
Algorithm 2 summaries the entire RB scheduling algorithm proposed above.  
```
1:Calculate \(R_{k}^{n}\) according to Equation (2).
2:Initialize probability distribution \(\mathbf{q}^{m}(m=1,\cdot\cdot\cdot,M)\) according to Equation (10).
3:for\(t=1:t_{\text{max}}\)do
4:for\(m=1:M\)do
5: Process Algorithm 1, and output \(\mathbf{X}_{\text{out}}^{m},(m=1,\cdot\cdot\cdot,M)\)
6:endfor
7:Map obtained \(\mathbf{X}_{\text{out}}^{m}\) into \(\{\beta_{m,k}^{n}\}\).
8:Update \(R_{k}^{n}\) according to Equation (3), and obtain \(\{\beta_{m,k}^{n}\}\).
9:if All of the elements in \(\mathbf{q}^{m}(m=1,\cdot,\cdot,\cdot,M)\) converge then
10: Output \(\mathbf{X}_{\text{out}}\)
11:BREAK
12:endif
13:endfor
14:Map obtained \(\mathbf{X}_{\text{out}}^{m}\) into \(\{\beta_{m,k}^{n}\}\).
15:for\(n=1:N_{\text{RB}}\)do
16:for\(u=1:K\)do
17:if\(R_{k}^{n}<R_{\text{thres}}\)then
18: Let the corresponding \(\beta_{m,u}^{n}=0\).
19:endif
20:endfor
21:endfor
```  
**Algorithm 2** Centralized RB scheduling algorithm.
## 2 Related Work
### Encrypted Traffic Classification and Feature Engineering
#### 2.2.2 Machine Learning-Based Models  
There are a lot of traditional machine learning methods, and most of them have been used in encrypted traffic classification. The most used ones are ensemble learning methods, which perform a better effect. There are integrated Markov chain classifiers WENC [20], gradient boosting tree [35] and RF [36]. After that, there are also some optimized methods such as the quadratic voting RF [37]. However, [PERSON] et al. [38] proposed a traffic camouflage method, effectively protecting user traffic from recognition by RF. As a result, it may be difficult for traditional machine learning methods to classify encrypted traffic in the future.
# JARUA: Joint Embedding of Attributes and Relations for User Alignment across Social Networks
## 5 Experiments
### Data Sets  
The data sets include a pair of social networks and another pair of academic coauthor networks. Their statistics are listed in Table 2.  
**Social networks** : This data set contains two popular Chinese social networks: Weibo (Sina Weibo) and Douban denoted as WD. Some Douban users have revealed their own Weibo accounts on their homepages. Thus, these accounts are taken as pre-matched users  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Network**} & \multirow{2}{*}{**\#Users**} & \multirow{2}{*}{**\#Relations**} & \multicolumn{1}{c}{**Min. Degree**} & \multicolumn{1}{c}{**Ave. Degree**} & \multicolumn{1}{c}{**Max. Degree**} & \multicolumn{1}{c}{**Ave. Coeff.**} & \multicolumn{1}{c}{**\#Aligned Pairs**} \\ \cline{3-8}  & Weibo & 9714 & 117,218 & 2 & 12.1 & 607 & 0.112 & \\ \cline{2-8}  & Douban & 9526 & 120,245 & 2 & 12.6 & 608 & 0.101 & \\ \hline \multirow{3}{*}{Coauthor networks} & DBLP17 & 9086 & 51,700 & 2 & 5.7 & 144 & 0.280 & \\ \cline{2-8}  & DBLP19 & 9325 & 47,775 & 2 & 5.1 & 138 & 0.322 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: The statistics of our data sets.  
for the alignment task. The two social networks enjoy typical social network characteristics, e.g., approximate power-law degree distribution (Figure 3) and high aggregation coefficient.  
**Coauthor networks**: DBLP is a computer science coauthorship network where two authors are linked if they have published at least one paper together. Every author on DBLP is assigned a unique key, which is viewed as the ground truth for UA. DBLP networks also enjoy social network characteristics of approximate power-law degree distribution (Figure 3) and high aggregation coefficient.
# SGST-YOLOv8: An Improved Lightweight YOLOv8 for Real-Time Target Detection for Campus Surveillance
## 2 Method
### Lightweight Neck Network Module: GSConv  
GSConv (Group Separable Convolution) is a unique convolution operation technique [23]. Its core objective is to enhance the performance of Depth-wise Separable Convolution (DSC) [24] by making its output closer to Standard Convolution (SC) [25]. The design concept of GSConv is to find an optimal balance between the accuracy of the model and the speed of calculation. By using GSConv, the computational cost of the model can be effectively reduced while maintaining or even improving the performance of the model.  
In the improved model of this study, the Standard Convolution (SC) in the neck networks is replaced by GSConv. The reason for not using this method in the backbone network is that employing GSConv in the backbone may lead to excessively high computa  
Figure 3: Network structure diagram of SGST-YOLOv8 with added modules. “(a)–(c)” They respectively represent three branches capturing cross-dimensional interaction information between different dimensions.  
tional complexity. Meanwhile, in the neck part, the feature map has become elongated, and hence, transformation is no longer needed [26]. Replace the formula as  
\[YOLOv8(input)=GSConv(SC(C2/2\times 2)) \tag{1}\]  
Here, \"input\" refers to the input feature map, and (C2/2 \(\times\) 2) represents the convolution results after two depth-wise convolutions. GSConv uses a combination of SC, DSC, and shuffle-mixed convolutions for dense convolution calculations, maximizing the retention of hidden connections between each channel. Consequently, it achieves the same output effect as SC with fewer computational costs. In this study, a cross-stage part network called VoV-GSCSP module was used, which is generated based on GSConv. The introduction of convolutions like GSConv and VoV-GSCSP increases the model's non-linear capability while maintaining parameter sharing, thereby enhancing the model's generalization ability [27]. It decreases computational complexity and simplifies network structure while preserving adequate accuracy. This helps to reduce the risk of overfitting in the model.
## 3 System Layout
### Meteorological Data  
Meteorological data, including solar radiation, wind speed, ambient temperature, and more important information are essential to design, size and construct an optimal PV system that would fully cover the required energy by the load.  
Solar radiation is the main driver of various physical, chemical, and biological processes on the surface of the earth. Accurate and complete solar radiation data are indispensable to any solar energy-related research.  
The meteorological data shown in this section were collected by a weather station built at the specified location. The weather data were collected every ten minutes during 2013. The results are shown and illustrated in Figure 9[19; 20].  
It can be noticed that the peak solar radiation was detected in April, at 1376 W/m\({}^{2}\). However, this does not mean that April was the month with the highest irradiation rate. For instance, the monthly average solar radiation was registered to be maximum in June, at around 1000 w/m\({}^{2}\), compared to 810 W/m\({}^{2}\) in April. The daily average solar radiation is shown in Figure 10[19; 20].  
Figure 9: Solar irradiation at 10-min intervals over one year [19; 20].  
It can be seen from Figure 10 that June had the highest average irradiation rate of all the months, while December had the lowest average irradiation rate of the year [19,20]. The maximum average irradiation in June was 993 W/m\({}^{2}\), while the maximum average irradiation in December was 487 W/m\({}^{2}\). The yearly average solar radiation profile is shown by the red dotted line, and it is illustrated clearly in Figure 10. The yearly average solar irradiation was 762 W/m\({}^{2}\).
## 3 Design of Proposed Intelligent FSO System
### Transmitter Module
This would enable dynamic performance optimization under varying atmospheric conditions

Conceptualization, S.A.A.E.-M. and A.A.; methodology, S.A.A.E.-M. and A.A.; software, S.A.A.E.-M. and A.A.; validation, A.A.; formal analysis, S.A.A.E.-M. and A.A.; investigation, S.A.A.E.-M. and A.A.; resources, S.A.A.E.-M. and A.A.; writing--original draft preparation, S.A.A.E.-M.;

Figure 15: Comparison between model complexity. * (20) [PERSON]; [PERSON]; [PERSON] Optical Power Allocation for Adaptive Transmissions in Wavelength-Division Multiplexing Free Space Optical Networks. _Digit. Commun. Netw._**2015**, \(1\), 171-180. [CrossRef]
* (21) [PERSON]; [PERSON]; [PERSON] Comparison of Laser Beam Propagation at 785 Nm and 1550 Nm in Fog and Haze for Optical Wireless Communications. In _Optical Wireless Communications III_; [PERSON]; SPIE: Ile-de-France, France, 2001; Volume 4214, pp. 26-37. * (22) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Enabling High Capacity Flexible Optical Backhaul Data Transmission Using PDM-OAM Multiplexing. In Proceedings of the 2024 IEEE Global Communications Conference (GLOBECOM), Cape Town, South Africa, 8-12 December 2024; pp. 403-408. * (23) [PERSON]; [PERSON]; [PERSON] [PERSON] Channel Modeling for Terrestrial Free Space Optical Links. In Proceedings of the 2005 7 th International Conference Transparent Optical Networks, Barcelona, Spain, 7 July 2005; Volume 1, pp. 407-410. * (24) [PERSON]; [PERSON]; [PERSON]; [PERSON] Comparing the Snow Effects on Hybrid Network Using Optical Wireless and GHz Links. In Proceedings of the 2009 International Workshop on Satellite and Space Communications, Siena, Italy, 9-11 September 2009; pp. 171-175. * (25) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Performance Analysis of Hybrid PDM-SAC-OCDMA-Enabled FSO Transmission Using ZCC Codes. _Appl. Sci._**2023**, _13_, 2860. [CrossRef]
* (26) [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] PP-CNN: Probabilistic Pooling CNN for Enhanced Image Classification. _Neural Comput. Appl._**2024**, _37_, 4345-4361. [CrossRef]

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# The Customized Automatic Processing Framework for HY-2A Satellite Marine Advanced Products
## 2 Products and Related Work  
This section reviews the sensors carried on HY-2A satellites and their products, and the existing ground application system. It also discusses the relevant research on remote sensing data services over the past decades and the problems that we are concerned with.
## 2 Methods
### Channel Equalization Based on Fractional Fourier Transform
After transformation into the FrFT domain with optimal order, we obtain  
\[R_{p_{0}}(u)=\sum_{n}x[n]\cdot H_{p_{0}}(u)\cdot e^{-\beta\pi uuT_{s}}+N_{p_{0 }}(u) \tag{16}\]  
To equalize the frequency-selective channel, we apply a frequency-domain equalizer  
\[\widetilde{\mathrm{X}}(u)=\frac{R_{p_{0}}(u)}{H_{p_{0}}(u)+\varepsilon} \tag{17}\]  
where \(\varepsilon\) is a regularization constant to mitigate division by deep-fade components. Next, we perform an inverse FrFT to recover the time-domain signal  
\[\widetilde{\mathrm{x}}(t)=\mathcal{F}_{-p_{0}}(\widetilde{\mathrm{S}}(u)\} \tag{18}\]  
The whole flow chart of channel equalization in fractional Fourier domain is shown in Figure 2. The specific steps for implementing fractional Fourier domain channel equalization are outlined as follows:  
1. Separate the training sequence and data sequence of the baseband data. 2. Transform the training sequence into fractional Fourier domains of varying orders for least squares channel estimation and select the optimal order. Set the tap coefficients of the equalizer based on the least squares channel estimation of the optimal order. 4. Partition the received signal sequence into blocks for the optimal-order fractional Fourier transform. 5. Apply a multiplicative filter and revert the optimal order back to the time domain. 6. Convert the segmented processed data into serial data, which can then be fed into a decision feedback equalizer in the time domain for decision making.
# Implementation of a Breast Phantom with Acoustic Properties for Ultrasonic Thermometry
## 2 Theoretical Framework
### Breast Thermal and Ultrasonic Properties of Interest
The authors also introduced a non-uniform flow-through tissue phantom to simulate blood flow, effectively replicating the acoustic and thermal properties of the real tissues. The properties of this phantom align with typical literature values for human tissue, demonstrating that these materials can accurately simulate both thermal and acoustic characteristics. [PERSON] et al. [61] developed a breast phantom by mixing agar in deionized water, as has been performed in previous studies, and adding gelatin to combine acoustic and mechanical properties for use in Positron Emission Tomography (PET), B-mode ultrasound, and elastography imaging. The study found that the phantom's acoustic and mechanical properties closely resembled those of real tissues, including fat, gland, fibrous tissue, and carcinoma. The phantom design consisted of a single volume of gel simulating fat tissue with inserts of varying dimensions (1 to 3 cm) representing cancer lesions. [PERSON] et al. [62] proposed a thermo-dielectric breast phantom designed for application in microwave hyperthermia, simulating the density, specific heat capacity, and thermal conductivity of breast tissue. Additionally, the phantom incorporates permittivity and conductivity as dielectric properties. The authors used a mixture of gelatin, oil, and salt in water at varying concentrations to emulate different tissue types, including glandular, adipose, skin, and tumor tissues, resulting in a heterogeneous, anatomically realistic breast phantom. Notably, the results demonstrated thermal properties closely matching those of real breast tissues. Although the tests were not conducted at varying temperatures, the study achieved good approximations of the thermal properties. These results align with previously validated findings from other authors [63; 64; 65; 66; 67; 68; 69], but this phantom is suitable for electromagnetic applications.
_article_  
Multicriteria Prediction and Simulation of Winter Wheat Yield Using Extended Qualitative and Quantitative Data Based on Artificial Neural Networks  
**[PERSON] 1,*+**, [PERSON] 2\({}^{\text{\textregistered}}\), [PERSON] 2\({}^{\text{\textregistered}}\),  
**[PERSON] 3\({}^{\text{\textregistered}}\), [PERSON] 2\({}^{\text{\textregistered}}\), [PERSON] 2\({}^{\text{\textregistered}}\), [PERSON] 2\({}^{\text{\textregistered}}\) and [PERSON] 2\({}^{\text{\textregistered}}\)  
Received: 14 May 2019; Accepted: 6 July 2019; Published: 10 July 2019  
**Abstract:** Wheat is one of the main grain species as well as one of the most important crops, being the basic food ingredient of people and livestock. Due to the importance of wheat production scale, it is advisable to predict its yield before harvesting. However, the current models are built solely on the basis of quantitative data. Therefore, the aim of the work was to create three multicriteria models for the prediction and simulation of winter wheat yield, which were made on the basis of extended quantitative and qualitative variables from field research in the year period 2008-2015. Neural networks with MLP (multi-layer perceptron) topology were used to build the following models, which can predict and simulate the yield on three dates: 15 April, 31 May, and 30 June. For this reason, they were designated as follows: QQWW15_4, QQWW31_5, and QQWW30_6. Each model is based on a different number of independent features, which ranges from 19 to 25. As a result of the conducted analyses, a MAPE (mean absolute percentage error) forecast error from 6.63% to 6.92% was achieved. This is equivalent of an error ranging from 0.521 to 0.547 tha-1, with an average yield of 6.57 ton per hectare of cultivated area. In addition, the most important quantitative and qualitative factors influencing the yield were also indicated. In the first predictive range (15 April), it is the average air temperature from 1 September to 31 December of the previous year (T9-12_PY). In the second predictive range (31 May) it is the sum of precipitation from 1 May to 31 May, and in the third (30 June) is the average air temperature from 1 January to 15 April of the year (T1-4_CY). In addition, one of the qualitative factors had a significant impact on the yield in the first phase-the type of forecrop in the previous year (TF_PY). The presented neural modeling method is a specific extension of the previously used predicting methods. An element of innovation of the presented concept of yield modeling is the possibility of performing a simulation before harvest, in the current agotechnical season. The presented models can be used in large-area agriculture, especially in precision agriculture as an important element of decision-making support systems.  
**Keywords:** winter wheat yield; artificial neural networks; yield prediction; yield simulation; MLP network; sensitivity analysis; precision agriculture; machine learning
## 4 Evaluation Study
### Setup
#### 4.1.2 Environments
\begin{table}
\begin{tabular}{p{142.3 pt} p{284.5 pt}} \hline \hline
**Attack** & **Description** \\ \hline GNSS signals spoofing & The attacker emits a fake GNSS signal (i.e., providing false positioning and timing data) to the area that the CAV is moving through. The CAV receives this signal and processes false vehicle location. As a result, the moving CAV gets disoriented, demonstrating unexpected and dangerous behavior. \\ \hline GNSS signals jamming & The attacker attempts to disrupt GNSS communications by jamming GNSS signals. As GNSS signals are vulnerable to intentional interference, the attacker uses a specific device (e.g., Software Defined Radio (SDR) hardware) to overpower GNSS signals so that they cannot be acquired and tracked by the GNSS receiver. \\ \hline CAN DoS attack & The attacker floods the CAN bus (i.e., the control area network of the CAV that implements a message-based protocol, which enables communication between the in-vehicle components, such as micro-controllers and devices) with messages with high priority. As a result, the CAN bus becomes unresponsive, and thus, there is no control over the CAV, which demonstrates unexpected and dangerous behavior. \\ \hline Alteration of CAN messages & The attacker tries to manipulate the CAN frames. Data manipulation can be defined as the insertion of an unauthorized CAN frame into the network. Since the CAN protocol does not have an authorization mechanism, a malicious node can attach to the network and inject malicious data. An attacker exploits this vulnerability and sends a CAN message to activate emergency brakes while the vehicle is moving. \\ \hline Alteration of camera stream & The attacker attempts to alter the video transmitted over the Ethernet network of the CAV. They intercept the video stream and tamper with it. Next, they send it to the supervision center that monitors the CAVs. This results in image elements not being identified by the supervision center correctly. As a result, the supervision center cannot identify the actual situation of the CAV through the internal camera. \\ \hline False sensor reading & The attacker manipulates the speedometer (i.e., the gauge that measures the instantaneous speed of the CAV) readings to trick the CAV into making bad decisions. The attacker replaces the existing speedometer with a malicious one. Therefore, the control module receives an erroneous value on the speed of the new device, which leads the CAV to make bad decisions and demonstrate unexpected and dangerous behavior. \\ \hline Malware/Ransomware & The attacker attempts to penetrate and infect the vehicle with malware (e.g., ransomware). They use a Trojan disguised as a legitimate file to trick the end-users into downloading or installing it on a vehicle. The malware encrypts the vehicle’s files, making them inaccessible and the vehicle inoperable, and demands a ransom payment to decrypt them. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The attack scenarios performed in our study.