esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, a three-dimensional finite element model was developed. The critical bearing capacity of subsoil for distinguishing flexible and rigid floating piles was determined. Two typical engineering cases were used to validate the accuracy and practicality of the modeling method, with a maximum error of less than 20%. Furthermore, an analysis of the factors influencing soil arching in embankments supported by rigid floating piles was conducted. The main conclusions are as follows:  
1. Soil arching in the embankment begins to occur when the bearing capacity of the foundation reaches 150 kPa, at which point the pile will function as a rigid floating pile. When the bearing capacity of the foundation reaches 280 kPa, plastic failure occurs at the bottom of the embankment, and the soil arching is fully developed, aligning with the criteria for end-bearing piles.
2. It has been found that the soil arching in the embankment supported by rigid floating piles does not fully develop due to an insufficient bearing capacity of the foundation. When \(H/(s-a)\geq 1.6\), the earth pressure coefficient at the top of the embankment still approaches \(K_{0}\), indicating the onset of soil arching. Even if the \(H/(s-a)\) reaches 3.25, no plastic failure occurs in the embankment. As the pile spacing increases, the height of both the inner and outer arches in the embankment increases.
3. Selecting an embankment fill with a higher friction angle was beneficial for the development of soil arching. A larger fill cohesion can effectively reduce the differential settlement in the embankment. With an increase in the friction angle from 20\({}^{\circ}\) to 40\({}^{\circ}\), the vertical stress above the subsoil decreased by 34.9%. It was found that the increase in cohesion from 1 kPa to 7 kPa reduced the settlement on top of the embankment and surface of subsoil by approximately 72% and 58%, respectively.
4. It was also revealed that rigid floating piles rely on end resistance and skin friction to sustain the overlying load. The skin friction of the pile increases and the neutral plane gradually moves downward as soil consolidation progresses. With increasing pile length from 12 m to 20 m, the skin friction decreased by 66.7% while the axial force along the pile increased by 300%.  
Conceptualization, Y.Z.; methodology, Y.Z. and S.H.; software, S.H.; writing--original draft preparation, Y.Z.; writing--review and editing, S.H.; validation, H.F.; formal analysis, H.F.; data curation, Y.Z.; visualization, S.H. and H.F.; supervision, Y.Z.; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.  
The financial support of the National Natural Science Foundation for General Program of China (Grant No. 52178316) and the National Science Foundation for Excellent Young Scholars of China (Grant No. 51922029) is acknowledged.  
Not applicable.  
The data presented in this study are available upon request from the corresponding author.  
The authors declare no conflict of interest.
# The Microverse: A Task-Oriented Edge-Scale Metaverese
## 2 Background and Related Work
### Network Slicing  
Network slicing (NS) is an innovative and transformative idea within the telecommunications field, with the potential to significantly reshape the deployment and management of networks. Network slicing is a fundamental concept that entails the establishment of several virtual networks, referred to as _slices_, within a solitary physical network architecture [37]. Every individual slice is customized to fulfill distinct criteria, including bandwidth, latency, security, and other performance metrics, to accommodate the wide-ranging demands of different applications and services.  
An NS strategy enables network operators to effectively manage resources and tailor services according to the distinct requirements of various use cases [38], such as massive machine-type communication (mMTC), ultrareliable low-latency communication (URLLC), and enhanced mobile broadband (eMBB). As an illustration, a network slice specifically designed for Internet of Things (IoT) devices may emphasize minimizing power consumption and ensuring extensive coverage. Conversely, a network slice tailored for augmented reality applications may prioritize minimizing latency and providing ample bandwidth.  
Implementing network slicing plays a crucial role in facilitating the establishment of 5G networks, wherein various distinct and high-demand scenarios may operate together [39]. Operators can enhance network speed and responsiveness through the dynamic creation and management of slices, guaranteeing a smooth user experience across various applications.  
Furthermore, network slicing facilitates the advancement of novel ideas and concepts by offering a dedicated environment for external developers and enterprises to generate and launch their services. The ability to adapt and change the network quickly facilitates the rapid progress of novel applications and services, stimulating economic expansion and technical innovation.  
Ensuring security and isolation between slices are of utmost importance in network slicing. The NS architectural design integrates robust security procedures to mitigate interference and safeguard data privacy. The inclusion of NS into the metaverse is of utmost importance, mainly due to the coexistence of vital services like healthcare or autonomous cars with conventional mobile services inside the same network architecture.
# The Removal of Crystal Violet from Textile Wastewater Using Palm Kernel Shell-Derived Biochar
## 3 Results and Discussion  
### Kinetic Studies  
Figure 1 shows the results of the adsorption kinetic experiments carried out by varying the contact time from 0.5 h to 24 h using 400 mg/L initial concentration. The sorption of CV increased precipitously during the first 30 min of the adsorption process and later slowed until equilibrium was reached. The adsorption rate controlled by intra-particle diffusion involves pore and surface diffusion, which occur coherently within the adsorbent particles [58]. The rapid removal may be attributed to the diffusion of CV to mesopores on the surface of BC-PKS. In our previous study [59], the average pore diameter of BC-PKS was 4.1 nm, which corresponds to mesopores (2-50 nm) as per the classification of the International Union of Pure and Applied Chemistry. The dispersion of CV molecules through the mesopores toward the BC-PKS intra particle matrix caused the removal rate to disincline because of low dye concentration and fewer adsorption sites [59; 60; 61].  
The pseudo-first- and second-order models were employed to explore the mechanism involved in the adsorption process, and the results are summarized in Table 1. Comparatively, a higher determination coefficient (\(R^{2}\)) value was obtained for the pseudo-second-order than for the pseudo-first-order. This implies that the experimental data is a superior fit with pseudo-second-order model for CV adsorption onto BC-PKS and the adsorption process is principally governed by chemisorption through electron sharing between BC-PKS and CV [62; 63; 64]. The adsorption of CV to BC-PKS can be explained by ion exchange between deprotonated carboxyl and hydroxyl groups on the surface of BC-PKS and the quaternary amine groups of cationic CV molecule [64]. Similar results have been reported for the sorption of dye onto rice husk [65] and the adsorption of basic dye onto BC-PKS [66].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**adsorbent**} & \multicolumn{3}{c}{**Pseudo-First-Order**} & \multicolumn{3}{c}{**Pseudo-Second-order**} \\ \cline{2-7}  & \(q_{e}\) **(mg/g)** & \(k_{1}\) **(1/h)** & \(R^{2}\) & \(q_{e}\) **(mg/g)** & \(k_{2}\) **(mg/g/h)** & \(R^{2}\) \\ \hline BC-PKS & 14.9 & 2.61 & 0.817 & 16.4 & 0.19 & 0.884 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Kinetics parameters for CV adsorption onto BC-PKS.  
Figure 1: The kinetic adsorption of crystal violet (CV) onto biochar derived from palm kernel shell (BC-PKS) with the model fits of the pseudo-first and second-order models (initial concentration: 400 mg/L; volume: 30 mL; adsorbent dosage: 0.5 g; reaction time: 0.5–24 h; temperature: \({}^{\circ}\)C).
# Influence of Printing Angulation on the Flexural Strength of 3D Printed Resins: An In Vitro Study
## 3 Results  
Table 2 reports the mean and standard deviation for each tested material independently from the printing orientation. No statistically significant difference was found between the tested samples.  
Two-way ANOVA tests indicate that both the material and the inclination have a significant effect on the flexural strength, and that there is a significant interaction between the two factors, as reported in Table 3.  
Table 4 reports the mean and standard deviations for each resin group depending on the printing orientation. For V-print C&B temp, flexural strength values were statistically significantly lower when the samples were printed at 90\({}^{\circ}\) (116.97 \(\pm\) 34.87) angulation with respect to 0\({}^{\circ}\) (156.56 \(\pm\) 25.58) or 45\({}^{\circ}\) (130.46 \(\pm\) 12.33). In contrast, for Bego GmbH & Co., Bremen, Germany), the samples printed with an inclination of 45\({}^{\circ}\) (148.91 \(\pm\) 21.23) showed statistically significantly higher values than the one printed at 0 (113.37 \(\pm\) 31.93) or 90\({}^{\circ}\) (100.96 \(\pm\) 16.66) No statistically significant difference was found for Bego Varseo Smile (Bego GmbH & Co., Bremen, Germany) or Sprintray Crown (SprintRay, Los Angeles, CA, USA).  
Table 5 reports the mean and standard deviations from each of the 3D-printing degrees independently from the resin type. Samples printed at a 90\({}^{\circ}\) orientation registered statistically significantly lower values of flexural strength compared to 0\({}^{\circ}\) or 45\({}^{\circ}\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Material** & **N** & **Mean** & **SD** & **Sig. \(p\) < 0.05** \\ \hline Bego Varseo Smile teeth & 30 & 133.27 & 24.64 & A \\ V-print C\&B temp & 30 & 134.66 & 30.12 & A \\ Bego trinQ & 30 & 121.08 & 31.14 & A \\ Sprintray Crown & 30 & 120.53 & 26.62 & A \\ \hline \hline \end{tabular} Legend: N: number of samples for each group, SD: standard deviation, Sig.: significant differences. Same letters per table denote no statistically significant differences (\(p\) > 0.05).  
\end{table}
Table 2: Mean flexural strengths (Mean), standard deviations (SD), and significant differences (Sign.) for each material.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **sum\_sq** & **df** & **F** & **P** \\ \hline
3D-printed material & 9099.07 & 4.00 & 4.01 & 0.00 \\ Inclination & 4894.23 & 2.00 & 4.31 & 0.02 \\ C (material):C (inclination) & 21,149.54 & 8.00 & 4.66 & 0.00 \\ Residual & 76,650.36 & 135.00 & & \\ \hline \hline \end{tabular} Legend: sum\_sq: sum of squares, dF: degree of freedom, F: f-value, P: \(p\)-value.  
\end{table}
Table 3: Two-way ANOVA results.
## 3 Mathematical Model for Optimal Trajectory Planning
### Objective Function Design
#### 3.2.3 Surveillance Area Important (SAI)  
We divided the whole operational area into three different important levels, which are considered the cell edge area, middle area, and center area of the total operational area. Cell edge areas are the areas that are located in the boundary line of operational areas, and the middle areas are the adjacent cells or inner cells of our operational area. Since the penetration of any illegal drones directly come from the edge area, we have assigned the highest priority of surveillance to those areas, which means the cell edge area should be surveilled first in our implementation. Whereas the center cells are the most secured area among the total operational area, we assigned the least SAI Value for the center area. In this way, we have assigned the order of priority to surveille whole area coverage. The normalized SAI value can be calculated by Equations (15)-(17),  
\[F_{SAI}=\frac{\sum_{i=1}^{N_{w}}SAI_{i}(t)}{maxSAI} \tag{15}\]  
\[SAI_{i}=\sum_{cell\ xeN(i)}v_{cellx}(t) \tag{16}\]  
\[maxSAI=N_{w}*N_{i}*v_{max} \tag{17}\]  
where \(SAI_{i}(t)\) the SAI values of the \(i\)th waypoint, \(v_{cellx}(t)\) is the cell value of \(x\) at the \(i\)th flight time, \(N_{i}\) is the supervised cell set from the \(i\)th waypoint, \(v_{max}\) is the maximal SAI value.
# Enhanced Multiple Speakers' Separation and Identification for VOIP Applications Using Deep Learning
## 3 Model Description
### Voice Separation for Multiple Speakers
#### 3.2.2 Separation Training Objective  
We can directly use the source-to-distortion ratio (SDR) as our training objective because the network's output is the waveform of the predicted clean signals. As the training objective, we employ the scale invariant source-to-noise ratio (_SI-SNR_), which is used as the assessment measure in [12; 14], instead of the usual SDR. Following is a definition of the _SI-SNR_:  
\[s_{target}=\frac{\left\langle\hat{\mathbf{s}},s\right\rangle s}{\left\|s \right\|^{2}} \tag{1}\]  
\[e_{noise}=\hat{\mathbf{s}}-s_{target} \tag{2}\]  
\[SI-SNR=10\ log_{10}\frac{\left\|s_{target}\right\|^{2}}{\left\|e_{noise} \right\|^{2}} \tag{3}\]  
where \(\hat{\mathbf{s}}\ \epsilon\ R^{1\times t}\) is the estimated source and \(s\ \epsilon\ R^{1\times t}\) is the target clean source.  
The length of the signals is denoted by \(t\). To ensure scale-invariance, \(\hat{\mathbf{s}}\) and \(s\) are both normalized to have zero-mean. The source permutation problem [12; 13; 14] is solved using permutation invariant training (PIT) [13], which is used during training. Speaker Identification Model.
## 4 Fractal Analysis to Support Oral Cancer and OPMDs Diagnosis and Prognosis  
From the analysis of the results presented in the selected literature, fractal analysis can be globally considered to be a valid supporting method to diagnose oral cancer or its precursors in humans.  
The most frequently investigated parameter that gave, in general, supportive robust results was the nuclear fractal dimension (nFD). nFD was statistically significantly different in OC nuclei than in normal cell nuclei [23; 24; 26; 38; 39]. In detail, a progressive increase in the mean FD was shown from healthy mucosa to poorly differentiated OC. These pieces of evidence were strengthened by the statistical analysis of [PERSON] et al., who reported that nFD was significantly higher in tumors than in healthy tissues, with an 86% specificity and 100% sensitivity [41], and confirmed by [PERSON] et al. [43], who reported a similar high sensitivity rate (correctly identifying cancerous cells) and high specificity rate (correctly identifying normal cells). Furthermore, the nuclear fractal dimension was also proved to be an independent prognostic factor of survival in oral cancer patients, while, in synergy with the grading, it demonstrated significant predictive potential for lymph node metastasis [31; 32; 33].  
Furthermore, nFD also correlated with prognostic factors from the tumor microenvironment, such as proliferation and immune infiltration, into a single digital pathology-based biomarker [22; 32; 42].  
The exact significance was also reported in those studies in which the fractal object was a histological section. In these cases, the FD of histological architecture and the epithelial-connective interface of healthy mucosa compared with OC or OPMDs significantly differed [21; 27].  
Moreover, fractal analysis applied to vasculature also revealed its efficacy as a prognostic parameter to determine the potential malignant progression of OLP lesions based on the microvessel density analysis [36]. Statistically significant differences were also reported concerning the vascular fractal dimension of OC compared with normal mucosa [33].
## 6 Simulation Results and Discussion
### Simulation Parameters
#### 6.1.1 EC  
The EC of the proposed and benchmark protocols is shown in Figure 16. This figure shows the EC of the sensor nodes in IoT-UWSN (during packets transmission). It is obvious from the figure that GEDAR, LMPC and LETR have high EC until the network is sparse. The reason for high EC is that GEDAR performs several DAs for void hole alleviation. These DAs result in excessive EC in GEDAR. Meanwhile, LMPC has high EC due to multiple paths. This tree generation helps the IoT-UWSN to perform reliable data delivery. Similarly, in LETR, excessive DAs and adaptive transmission range adjustments result in high EC. In contrary, the EC reduces by increasing the nodes density (due to several alternative paths). Therefore, the EC of Improved Hydrocast is minimum because of short recovery paths for void nodes. Fixed deployment of the nodes also contributes towards minimum EC of the protocol. Whereas, Co-hydrocast and Co-Improved Hydrocast consume higher energy than Improved hydrocast routing protocol because of an opportunistic cooperation technique. At this end, the EC in Hydrocast in higher than both aforementioned protocols because of longer recovery paths.  
In proposed protocols, EC is high when the UWSN is sparse. In contrary, EC is low. EP-VIR-Three provides the path using the potential forwarder node (by checking 3-hop neighbor's availability) with minimum neighbors. These forwarders help in collision-free communication with minimum interference. Therefore, little energy is dissipated on 3-hop neighbors checking and during collision-free path selection (on the base of CF value), while, in BF-SPR-Three, the EC is lesser than all above-mentioned protocols during sparse network. In addition, EC is low when the network is dense. The reason is the fastest and shortest path selection (using the BF algorithm) and 3-hop neighbors checking.  
It is clear from Figure 16 that both proactive protocols outperformed the counterpart as compared to benchmark routing protocols, i.e., Hydrocast, Co-Hydrocast and Co-Improved Hydrocast routing protocols.
# Reducing the Uncertainty of Lidar Measurements in Complex Terrain Using a Linear Model Approach
## 2 Materials and Methods
### Wind Field Reconstruction Methods
#### 2.2.1 Continuous Least-Square  
This procedure, here referred to as continuous least-square (_CLS_), is a modified variant of the standard VAD method as used in commercial systems [26] to determine the wind velocity components. The idea behind this is that the velocity components are calculated continuously and not after just one trajectory cycle. This means that if the trajectory has \(N\) data points, the last \(N\) data points will be used for the calculation. Considering Equation (1), a system of equations (Equation (3)) can then be established from these \(N\) data points to determine the wind velocity components \(u,\ v,\ w\) from the lidar data. The matrix \(\mathbf{A}\) of the equation system consists of the components of the normal vectors \(\vec{n}(x,\ y,\ z)_{i}\) of the used \(N\) data points. To solve Equation (3), the vector \(\mathbf{b}\) is multiplied by the inverse Matrix \(\mathbf{A^{-1}}\). If the trajectory has more than three data points (which are at least necessary) the Moore-Penroe pseudoinverse \(\mathbf{A^{-1}}\) will be used to solve the system of equations at the time step \(t\). In order to solve this system of equations, it is not necessary to assume the homogeneous flow, since a solution is estimated which has the smallest absolute error. As a result, measurement errors (unrealistic data (e.g., due to bad CNR) strongly distort the solution of the equation system (Equation (3)). These unrealistic data must then be filtered in post-processing.
## 3 Problem Definition and Formulation
### System Model
The set \(\mathcal{P}_{j}\) consists of all such feasible paths that can support the transmission of task \(j\)'s data under the current network conditions. Here, \(k\) denotes the path index within the set \(\mathcal{P}_{j}\). Each path \(P_{jk}\) is characterized by various network parameters, including but not limited to bandwidth, latency, and network load. These parameters are critical in  
Thus, the set of possible paths \(\mathcal{P}_{j}\) encompasses all paths that meet the network's resource requirements (e.g., available bandwidth, latency tolerance, etc.) and are considered viable for transmission at any given time. The multi-path scheduling strategy utilizes these possible paths to dynamically allocate transmission tasks across multiple routes, helping to mitigate issues such as packet loss, congestion, and latency, thereby improving the overall network efficiency. Resource allocation and scheduling are thus formulated as an optimization problem that assigns tasks to paths, while considering resource constraints and network performance metrics. Resource competition among satellite nodes is modeled using game-theoretic principles. Each satellite node \(i\) possesses a utility function \(U_{i}\) that depends on its resource allocation strategy \(S_{i}\) and its interactions with other nodes. The objective for each node is to determine the optimal strategy \(S_{i}\) that maximizes \(U_{i}\), leading to a game equilibrium (e.g., Nash equilibrium) that guides an equitable and efficient resource distribution. This equilibrium ensures optimal resource utilization and load balancing across the network. Formally, for each satellite node \(i\), the resource vector is \(R_{i}=(C_{i},B_{i},Q_{i})\), and the resource demand for each task \(j\) is \(T_{j}=(C_{j},B_{j},P_{j})\). Task \(j\) necessitates data transmission through the network, with path selection governed by the multi-path scheduling strategy. Due to the inherent dynamics in network topology and traffic loads, resource allocation strategies must be adaptable in real time, to optimize network utility.
# PrimaVera: Synergising Predictive Maintenance
## 4 Predictive Maintenance Process Model
### PrimaVera Approach
#### 4.1.2 Data Processing and Diagnostics  
Novel automated methods for real-time data validation and correction will be developed, building upon probabilistic and statistical techniques for missing value imputation [25; 26] and outlier detection [28]. Development of these methods is done under realistic assumptions reflecting the industrial practice. This implies that methods should be able to handle high-dimensional data streams, mixed sampling frequencies, guarantee performance and should take internal dependencies into account [29]. Furthermore, care will be taken to incorporate effects of changing operational conditions on the measurements, as these are directly affecting failure rates, and separate them from the direct effects caused by failures.  
Since uncertainties propagate along the predictive maintenance workflow, these techniques play a crucial role at the beginning of the predictive maintenance cycle. Since it is impossible to eliminate uncertainty, it is better to acknowledge uncertainty and quantify it [97]. Uncertainty is a quantitative indication of the quality of the result, it allows decision makers to assess the reliability of predictive maintenance process outcomes [98]. The PrimaVera project will explore methods, such as fuzzy sets [98], Bayesian approach [97] or Dempster-Shafer theory [99], to quantify uncertainty throughout the predictive maintenance workflow. This will contribute to overcoming the data uncertainty obstacle.  
A key step in failure prevention is to understand why systems fail. While data-driven techniques are good at finding correlations between failure modes, finding causal relations is challenging. Causal inference graphs can be efficiently queried to find the root causes of specific failures [100; 101]. The PrimaVera project will propose a framework to construct these causal inference graphs and extend them to perform in an industrial setting. The framework will build on the advantages of Bayesian networks [102; 103], Granger causality [104] and transfer entropy [35; 105].