esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Routing Protocols: A Brief Overview  
In such a dynamic environment of Wireless Body Area Network (WBAN), it is very difficult to attain the route within the stipulated time. Thus, to achieve that, we generally used two different routing protocols, i.e., proactive and reactive routing protocols. Our study has focused on the routing algorithms to figure out the best suited for WBAN. The proper overview of routing protocols has been as following as:  
Figure 1: General WBAN Architecture.
## 3 Results
### Second Evaluation of Trained Models
#### 3.4.2 Suspected Planetary Nebulae
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Trained Model (Balanced Data)**} & \multicolumn{3}{c}{**Trained Model (Unbalanced Data)**} \\ \cline{2-9}
**Suspected Planetary Nebulae Stars** & \multicolumn{3}{c}{**ANN**} & \multicolumn{3}{c}{**RF**} & \multicolumn{3}{c}{**ANN**} & \multicolumn{3}{c}{**RF**} \\ \cline{2-9}  & **SY** & **PN** & **RG** & **Class** & **SY** & **PN** & **RG** & **Class** \\ \hline IRAS 02379+5724 & 0.8607 & 0.0888 & 0.0505 & PN & 0.0204 & 0.1176 & 0.8621 & RG \\ \hline UCAC2 46104304 & 0.9584 & 0.0372 & 0.0044 & Sy & 0.5947 & 0.396 & 0.0093 & Sy \\ \hline IRAS 05495+2620 & 0.0 & 0.9937 & 0.0063 & PN & 1e-04 & 0.9979 & 0.0021 & PN \\ \hline IRAS 06549-2330 & 0.0033 & 0.9135 & 0.0833 & PN & 0.0073 & 0.961 & 0.0316 & PN \\ \hline PN G228.1+00.8 & 0.0 & 0.9667 & 0.0333 & PN & 0.0012 & 0.9821 & 0.0167 & PN \\ \hline \hline \end{tabular}
\end{table} 
Table 21: Results of the classification using ANN and Random Forest. Both algorithms were trained using both balanced and imbalanced datasets. The dataset of suspected planetary nebulae stars was obtained from the Symbol online database. The ANN model’s results are expressed as probabilities with four significant digits, whereas the Random Forest model only shows the resulting class. Figure 10: The figure displays the spectra obtained from the Gaia DR3 catalog that were sourced from the article “A machine learning approach for identification and classification of symbiotic stars using 2 MASS and WISE” [29]. Figure 11: The figure displays the spectra of suspected planetary nebulae stars obtained from the Gaia DR3 catalog that were sourced from Symbad database online. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Trained Model (Balanced Data)**} & \multicolumn{3}{c}{**Trained Model (Unbalanced Data)**} \\ \cline{2-9}
**Suspected Planetary Nebulae Stars** & \multicolumn{3}{c}{**ANN**} & \multicolumn{3}{c}{**RF**} & \multicolumn{3}{c}{**ANN**} & \multicolumn{3}{c}{**RF**} \\ \cline{2-9}  & **SY** & **PN** & **RG** & **Class** & **SY** & **PN** & **RG** & **Class** \\ \hline PN G229.3-02.7 & 0.0 & 0.8194 & 0.1806 & RG & 0.0004 & 0.1959 & 0.8038 & RG \\ \hline PN Y-C 40 & 0.0171 & 0.0416 & 0.9413 & RG & 0.0073 & 0.1335 & 0.8592 & RG \\ \hline EGB 2 & 0.0127 & 0.6487 & 0.3386 & PN & 0.0071 & 0.5841 & 0.4088 & RG \\ \hline IRAS 10348-6320 & 0.0 & 1.0 & 0.0 & PN & 0.0058 & 0.9942 & 0.0 & PN \\ \hline IRAS 11555-6031 & 0.0092 & 0.0612 & 0.9297 & RG & 0.0065 & 0.0445 & 0.949 & RG \\ \hline IRAS 11415-6540 & 0.0188 & 0.0728 & 0.9084 & RG & 0.0091 & 0.0515 & 0.9394 & RG \\ \hline PN G292.3+00.5 & 0.0 & 1.0 & 0.0 & PN & 0.0245 & 0.9755 & 0.0 & PN \\ \hline
2 MASS J05221214-6945980 & 0.002 & 0.998 & 0.0 & PN & 0.0031 & 0.9969 & 0.0 & PN \\ \hline
[RP2006] J052438-690413 & 0.0 & 0.7944 & 0.2056 & RG & 0.0007 & 0.1282 & 0.8711 & RG \\ \hline
[RP2006] 312 & 0.01 & 0.99 & 0.0 & PN & 0.0067 & 0.9933 & 0.0 & PN \\ \hline \end{tabular}
\end{table} 
Table 21: _Cont._
## 2 Background
This principle must ensure that each next-hop node has enough data to decode the encoded packet after receiving it. The decoding conditions is as follows: Suppose that a node needs to transmit \(n\) packets \(p_{1},p_{2},...,p_{n}\) to the \(n\) next-hop nodes \(r_{1},r_{2},...,r_{n}\). The unique condition by which the node can encode the \(n\) packets is that every next-hop node \(r_{i}\) has the other \(n-1\) packets \(p_{i}(i\
eq j)\). 3. It proposed the concept of coding gain. COPE compared the number of transmission between the coding method and the non-coding method, and took the result as a reference standard for improving network performance, such as network throughput and so on. Based on this, it put forward the concept of coding gain. Coding gain is defined as the ratio of the number of transmission without network coding to the minimum number of transmission with the network coding in COPE. According to the definition, the ratio is greater than or equal to 1. For any network, the performance of unicast is still an unresolved problem in the traditional network coding [14]. In actual transmission, due to the availability of coding opportunities, the overhead of packet header and other reasons, the coding gain is often relatively lower.
## 4 Overview of the Privacy and Security Requirements in a BYOD Environment  
Privacy requirements are the set of security and privacy requirements that BYOD security policies should achieve to provide sufficient security. Every decision made by BYOD users within an organisation is influenced by security and privacy, including permitting email on a personal device. However, this could expose the organisation to numerous risks and data loss. Employees are also concerned about how much personal data employers can access and use to control their devices, although enterprises are authorised to protect company data. Therefore, privacy requirements are insufficient for the organization's and BYOD users' security. The security access control in BYOD should consider the privacy and security requirements of both the organisation and BYOD users. Several security models have been proposed to overcome these challenges and concerns, including the CIA triad, which refers to confidentiality, integrity, and availability and is designed to guide information security policies that include confidentiality, integrity, and availability within an organisation. Also, the IAS-Octave has confidentiality, integrity, availability, accountability, auditability, authenticity, trustworthiness, non-repudiation, and privacy preservation.  
The conventional CIA triad framework is inadequate for addressing emerging threats in shared environments such as BYOD, as evidenced by [PERSON] and [PERSON]'s research [35]. The introduction of BYOD exposes organizations to various security risks, including email phishing attacks, embedded viruses in applications, and denial-of-service incidents. Consequently, the CIA triad framework fails to meet evolving security and privacy requirements, necessitating an upgrade to the more comprehensive IAS-Octave standard. [PERSON] et al. [22] propose a combined approach that integrates the privacy requirements of the CIA triad model and the IAS-Octave model, as depicted in Figure 3. This upgraded model incorporates the IAS-Octave security requirements and introduces additional attack detection requirements. [PERSON] et al. [22] further suggest enhancing security and privacy requirements in edge computing, comprising eight essential privacy requirements, including the CIA and IAS-Octave standards, attack detection, and reliability. Adopting this same model while excluding the reliability requirement is advisable for BYOD security, as previous studies have not emphasized its significance, prioritizing other security and privacy requirements [1]. In the forthcoming sections, we will investigate each component of this model to improve privacy requirements and investigate their relevance to BYOD security.
## 6 Conclusions  
This paper studies the direct under-sampling compression sensing method from the perspective of theory and engineering implementation. The DUS-CS method is proposed based on the theory of compressive sensing and the combination of the characteristics of echo signals with the implementation of direct under-sampling. Underwater echo simulation data was used to verify the correctness and effectiveness of the proposed method. Engineering implementation was achieved by improving the Nyquist sampling circuit with the adoption of pseudo-random sampling timing control. The rising edge (or falling edge) of each trigger signal was sampled and the signal was stored in the data buffer to obtain observation signals with compression ratios of 100%, 50%, 20%, and 10%. Then the DCT-OMP and the DUS-CS methods were used to process the data. The results show that both methods can reconstruct the original signal well when the compression ratio is 100% (in Nyquist sampling mode). Based on this, the correctness of the two methods was verified. When the compression ratio was 20% and below, the reconstruction effect of the DCS-OMP method was weak, but the DUS-CS method proposed in this paper was found to reconstruct the original signal well, even with the SNR being exceptionally low. These results confirm the correctness and engineering practicability of the DUS-CS method and data acquisition system. The method proposed in this paper provides an effective way for compressive sensing to be employed in practical engineering applications.  
Conceptualization, methodology, validation, writing--original draft preparation, T.S. and J.L.; Writing--review and editing, P.B.; Formal analysis, J.L.; Funding acquisition, T.S. This research was funded by the pre-research field fund project from Chinese Military Equipment Development Department, grant number 6140243010116 DZ04001; the Extension Fund from Underwater Test & Control Technology Key Laboratory, grant number YS24071802; and Chinese National Natural Science Foundation, grant numbers 11974084. This article used experimental data collected at Jiaxing Yisheng Electronic Technology Company (China). We gratefully acknowledge our colleagues for their experimental skills. [PERSON] worked on this article as a Visiting Research Fellow to the University of Bath (UK), thanks to the Chinese Scholarship Council (State Scholarship Fund no. 201608330338). The authors declare no conflict of interest.
# Peak Age of Information Optimization in Cell-Free Massive Random Access Networks
## 6 Conclusions  
This paper addresses the PAoI optimization problem in grant-free CF-RAN with mMTC services. We derive the PAoI expression by constructing a low-complexity and scalable topology model, and we propose channel access probability adjustment methods based on global/local PSO algorithms. Experimental results demonstrate that dynamically adjusting channel access probabilities significantly reduces network-wide PAoI. The global PSO algorithm achieves the optimal solution through global topology information exchange, but its signaling overhead grows substantially with network scale. Thus, it is more suitable for static or small-scale networks with fixed AP locations and controllable user density, where fine-grained channel access optimization enhances information freshness. In contrast, the local PSO algorithm, relying on information exchange among neighboring APs and performing independent optimization across different regions, confines signaling overhead within collaborative groups. While reducing signaling overhead, it effectively optimizes PAoI, making it applicable to large-scale access scenarios with wide coverage. Furthermore, this study is conducted under idealized conditions. Future work will explore more realistic system scenarios, including user mobility, non-ideal fronthaul links, and heterogeneous service demands. Additionally, promising directions such as online learning under dynamic topologies, joint optimization of PAoI and energy consumption, and experimental validation based on platforms like Open Radio Access Network are worth investigating in follow-up research.  
Conceptualization, Z.Z., Y.H. and W.Z.; methodology, W.Z.; software, Z.Z. and W.Z.; validation, Z.Z. and Y.H.; formal analysis, Z.Z.; investigation, Y.H.; resources, W.Z.; data curation, Z.Z.; writing--original draft preparation, Z.Z.; writing--review and editing, Y.H. and W.Z.; visualization, Y.H.; supervision, Y.H.; project administration, Z.Z., Y.H. and W.Z. All authors have read and agreed to the published version of the manuscript.  
The work of [PERSON] was supported in part by Guangdong S&T Program under Grant 2024B0101010001 and in part by the Guangdong Basic and Applied Basic Research Foundation (No. 2025A1515010235).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
# Joint Transmit Power Allocation and Splitting for SWIPT Aided OFDM-IDMA in Wireless Sensor Networks
## 2 System Model and Problem Formulation
### Iterative Multi-User Detection and SNR Evolution
Following the same idea, the BER performance of RN \(k\) can also be semi-analytically tracked as BER\({}_{k}=g(\Lambda_{k}^{Q})\), where \(g(\cdot)\) is also obtained through simulating the single RN system similar to that of \(f(\cdot)\). Details about the SNR evolution method c.f. [2,24].
# Collaborative Indoor Positioning by Localization Comparison at an Encounter Position
## 2 Related Work
### Inertial Measurement Unit-Based Localization  
Pedestrian Dead Reckoning (PDR) is a method of localization using IMU sensors such as accelerometers, gyroscopes, and magnetometers in the smartphone [10; 11]. Smart-PDR [11] obtains the user's stride length using the relationship between stride and the magnitude of acceleration along the z-axis. The heading direction is determined by integrating the angular velocity obtained from the gyroscope and the value of the magnetometer.  
Some methods process IMU sensor data with Deep Neural Networks (DNNs) such as CNNs to obtain the user's velocity vector [12; 13; 14; 15; 16; 17]. RoNIN [14] uses accelerometer and gyroscope values from a smartphone as input. This method uses Visual Simultaneous Localization and Mapping (SLAM) trajectories as the training data to learn the trajectory of the user. Thus, the method uses velocity vectors to predict daily activities, such as walking, putting the device in a pocket, etc.  
IMU-based methods suffer from error accumulation since localization is relative to the start position. Thus, the localization quality deteriorates significantly when the user walks a long distance. Therefore, the proposed method suppresses error accumulation by exploiting a new collaborative technique.  
Figure 1: The key idea of collaborative localization. The inaccurate position (blue dot) is corrected using the more accurate encounter position (red dot).
## 4 Presentation of Results and Discussion  
After removing the indicators with a weight below the cut-off boundary, the weights of the remaining indicators, categories, and sustainability areas were recalculated using the AHP method. Figure 2 presents the relative importance of the sustainability areas and shows that according to the professionals' opinion, the Environmental (A1), Sociocultural and Functional (A2), and Technical (A4) areas have similar relative importance. Subsequently, the Economy (A3) area's importance was 17%, and Site (A5) was appointed as the least relevant, corresponding to a weight of 8%. Analysing the final weights of the indicators, the \"technical systems\" and \"corruption avoidance plan\" were both ranked as the most important ones in the \"economic\" and \"technical\" sustainability areas, respectively, accounting for almost about 10% of the total weight in each category. The third most prioritised category in sustainable healthcare buildings was water, with potable water consumption and treatment of contaminated effluents as the most critical indicators. The \"Sociocultural and functional\" area was ranked the third most important in healthcare buildings. The most important categories of this area are \"mobility plan\" and \"controllability by the user\", both with a weight of 33% inside the area. Furthermore, the fourth-ranked category was \"environmental life cycle impacts assessment\", closely followed by the \"life cycle costs\" category. The \"Operational costs within the life cycle costs\" category scored highest. Finally, in the \"Sociocultural and functional\" area, the less important categories are \"landscaping\" and \"passive design\". The categories proposed for the \"Environmental\" area were defined to allow, in a holistic way, the assessment of the most common life-cycle environmental impacts according to national priorities. The \"Social and Functional\" area presented a list of indicators divided into six categories, including the critical aspects of building occupants' health and comfort, considering the importance of mobility and space design quality. The \"Economic\" area was defined to consider the most relevant building life-cycle costs and the impact on the local economy. On the other hand, the \"Technical\" area reflected issues about security, durability, management of systems and their efficiency and the occupants' education and awareness regarding sustainability concerns. In contrast, the \"Site\" areas' main issues regarded community and cultural values. The proposed list of sustainability areas, categories and indicators is presented in Table 4, together with the weights assigned by respondents to each sustainability category and area. Analysing Table 4, it is essential to highlight that the categories' weight was equal to that of the indicator in categories with only one indicator. Analysing each category made it possible to understand the most important Indicators according to the respondents' opinions. Figure 2: Final weight of the sustainability areas.
## 2 Dynamic Area Optimization Using Search Methods
### System Model
Consequently, for the \(u\)-th UE attached to the \(c\)-th cell, the values for DL SNR and DL SINR in dB can be expressed as follows:  
\[\gamma_{\rm SNR,\mu}=10\log_{10}\left(\frac{10^{\frac{R_{u,\mu}}{10}}}{\sigma ^{2}}\right) \tag{7}\]and  
\[\gamma_{\text{SINR},u}=10\log_{10}\Bigg{(}\frac{10^{\frac{R_{u,G_{u}}}{10}}}{ \sigma^{2}+10^{\frac{10}{10}}}\Bigg{)}. \tag{8}\]  
Here, \(\sigma^{2}\) denotes the noise power in a linear scale. The calculation of the noise power in dB, which is the conversion of \(\sigma^{2}\) to a dB scale, can be expressed as follows:  
\[\sigma^{2}_{\text{dBm}}=-174+10\log_{10}\left(B\right)+F, \tag{9}\]  
where \(B\) and \(F\) denote the bandwidth and the noise figure, respectively. Following the same procedure as DL, the uplink (UL) received signal level of the \(u\)-th UE in the \(c\)-th cell is as follows:  
\[R^{\prime}_{u,c}=P^{\prime}_{t}+G_{UE}-L_{u}+G_{u,c}, \tag{10}\]  
where \(P^{\prime}_{t}\) stands for the UE transmission power. The UE attaches to the cell that has the maximum DL received power, designated as \(c_{u}\). Here, we employ coordinated multi-point (CoMP) reception for the UL, a technique widely adopted in mobile wireless communications systems, such as 4G LTE. The UL CoMP enables us to achieve diversity gain by receiving signals from UE across multiple neighboring cells, thereby enhancing the UL SNR, especially at cell borders, which extends the coverage area. In this paper, we consider UL CoMP with two cells. If the second strongest signal power from the \(u\)-th UE is denoted as \(R^{\prime}_{u,c_{u}}\), where \(c^{\prime}_{u}\
eq c_{u}\), the UL SNR considering UL CoMP is expressed as follows:  
\[G=\max(G_{v}+G_{h},L_{F})+G_{p}, \tag{2}\]0
# Network Performance Optimization for Low-Voltage Power Line Communications
## 6 Conclusions  
For asymmetric and error-prone channels, we herein proposed an improved adaptive \(p\)-persistent CSMA based on dynamic game to optimize the saturation performance of an improved LVPLC artificial cobweb in a typical home local area network scenario. The following results are obtained:  
1. An improved \(Q\)-learning based hybrid CSMA/TDMA protocol was proposed to address the instability problem. The proposed method self-adaptively undertakes hop-by-hop learning to network under the variable channel conditions. Compared to the symmetrical channel, quantitative statistics on the average throughput, end to end delay, and hop count for stations under the asymmetrical constraint factor could be gathered when the system had completed the network functions.
2. The bandwidth utilizations and access delays were improved by controlling the r values. The maximum bandwidth utilization improved by 1.7%, while the maximum access delays  
Figure 17: Comparison of the two algorithms.  
improved by a factor of 2.57, relative to the original model. The average bandwidth utilization of the time slots at maximum values improved by 89.2%, relative to the results of the MAP algorithm.  
This proposed method is able to improve the network throughput, which will reduce the access delay and the collision probability of data packet in the home local area networks. In addition, the proposed method may also be applied in power distribution networks with automatic meter reading/advanced metering infrastructure, and vehicle-to-grid communications requirements. In future research, we plan to extend this work to a multi-network fusion and maintenance technology in the LVPLC.  
All the authors contributed to publish this paper. [PERSON], [PERSON], [PERSON] and [PERSON] mainly proposed the scheme of this paper. [PERSON] has done the writing, carried out the simulation and [PERSON] has checked the simulation results. [PERSON], [PERSON], [PERSON] and [PERSON] corrected and finalized the paper.  
This work is supported by National Natural Science Foundation of China (No. 51677034) and the Key Program of Natural Science Foundation of Heilongjiang Province in China (ZD2018012).  
The authors declare no conflicts of interest.
## 5 Results
### Multi-Objective Optimization
#### 5.1.3 Optimization Method and Results  
The multi-objective optimization can be conducted using population-based algorithms directly without formulating a combined cost function [23]. MOPSO and NSGA-II are popular algorithms frequently used in crashworthiness problems [41]. In this study, NSGA-II was adopted to solve the multi-objective optimization problem, and the specific parameters are listed in Table 5.  
The Kriging surrogate model is established for each optimization objective, and the NSGA-II algorithm is utilized to search for the Pareto front, as shown in Figure 14a. The two optimization objectives are competing with each other. When the \(-MCF\) increases, the \(STT\) decreases. This paper adopts the minimum distance method to determine the optimal solution (also known as the knee point) on the Pareto front. The minimum values of \(-MCF\) and \(STT\) determine the ideal point. Then, the closest point to the ideal point is identified as the knee point. To minimize the effect of different scales of \(MCF\) and \(STT\) on identifying the knee point, the Pareto front is normalized according to  
\[F_{i}=\frac{f_{i}-f_{\min}}{f_{\max}-f_{\min}} \tag{11}\]  
where \(F_{i}\) is the normalized value of the Pareto optimal solution \(i\), \(f_{i}\) is the value of the Pareto optimal solution \(i\), and \(f_{\min}\) and \(f_{\max}\) are the minimum and maximum value of the Pareto optimal solution.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Surrogate Models** & \(MRE\) & \(RMSE\) \\ \hline \(STT\) & 8.7\% & 31.89 K \\ \(MCF\) & 2.7\% & 4.78 kN \\ \hline \hline \end{tabular}
\end{table}
Table 4: Error analysis of surrogate models.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Initial population & 150 \\ Maximum generations & 100 \\ Crossover probability & 0.9 \\ Mutation probability & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parameters of NSGA-II.  
Figure 14b displays the normalized Pareto front. \(STT\) = 514 K and \(MCF\) = 131 kN are the values of the objective functions at the knee point. The corresponding design parameters are \(d=3.8\)mm, \(w=26.5\)mm, and \(\alpha=7.1^{\circ}\), respectively. Knee point balanced both \(STT\) and \(MCF\) better than point A (\(STT\) = 470 K, \(MCF\) = 63 kN) and point B (\(STT\) = 575 K, \(MCF\) = 146 kN).