esa-sceva/satcom-chunk-collection huggingface dataset:
# Seeking Optimal GNSS Radio Occultation Constellations Using Evolutionary Algorithms
## 1 Introduction
The fitness function of the GA applied in Reference [24] is established on the basis of the minimum number of 24 h ROEs over each 4.5\({}^{\circ}\)\(\times\) 4.5\({}^{\circ}\) longitude-latitude grid cell and the temporal distribution of the ROEs is not considered. [PERSON] and [PERSON] [25] also applied GA to optimize the 2D-lattice flower constellations composed of 12 satellites for a LEO-LEO RO mission, with the purpose of maximizing the odds at which pairs of satellites can see each other through the atmosphere (active or observation time). [PERSON] and [PERSON] [26] further used GA to optimize the 2D-lattice flower constellation (2D-LFC) and 3D-lattice flower constellation (3D-LFC) composed of 6 LEO satellites for GPS RO observations over Asia and the Pacific region, and the performances of the optimized constellations for RO observation are even superior to COSMIC over the target region. They devised the fitness function based on the concept of a Voronoi diagram, which evaluates the spatio-temporal distribution of ROEs. In these previous studies which use the evolutionary algorithms to optimize the RO constellation [24; 25; 26], a GA is generally applied. While in the design of optimal satellite constellations for different kinds of applications, another evolutionary algorithm, the particle swarm optimization (PSO) algorithm, is also widely used and it has been documented that a PSO performs even better than a GA [27; 28]. In this study, the navigation signals from all the five available Global Navigation Satellite Systems (GNSS) (i.e., GPS, GLONASS, BDS, GALILEO, and QZSS) are fully exploited for the first time and two constellation patterns, the 2D-LFC and 3D-LFC, are adopted to develop the optimal constellations composed of 6-12 LEO satellites for GNSS RO observations over the globe. Considering that the optimization algorithms may result in different performance levels of the eventual constellation [27], both GA and PSO are applied to search for the optimal configuration of the constellation for GNSS RO observations. The fitness function of the evolutionary algorithms is established based on the concept of spatio-temporal point process. Both the spatial and the temporal distributions of ROEs are taken into consideration evaluating the optimality of the resultant constellation. In what follows, we outline how this is done. Section 2 describes the principles and the methods used in the optimization process and the simulations. In Section 3, the optimal constellation configurations are presented and are evaluated. The comparisons of the methods and results with related researches are discussed in Section 4, and some conclusions are reached in Section 5.
## 2 Electro-Membrane Technologies: Current Status and Challenges
### Alternative A2--Salinity Gradient Energy Harvesting  
Alternative A2 (Figure 4) considers the harvesting of salinity gradient energy contained in SWRO prines. In this alternative, no chemical self-supply could be achieved.  
[PERSON] et al. [19] report a summary of recent studies focused on energy recovery from desalination brines by means of RED, being able to reduce the external energy consumption of desalination processes. In this work, maximum power densities in a wide range in the order of \(\sim\)1 W\(\cdot\)m\({}^{-2}\) are reported for the different experimental conditions, mainly feed concentrations and number of pair cells. In this sense, the integration of RED into desalination facilities is presented as a promising alternative towards the achievement of self-sufficient or low-energy consuming systems, although capital costs are still limiting its installation. Moreover, life cycle assessments of RED units conclude that RED is environmentally competitive with other RE such as solar PV energy or wind energy [67].  
Thus, in alternative A2, in order to maximize energy recovery, as much brine as possible should be conveyed to the RED plant, and in the event that a fraction of the brine cannot be used, it will be directly disposed of. Although the self-supply of chemicals is not considered, as alternative A1 did, with RED, a larger volume of brines could be achieved, which decreases the environmental burdens associated with the direct disposal of brines. In addition, energy recovery from salinity gradients is considered a renewable and sustainable power supply [68; 69; 70], environmentally competitive with other well established RE such as solar PV or wind [67], that could reduce up to up to 8 Gt of CO\({}_{2}\)-eq\(\cdot\)year\({}^{-1}\) (24% of the total emissions of Greenhouse Gases related to the energy sector).  
[PERSON] et al. [71] estimated the maximum SGE available in terms of net power density and the net specific energy delivered by a RED system in six SWRO desalination plants distributed worldwide, obtaining net specific energy maximum values in the range of 0.08-0.15 kWh\(\cdot\)m\({}^{-3}\) of desalted water and net power densities up to 3.7 W\(\cdot\)m\({}^{-2}\). Moreover, recent studies [25] concluded that if all the salinity gradient energy is harnessed,-40% of the energy demand of an SWRO facility could be supplied; however, a reduction to the  
Figure 4: Alternative A2 flow chart.  
\(-\)10% of the supply is estimated due to the energy conversion irreversibility and untapped salinity gradient energy.
# Activation-Based Pruning of Neural Networks
## 2 Related Work
### Structured/Unstructured Pruning  
Pruning can be classified into structured [3; 4; 5; 12; 13; 14; 15; 16], unstructured [2; 17], and semi-structured [18] pruning. Structured pruning removes filters, channels, or layers to induce structured sparsity patterns. It is commonly used in convolutional neural networks (CNNs), where entire filters or channels (groups of neurons) can be pruned. In unstructured pruning, individual weights without structural constraints are considered for removal. It can be applied to any layer of a neural network, including fully connected layers and convolutional layers. Semi-structured pruning is a hybrid approach that combines aspects of both structured and unstructured pruning. It involves removing entire structures, such as filters or channels, but within those structures, individual weights may be pruned. Activation-based pruning falls under unstructured pruning, as we assign a score to individual neurons for pruning.
# Production of Microfibrilated Cellulose from Fast-Growing
## 3 Results and Discussion
### MFC Characterization
#### 3.2.4 Crystallinity  
The powder X-ray diffraction (XRD) Pattern of the three MFCs show the typical reflections of crystalline cellulose I\(\beta\) at 20 = 22.6\({}^{\circ}\) and 15.8\({}^{\circ}\), which correspond to the (200), (110) and (1\(\overline{1}\)0) planes, respectively (Figure 2) [47]. The latter two reflections are superimposed. The crystallinity indices, CI, calculated from the diffractograms, were 84% (P-MFC), 79% (E-MFC) and 76% (O-MFC). These values suggest that P-MFC is slightly more crystalline than the other two MFC samples. Thus, although the process used to obtain MFC was the same, crystallinity also depends on the initial raw material [43].  
On the other hand, it is well known that nanofibrillation processes can reduce the crystallinity of the resultant cellulose nanofibers [48]. Some authors have explained that this may be due to the breaking of the highly ordered crystalline structure or due to the cellulose nanofibers' horrification during the nanofibrillation process [49]. Herein, the chemo-mechanical process involving TEMPO-mediated oxidation prior to nanofibrillation is especially prone to Partially damaging the crystalline cellulose structure [20]. For instance, the CI of TEMPO-CNF from eucalypt was reported as 57% [20], from reed as 65% [50] and from rice straw as 64% [48]. Hence, it appears that the purely mechanical process employed in this study, i.e., PFI, renders MFC with higher crystallinity and less structural damage than the reported TEMPO routes. The same trend was noted by [PERSON] et al. [43], who found less reduction in crystallinity for CNFs obtained from elm using only mechanical processes as compared to the TEMPO oxidation pretreatment prior to microfluidization. These authors mentioned that this fact may be linked, among other factors, to the oxidation of the internal cellulose crystallites.
# Extended Smoothing Methods for Sparse Test Data Based on Zero-Padding
## 2 The Smoothing Theory of Two-Dimensional Discrete Data  
The core idea of two-dimensional discrete data smoothing theory is to extend the discrete data series by zero-padding in the wavenumber domain. The transformation between the spatial domain and the wavenumber domain is mainly realized by the two-dimensional Fourier transform method, and its process block diagram is shown in Figure 1.  
The smoothing process for two-dimensional discrete data is shown in Figure 1, and the process consists of three main steps.  
In the first step, the discrete signal is transformed from the spatial domain to the wavenumber domain by two-dimensional DFT. It is complicated to process discrete data in the spatial domain, but the data can be transformed into the wavenumber domain by DFT to process its angular spectrum.  
In the second step, the above angular spectrum sequence is supplemented with zeros to artificially expand its sequence, and the angular spectrum value at a specific position is  
Figure 1: Block diagram of smoothing process for two-dimensional discrete data.  
changed accordingly. This procedure complements the original sequence with zeroes but does not add any additional information to the angular spectrum in the wavenumber domain.  
In the third step, the newly generated angular spectrum in the wavenumber domain is transformed into the spatial domain by two-dimensional IDFT, and a new two-dimensional discrete signal is obtained. Smoothing produces the discrete signal that was regenerated.
## 6 Conclusions  
In this paper, we have proposed an interference-aware iterative clipping and filtering PAPR reduction scheme for the LS-MU-MIMO-OFDM system. To deal with an LS-MIMO-OFDM system that has a large number of TX antennas, low complexity is one of the most important factors. Iterative clipping and filtering is a very powerful PAPR reduction technique for LS-MIMO-OFDM due to its simplicity and effectiveness. However, the clipping noise can be a serious problem because it can cause a performance degradation. We showed that the clipping noise can be buried in the IUI, if we carefully choose the CR depending on the IUI and/or channel condition. Then, we can improve the EE of the system with little performance penalty. Choosing the appropriate CR is very important and the appropriate CR can change depending on the situation. The adaptive choice of CR can be a great help. Comparing with other techniques which require high complexity and price, the proposed scheme can simply increase the EE. To validate the proposed scheme, we compared the SIR and SCR and determined the approximate range of CR, \(\
u(dB)\). Then, we analysed the normalized distortion power for the iterative clipping and filtering technique to derive the theoretical effective SINR. We presented various performances of the proposed scheme, SE, BER, and PAPR, and showed that the proposed scheme has very effective PAPR performance with little SE/BER performance loss. The proposed scheme can be a useful design method to increase the EE of the LS-MIMO-OFDM systems.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Education) (NRF-2017R1D1A1B03028350), and faculty research fund of Sejong University in 2017. This work was conducted by the research Grant of Kwangwoon University in 2017.  
[PERSON] designed the algorithm, performed the simulations, and prepared the manuscript. [PERSON] was responsible for coordinating, writing, and revising the paper. Both authors discussed the results and approved the publication.  
The authors declare no conflict of interest.
# Performance Analysis of Troposphere Scattering Communication Channel with Chirp-BOK Modulation
## 5 Conclusions  
In this paper, we presented a BER performance analysis and parameter optimization of chirp-BOK modulation over a troposcatter channel, theoretically and numerically. Firstly, the averaged BER performance of chirp-BOK modulation over a Rayleigh flat fading channel was derived theoretically. Then, a seven-path equal-delay Rayleigh fading channel model was established to simulate the chirp-BOK modulation BER performance under various conditions. Furthermore, the impact of the time-bandwidth product and channel tap delay was optimized and analyzed with different chirp-BOK parameters. These results demonstrate that when the path gain remains constant and the tap delay increases, the value of \(D\) needs to be increased in order to achieve optimal performance. It is worth noting that an increase in the primary path gain will not necessarily result in an improvement in BER performance, when the same time-bandwidth product and tap delay conditions are considered. As observed in Part 4, when optimized with appropriate parameters, chirp-BOK modulation can exhibit a significantly lower BER compared to other modulation techniques under deep fading channel conditions. On the one hand, in a future study, an actual chirp signal experiment should be tested to characterize chirp-BOK modulation parameters. On the other hand, further enhancement may focus on the combination of chirp modulation with some advanced error correction codes to achieve higher data transmission reliability.  
Conceptualization, J.S. and Z.L.; software, J.S.; validation, Z.L., Y.L. and T.X.; writing--original draft preparation, J.S., Z.L. and T.X.; writing--review and editing, J.S., Z.L. and Y.L.; visualization, J.S.; supervision, J.S. and T.X. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (62371390).  
Not applicable.  
Data Availability Statement: Data are contained within this article.  
Conflicts of Interest: Author [PERSON] is employed by the company China Academy of Space Technology. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.  
Figure 10: The BER curve of different modulation schemes in \(T=30\) ns.
# Dynamic Performance Analysis for an Absorption Chiller under Different Working Conditions
## 3 Modelling
### Mass and Species Conservation
#### 3.2.2 Condenser  
For the condenser, the principle is similar, but these equations are simpler, since there is only one species.  
(1) Vapor storage equation:  
\[m_{v,out,g}-m_{l,c}=\frac{dM_{v,c}}{dt} \tag{8}\]  
where \(m_{l,c}\) is the mass flow rate of the refrigerant condensed from the vapor in the condenser, kg/s; and \(M_{v,c}\) is the refrigerant vapor storage in the condenser, kg.  
(2) Liquid storage equation:  
\[m_{l,c}-m_{l,out,c}=\frac{dM_{l,c}}{dt} \tag{9}\]  
where \(m_{l,out,c}\) is the mass flow rate of the refrigerant liquid leaving the condenser (point 2), kg/s; and \(M_{l,c}\) is the refrigerant liquid storage in the condenser, kg.  
(3) The condenser is also filled with liquid and vapor:  
\[\frac{M_{v,c}}{\rho_{1}}+\frac{M_{l,c}}{\rho_{2}}=V_{c} \tag{10}\]  
where \(\rho_{1}\) and \(\rho_{2}\) separately are the densities of the vapor and the liquid stored in the condenser, kg/m\({}^{3}\), and \(V_{c}\) is the condenser volume, m\({}^{3}\).  
(4) The mass flow rate of the refrigerant liquid leaving the condenser is:  
\[m_{l,out,c}=Cd\cdot S_{c}\cdot\sqrt{\frac{2\cdot\rho_{2}\cdot\left[p_{g}-p_{ a}+\rho_{2}\cdot g\cdot(H_{c}+z_{c})\right]}{\zeta_{c}}} \tag{11}\]  
where \(S_{c}\) is the valve section area between the condenser and the evaporator, m\({}^{2}\); \(H_{c}\) is the vertical distance between the bottom of the condenser and the liquid inlet of the evaporator, m; and \(\zeta_{c}\) is the resistance coefficient used to reflect the pressure losses in the pipes between the condenser and the evaporator.  
\[\zeta_{c}=1400\cdot\left(\frac{0.0002}{z_{c}}\right)^{2} \tag{12}\]  
where \(z_{c}\) is the liquid height inside the condenser, and calculated by:  
\[z_{c}=\frac{M_{l,c}}{\rho_{2}\cdot A_{c}} \tag{13}\]  
where \(A_{c}\) is the bottom area of the condenser, m\({}^{2}\).
# Bearing Capacity of Ring Foundations on Sand Overlying Clay
## 6 Conclusions  
The ultimate bearing capacity of rough ring footings on sand overlying clay was evaluated. Using FELA, the normalized bearing capacity, \(p/(\gamma R_{0})\), was calculated for a wide range of parameters. The effect of the undrained shear strength, \(c_{u}/(\gamma R_{0})\), of the clay, the thickness \(H/R_{0}\), the friction angle, \(\varphi\), of the sand layer, and the ratio of the inner to the outer radii, \(R_{i}/R_{0}\), of the foundation on \(p/(\gamma R_{0})\) was also investigated. The results obtained from the present study are found to match reasonably well with existing solutions. To facilitate use among engineers, design charts are provided. Based on the limit analysis results, the following conclusions can be made.  
1. The bearing capacity reaches a constant value when \(H/R_{0}\) exceeds the critical thickness \(H_{c}/R_{0}\), which depends on the magnitudes of \(\varphi\), \(R_{i}/R_{0}\), and \(c_{u}/(\gamma R_{0})\). In general, the value of \(H_{c}/R_{0}\) decreases with decreases in \(\varphi\) and increases in \(R_{i}/R_{0}\) and \(c_{u}/(\gamma R_{0})\). For \(H/R_{0}<H_{c}/R_{0}\), the bearing capacity increases continuously with an increase in the value of \(c_{u}/(\gamma R_{0})\), \(H/R_{0}\), and \(\varphi\).
2. For \(H/R_{0}<H_{c}/R_{0}\), the values of \(p/(\gamma R_{0})\) tend to become maximum corresponding to a certain value of \(R_{i}/R_{0}\). The magnitude of \(R_{i}/R_{0}\) for the bearing capacity reaching the maximum value decreases with increasing \(c_{u}/(\gamma R_{0})\), \(H/R_{0}\), and \(\varphi\). On the other hand, with smaller values of \(c_{u}/(\gamma R_{0})\), \(H/R_{0}\), and \(\varphi\), the value of \(p/(\gamma R_{0})\) increases continuously with increasing \(R_{i}/R_{0}\) and decreases continuously with increasing \(R_{i}/R_{0}\) for larger values of \(c_{u}/(\gamma R_{0})\), \(H/R_{0}\), and \(\varphi\).
3. For \(H/R_{0}\geq H_{c}/R_{0}\), the collapse mechanism is confined only within the sand layer, which is similar to the classical shallow footing mechanism. For \(H/R_{0}<H_{c}/R_{0}\), the punching shear failure occurs in the sand layer, and the log-spiral rupture lines develop from the clayey strata to the upper sand layer.  
Writing--original draft, C.Y.; software, Z.Z.; validation, Z.Z.; writing--review and editing, Y.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 51908208.  
The authors would like to acknowledge the financial support of the _National Natural Science Foundation of China_ ((No. 51908208), which made the work presented in this paper possible.  
The authors declare that there is no conflict of interest.
## 4 The Role of UAV-Base Station in Wireless Communications  
UAVs are employed for different operations in both military and civilian domains, including object detection, location tracking, goods delivery, disaster monitoring, information dissemination, etc. [13]. Recently, they have also found several applications in wireless communications because of their flexibility, adaptability, and easy deployment [19]. Figure 4 shows various use cases of UAV-BSs in wireless communications. In the following paragraphs, we briefly discuss some of these applications.
## 11 Exploring Hybrid Approaches: Integrating Diverse Techniques
### Exploring Hybrid Strategies for Internal and External Artifact Removal: Case Studies Insights
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref.** & **Domain** & **Method** & **Conclusions** \\ \hline \multirow{8}{*}{[137] All body movements} & & The system showcases a remarkable capability to differentiate between contaminated and non-contaminated EEG signals with high accuracy. It achieves this distinction with notably low processing times, quantitatively improving both the efficiency and effectiveness of the signal analysis. This dual performance feature ensures that the system not only rapidly processes data, but does so with a high degree of accuracy, making it highly effective in environments where quick and reliable EEG analysis is critical. This quantitative enhancement in processing speed and diagnostic accuracy positions the system as a leader in EEG signal analysis technology. \\ \cline{1-1}  & & & The removal of artifacts from EEG recordings results in a significant average increase in classification accuracy, ranging from 15\% to 16\%. This improvement highlights the system’s enhanced performance in signal analysis. \\ \hline \multirow{8}{*}{[139] All body movements} & & ICA proves superior performance over CCA in environments characterized by highly noisy EEG signals. This enhanced effectiveness is quantitatively evident as ICA consistently outperforms CCA under challenging conditions, effectively reducing noise interference and improving signal clarity. This key finding underscores ICA’s robust capability to handle complex, high-noise environments, where it maintains high accuracy in signal extraction and analysis, marking a significant improvement in the handling of EEG data under adverse conditions. \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Application of hybrid approaches for the removing of external artifacts in EEG. To confirm the superior performance of CNN techniques compared to LMS and RLS, Ref. [143] evaluated the performance of these methods through the analysis of plots in the time domain and the calculation of metrics such as the root-mean-square error (RMSE) and the improvement of the signal-to-noise ratio (\(\Delta\)SNR). They found that FCNN predicts the clean EEG signal better than the other two algorithms, especially in the presence of noise, offering a notable improvement in the quality of the de-noised signal. This work highlights the potential of deep learning methods in cleaning EEG signals, suggesting that FCNN may be particularly useful in clinical and research applications requiring high-quality EEG data. As above, Table 10 summarizes these remarks. Having reviewed the standard techniques, we will now focus on fuzzy methodologies as mentioned in the introduction, exploring how these advanced approaches improve the removal of artifacts in EEG signals, enriching the analysis with their ability to handle uncertainty and ambiguity of neurological data.