esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Verification
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(\beta\) & 0.5 \\ \(\chi\) & 0.5 \\ \(\delta\) & 0.2 \\ \(\epsilon\) & 0.5 \\ \(\gamma\) & 0.3 \\ \(\eta\) & 0.3 \\ \(\kappa\) & 0.3 \\ \(\
u\) & 0.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Adjustment factors parameters. Figure 12: Delivery loss ratio for bearer services. To verify the load balance performance of the specific scheme, the number of average queue packets are simulated, as shown in Figure 13. The mean of average queue packets of ESCB is below 15. This result indicates that the routing scheme maintains stable load state. It achieves better load balance than VT-SP. Energy sensitive properties are reflected in the residual energy performance, as shown in Figure 14. Under high traffic density \(l=30\), the residual energy remains above 33%. The results indicate that the specific routing scheme largely improve the limited energy. In order to verify the energy balance performance, a residual energy jitter is simulated in Figure 15. The difference between the residual energy of different nodes is measured by this performance. The uniformity of energy can also be reflected by it. The average residual energy jitter of ESCB is about 15%. The results indicate that the routing scheme maintains good energy balance. Figure 14: Average residual energy. Figure 13: Average queue packets. In order to more clearly reflect the effectiveness of this paper, we compared the performance of various algorithms. We chose LBRA, HRA, JDDA and QRLSN as comparison objects [25; 26]. As shown in Figure 16, the average delivery loss rates of different algorithms are compared. During the simulation, a high-density flow was introduced. At the same time, packet loss caused by energy failure is considered. ESCB shows better delivery performance in simulations. Similarly, Figure 17 shows the latency performance comparison results. In the case of considering energy efficiency, the load balancing of the entire satellite network can be better balanced. Average latency performance can also be optimized. ESCB shows better latency performance in simulations. Figure 16: Delivery performance comparison. Figure 17: Latency performance comparison. Figure 15: Average residual energy jitter. Through the above simulations, the performance of the specific routing scheme is verified. The comparative analysis shows that the comprehensive performance of ESCB is much better than VT-SP. The comprehensive performance comparison with LBRA, HRA, JDDA and QRLSN also reflects the superiority of ESCB.
## 3 RSMA-Assisted Integrated Sensing and Communication Systems
### Key Challenges for RSMA-Assisted ISAC  
#### 3.3.1 Joint Waveform Design for RSMA-Assisted ISAC  
The main goal of joint waveform design is to achieve a better communication and sensing performance trade-off. One can utilize the existing communication waveforms, such as orthogonal frequency division multiplexing (OFDM) [29]. In [30], it is shown that OFDM-ISAC improves the resolution of the velocity estimation without increasing the signal bandwidth.  
Due to the significant performance degradation of conventional OFDM in high-speed environments, orthogonal time frequency space (OTFS) modulation has received considerable attention to provide more reliable communication under such conditions [31]. Unlike the OFDM-ISAC system, which operates in the time-frequency domain, the OTFS-ISAC system operates in the delay-Doppler domain, providing resilience to Doppler spread and delay [32]. In [33], a new OTFS system model is introduced, utilizing ISAC to analyze channel estimation error probability and outage probability. Additionally, ref. [34] extends the OTFS-ISAC system to vehicular applications, offering a communication system capable of predicting vehicle states.  
However, such communication waveforms can experience high peak-to-average power ratio (PAPR) and resolution degradation, which can degrade detection performance [35]. To address these issues, recent research has focused on integrated waveform design that considers both communication and sensing performance. In [36], a joint waveform design was proposed to minimize multi-user interference, while [37] analyzed the integrated performance trade-offs in Gaussian channels based on the CRB. Furthermore, ref. [38] performed joint optimization based on mutual information in MIMO systems.  
Recently, RSMA-ISAC waveform design has been proposed by jointly minimizing CRB and maximizing MFR [22]. In addition, in [39], the RIS has been considered in the RSMA-ISAC waveform design. In [40], a transmissive RIS framework was proposed to improve secrecy energy efficiency and CRB under imperfect CSI conditions.
# Improved Particle Swarm Optimization for Sea Surface Temperature Prediction
## 1 Introduction
Learning from multiple time-series sequences was instrumental to facilitating consistency enhancement and noise cancellation, thus achieving high prediction accuracy. 3. Developing an improved Particle Swarm Optimization (PSO) method, dubbed LSPSO, which uses a local search strategy to achieve the combined requirement of prediction accuracy and efficiency. We were striving for optimal model parameters, to pursue SST prediction efficiency, providing a new way for marine operational forecasting. Overall, our work provides a new SSTP method that not only improves prediction accuracy and speed, but also better models the trend of SST changes. An important element was to find a suitable method to optimize the parameters of the combined DTW + SVM prediction method (dubbed DS hereafter). We achieved this goal by developing an improved Particle Swarm Optimization (PSO) method, which was responsible for a 16.7% improvement in prediction accuracy, in terms of reducing the Root Mean Square Error (RMSE), and for a 76% reduction in prediction time. We also achieved a significant improvement in prediction accuracy of non-stationary SST time series, compared to DTW, SVM, DS (i.e., DTW + SVM), and a recent deep learning method dubbed LSTM (Long-Short Term Memory). The rest of this paper is organized as follows. In Section 2, we describe related work on SSTP, similarity measures, and optimization methods. The main idea of DSL is introduced in Section 3. Experimental results and performance evaluation are shown in Section 4. Section 5 presents our conclusions.
# Coalition Formation Game for Cost-Efficient Multiparty Payment Channel in Payment Channel Networks
## 5 Coalition Formation Game for MPC Creation
### Coalition Cost Function
For example, if a flow \(x_{ik}^{u}\) depletes the balance of the PSP \(i\) at \(T_{0}\) and a new payment, \(A_{i}(t_{m})\), arrives at the next SPC \(T_{1}\), the payment needs to wait for \(\Sigma_{D}\) until the deposit of the PSP \(i\) can be \(C_{ij}+A_{i}(T_{1})\) at \(T_{N}\), where \(C_{ij}\) is the initial deposit of the PSP \(i\). We need an effective SPC scheduling algorithm for balance stability to reduce deposit variance. **Property 3**: _The MPC deposit of a PSP \(i\) is \(C_{ij}(1+\Sigma_{D})\), where the \(C_{ij}\) is the maximum initial deposit among coalitional PCs ij of the PSP \(i\)._  
If a legacy PC \(ij\) needs a maximum \(C_{ij}\) deposit for the highest PBT flow rate, the SPC \(ij\) requires an additional deposit \(C_{ij}\Sigma_{D}\) for queued transactions during the turnaround time. Accordingly, other SPCs are affordable, with less deposit for new and queued transactions than the maximum SPC. The collateral cost for additional transaction delays due to the SPC turnaround time as \(\mathbb{E}(D_{ij}^{MPC}-D_{ij}^{PC})\simeq\Sigma_{D}-\mathbb{E}\Delta\) is as below. \[v_{c}^{2}(S_{k})= \begin{cases}\sum_{ij\in S_{k}}C_{ij}(\Sigma_{D}-\mathbb{E} \Delta),&\text{if }\Sigma_{D}\leq T_{exp},\\ -\infty,&\text{otherwise},\end{cases}  \tag{10}\]  
where \(S_{k}\) is defined in Equation (7) and all PSPs have equivalent PBT flows, \(\lambda_{ij}=\mu_{ij}\), within the MPC coalition, \(S_{k}\). Consequently, we define the MPC CFG cost function, \(v_{c}(S_{k})=\epsilon_{c}(v_{c}^{1}(S_{k})+v_{c}^{2}(S_{k}))\), with an interest rate, \(\epsilon_{c}\), that is comparable with the transaction fee.
# Eco-Efficient Cement-Based Materials Using Biomass Bottom Ash: A Review
## 4 Biomass Bottom Ash in Concrete
### Fresh, Physical, Mechanical, and Durability Properties of Concrete Made from BBA
#### 4.2.3 Compressive, Flexural and Splitting strength
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Mixes**} & \multirow{2}{*}{**Workability (mm)**} & \multirow{2}{*}{**Compressive Strength (MPa)**} & \multicolumn{2}{c}{**Flexural**} & \multirow{2}{*}{**Absorption (\%)**} & \multirow{2}{*}{**Density**} \\  & & & & & **(MPa)** & & **(\%)** \\ \cline{3-6}  & & **7 days** & **28 days** & **90 days** & **28 days** & \\ \hline
**[PERSON] et al. [19]** & & & & & & \\ \hline Control-1 & 90 & 29.8 & 44.3 & 48.5 & 5.9 & 5.7 & 2.36 \\ BBA-2-NG & 80 & 29.6 & 37.7 & 42.2 & 4.9 & 5.9 & 2.34 \\ BBA-3-NG & 80 & 27.5 & 35.4 & 39.8 & 4.7 & 6.3 & 2.33 \\ BBA-4-RCA & 90 & 26.5 & 35.9 & 40.8 & 4.9 & 6.5 & 2.18 \\ BBA-5-RCA & 80 & 24.5 & 33.3 & 39.7 & 4.6 & 6.9 & 2.18 \\ BBA-6-RCA & 75 & 22.8 & 30.1 & 34.2 & 4.5 & 7.6 & 2.17 \\ BBA-7-MRA & 80 & 22.9 & 32 & 37.3 & 4.4 & 6.7 & 2.10 \\ BBA-8-MRA & 85 & 22.2 & 30.4 & 34.9 & 4.3 & 7.2 & 2.14 \\ BBA-9-MRA & 80 & 21.7 & 27.7 & 30.5 & 4.1 & 7.7 & 2.14 \\  & \multicolumn{3}{c}{} & \multicolumn{3}{c}{} & \multicolumn{3}{c}{} & \multicolumn{3}{c}{} \\ \cline{3-6}  & \multicolumn{3}{c}{} & \multicolumn{3}{c}{} & \multicolumn{3}{c}{} & \multicolumn{3}{c}{} \\ \cline{3-6}  & \multicolumn{3}{c}{} & \multicolumn{3}{c}{} & \multicolumn{3}{c}{} & \multicolumn{3}{c}{} \\ \hline Control-10-RCC & 37 & 38 & 42 & 49 & 8.3 & - & 2.39 \\ BBA-11-RCC & 57 & 31.5 & 35 & 40 & 5.6 & - & 2.36 \\ BBA-12-RCC & 70 & 25 & 29 & 30.5 & 4.8 & - & 2.33 \\ Control-13-PCC & 25 & 39 & 47.5 & 52 & 7.5 & 3.8 & 2.46 \\ BBA-14-PCC & 37 & 32 & 38 & 39.5 & 7.2 & 4.9 & 2.41 \\ BBA-15-PCC & 60 & 33.5 & 41 & 43.5 & 6.9 & 6.1 & 2.38 \\
**[PERSON] et al. [27]** & (mm) & & & (365 & & & \\  & & & & days) & & & \\ \hline Control-16 & 180 \(\pm\) 20 & 22.3 & 33.7 & 40 & \begin{tabular}{c} 2.53 \\ (Splitting) \\ \end{tabular} & 5.1 & 2.40 \\ BBA-17 & 180 \(\pm\) 20 & 24.3 & 27.8 & 33.8 & \begin{tabular}{c} 2.14 \\ (Splitting) \\ \end{tabular} & 4.8 & 2.40 \\ BBA-18 & 180 \(\pm\) 20 & 23.2 & 25.6 & - & \begin{tabular}{c} 2.02 \\ (Splitting) \\ \end{tabular} & 4.4 & 2.40 \\ BBA-19 & 180 \(\pm\) 20 & 22.6 & 25.3 & - & \
\begin{tabular}{c} 2.37 \\ (Splitting) \\ \end{tabular} & 4.4 & 2.40 \\ BBA-20 & 180 \(\pm\) 20 & 22.7 & 27.3 & - &
\begin{tabular}{c} 1.89 \\ (Splitting) \\ \end{tabular} & 4.5 & 2.40 \\
**[PERSON] et al. [23]** & (mm) & & & & & \\ \hline Control-21 & 80 & 25.9 & 31.6 & 37.6 & 4.4 & 8.8 & 1.80 \\ BBA-22 & 85 & 20.4 & 28 & 33.9 & 4.2 & 9.7 & 1.69 \\ BBA-23-MRA & 85 & 19.6 & 26.7 & 32.5 & 4.1 & 11.1 & 1.64 \\ BBA-24-MRA & 80 & 18.7 & 21.3 & 26.1 & 3.8 & 12.7 & 1.57 \\ BBA-25-MRA & 80 & 19.8 & 27.1 & 33.2 & 4.2 & 10.9 & 1.79 \\ BBA-26-MRA & 90 & 16.3 & 20.4 & 22.3 & 3.8 & 12.3 & 1.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Mechanical properties of concrete mixes by authors.
## 4 Discussion  
### Advantages of Developed CET  
#### 4.1.1 Detailed coastline Mapping and Representation  
Coastlines extracted by CET-WV are much less generalized (Figure 8, 12, 13) than coastlines extracted with another tested tool. This is best demonstrated on smaller islands and rock examples (e.g., Sika) (Figure 8). As explained by the coastline paradox, the accuracy of the coastline length calculation is directly related to the level of coastline generalization. Accordingly, CET should be applied in all cases where accurate coastline mapping is required.
# Multi-View Synthesis of Sparse Projection of Absorption Spectra Based on Joint GRU and U-Net
## 4 Results and Discussion
### Noise Resistance Analysis of the Algorithm  
To evaluate the noise immunity of the GMResUNet, Gaussian noise ranging from 0% to 12% was added to the original sparse projection data. As shown in Figure 17a, the average relative error after the synthesis of various types of sparse projections increases with the increase of noise intensity. Overall, the projected views with N = 16 are highly resistant to interference in the synthesis, and the average relative error of the synthesized multi projection data is less than 1%. When the noise level reaches 12%, the average relative error of projection number synthesis for N = 4 and above is less than 1.5%; while when N = 2, the average error of multi projection data synthesis is relatively large and has the worst anti-interference ability. In addition, the effect of noise on the quality of temperature field reconstruction was also investigated. As shown in Figure 17b, a similar trend was found in the reconstructed temperature field. For N = 4 and above, the reconstruction error remained below 8%. As the noise level increased, the temperature field reconstruction using the full projection data synthesized from N = 2 encountered significant errors and larger fluctuations.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Projection** & **Methods** & **PSNR** & **SSIM** & **Error** \\
**Angle N** & CNN & 35.456 & 0.957 & 3.00\% \\ N = 8 & U-Net & 36.955 & 0.984 & 1.32\% \\ \hline N =16 & CNN & 37.166 & 0.961 & 2.37\% \\ U-Net & U-Net & 39.827 & 0.987 & 1.05\% \\ \hline \end{tabular}
\end{table}
Table 4: _Cont._  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Projection** & **Composite State** & **PSNR** & **SSIM** & **Error** \\
**Angle N** & **Composite State** & **PSNR** & **SSIM** & **Error** \\ \hline N = 2 & \begin{tabular}{c} Incomplete view \\ Synthetic \\ multi-view \\ \end{tabular} & - & - & - \\ N = 4 & \begin{tabular}{c} Incomplete view \\ Synthetic \\ Multi-view \\ \end{tabular} & 33.522 & 0.953 & 3.43\% \\ N = 5 & \begin{tabular}{c} Incomplete view \\ Synthetic \\ Multi-view \\ \end{tabular} & 34.969 & 0.964 & 2.07\% \\ N = 8 & \begin{tabular}{c} Incomplete view \\ Synthetic \\ Multi-view \\ \end{tabular} & 36.423 & 0.969 & 1.68\% \\ N = 95 & \begin{tabular}{c} Incomplete view \\ Synthetic \\ Multi-view \\ \end{tabular} & 36.955 & 0.984 & 1.32\% \\ N = 16 & \
\begin{tabular}{c} Incomplete view \\ Synthetic \\ Multi-view \\ \end{tabular} & 38.821 & 0.987 & 1.23\% \\ N = 16 &
\begin{tabular}{c} Incomplete view \\ Synthetic \\ Multi-view \\ \end{tabular} & 39.827 & 0.987 & 1.05\% \\ \hline \end{tabular}
\end{table}
Table 5: Quality analysis of temperature field reconstruction before and after multi-view absorption synthesis using U-Net reconstruction.
# Compression Law and Settlement Calculation Method of Over-Wet Soil Based on Large Samples
###### Abstract
However, due to the high water content and low permeability of over-wet soil, employing standard compression test methods underestimates creep deformation owing to size effects [12; 13], resulting in higher post-construction settlement than expected. In this study, one-way compression tests were performed on large samples of over-wet soils with different compaction degrees to investigate the compression laws and analyze the effects of water content and compaction degree. Second, based on the compression data, the compression process of over-wet soil was divided into three stages, and the deformation law was analyzed. Thermogravimetric analysis was employed to analyze the changes in free water, weakly bound water, and strongly bound water during compression [14]. Finally, based on the layer-wise summation method, a calculation method for the settlement of an over-wet soil foundation considering the creep process was proposed and verified. The results of this study will provide a theoretical basis for the disposal of over-wet soil, which will help to accurately select foundation disposal measures and reduce potential resource waste.
# A Next-Generation Core Network Architecture for Mobile Networks
###### Abstract  
Over the years, the cellular mobile network has evolved from a wireless plain telephone system to a very complex system providing telephone service, Internet connectivity and many interworking capabilities with other networks. Its air interface performance has increased drastically over time, leading to high throughput and low latency. Changes to the core network, however, have been slow and incremental, with increased complexity worsened by the necessity of backwards-compatibility with older-generation systems such as the Global System for Mobile communication (GSM). In this paper, a new virtualized Peer-to-Peer (P2P) core network architecture is presented. The key idea of our approach is that each user is assigned a private virtualized copy of the whole core network. This enables a higher degree of security and novel services that are not possible in today's architecture. We describe the new architecture, focusing on its main elements, IP addressing, message flows, mobility management, and scalability. Furthermore, we will show some significant advantages this new architecture introduces. Finally, we investigate the performance of our architecture by analyzing voice-call traffic available in a database of a large U.S. cellular network provider.  
next-generation networks; cellular core network; cellular architecture; virtualization; peer-to-peer; security; cloud +
Footnote †: journal:  
0000-0001-4500-3899-223][PERSON]. [PERSON]
## 5 Experiments and Results
### Quality of the Rebalancing Operations
#### 5.5.5 Effect of Neighbourhood Radius \(d\) and Critical Threshold \(cr\)  
The neighbourhood radius \(d\) and the critical threshold \(cr\) define the problem itself and its complexity. Changing either one or both of them results in a different number of critical stations. Consequently, to properly compare approaches with differing \(d\) and \(cr\) values we compared the percentage of fixed stations instead of the absolute number of fixed stations. While an increase in \(d\) can easily justify the lower performances achieved due to the fact that a greater radius may induce new critical situations, no qualitative explanation can be provided for \(cr\). Considering the results shown in Figures 13 and 14, we chose \(d=500\) m and \(cr=0.2\).
# Fault Feature Extraction Using L-Kurtosis and Minimum Entropy-Based Signal Demodulation
###### Abstract  
The health of mechanical components can be assessed by analyzing the vibration and acoustic signals they produce. These signals contain valuable information about the component's condition, often encoded within specific frequency bands. However, extracting this information is challenging due to noise contamination from various sources. Narrow-band amplitude demodulation presents a robust technique for isolating fault-related information within the signal. This work proposes a novel approach based on cluster-based segmentation for demodulating the signal and extracting the frequency band of interest. The segmentation process leverages the criteria of maximum L-kurtosis and minimum entropy. L-kurtosis maximizes impulsiveness in the signal, while minimum entropy signifies a low degree of randomness and high cyclo-stationarity, and both characteristics are crucial for identifying the desired frequency band. Simulations and experimental tests using vibration signals from different gears demonstrate the effectiveness of this technique. The processed envelope of the signal exhibits distinct improvements, highlighting the ability to accurately extract the fault-related information embedded within the complex noise-ridden signals. This approach offers a promising solution for accurate and efficient fault diagnosis in mechanical systems, contributing to enhanced reliability and reduced downtime.  
f _applied_  
_sciences_  
20 August 2024  
Revised: 9 September 2024  
Accepted: 15 September 2024  
Published: 16 September 2024
# Validation of Automated Chromosome Recovery in the Reconstruction of Ancestral Gene Order
## 3 Clustering  
The hypothesis underlying chromosome recovery is that regions on DNA located near one another on the same chromosome are likely to be inherited (or \"linked\") together; thus, contigs originating from the same ancestral chromosome will have a good chance of appearing on the same chromosomes in the extant genomes descending from that ancestor. Even if the syntenic relationship of two contigs can be disrupted in one lineage by rearrangements such as translocation or chromosome fission, deletion or fractionation, the co-occurrence of the two may be conserved in other lineages. The frequency of co-occurrence of two contigs on the same chromosomes of the extant genomes is thus a good indication of whether these contigs appeared on the same chromosome in the ancestral genome.  
We can therefore expect pairs of contigs remote from each other on an ancestral chromosome to have lower frequencies of co-occurrence than contigs closer together. There may be various other reductions in co-occurrence in some groups of species such as those due to different chromosomal arm locations, as in some insects, or other operon-like organizations. However, two contigs on different ancestral chromosomes should definitely have the least frequent co-occurrence in the extant genomes. Thus, to partition the contigs into distinct chromosomes while still allowing for some chromosome internal structure of co-occurrence data, it seems emmently reasonable to have recourse to hierarchical clustering procedures, such as average-linkage or complete-linkage [12].  
This approach works well with some datasets, for example, the monocot reconstruction in [5], or the eudicot reconstruction in [13]. There are, however, some weaknesses in the procedure due to several factors.  
1. Loss of evolutionary signal due to a lengthy time period between the ancestor and its descendants. This leads to a sparsity of co-occurrence values of non-negligible size, meaning that some contigs do not fit into any cluster at a meaningful level.
2. Scale bias. Large contigs will have more co-occurrences than smaller contigs that will be included late, often erroneously (especially with complete-linkage), in the clustering procedure.
3. Variable scores. Due to vagaries in deletion and other evolutionary processes, not all high scores reflect true ancestral co-occurrence. Coversely, some co-occurrences cannot be captured due to low scores.
4. Inflexible visualization settings. The heat maps color pixels by dividing the range of scores into equal intervals by default. However, this is not useful in comparing heat maps produced by different settings in the construction of contigs or in the use of different similarity or distance measures of contig co-occurrence. One heat map may be simply darker or lighter than the other overall, thus obscuring the real object of comparison, which is how clear-cut and distinct the clusters are and how they are qualitatively different from map areas not corresponding to clusters.