esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Theoretical Contribution  
In group decision-making processes, different perspectives often arise due to individual decision-makers' varying knowledge, experience, and departmental backgrounds. Each decision-maker may also reach different conclusions due to incomplete information reception and personal uncertainties in interpreting linguistic variables. This study first used grey numbers to capture the uncertainty in individual expert judgments. In the past, group opinions were mostly aggregated using averages, but averages are easily affected by extreme values and may lead to information loss. This study adopted the Aczel-Alsina function to integrate different expert opinions. This approach not only considers expert weights but is also less likely to cause information loss. Additionally, it works well with grey numbers and fuzzy logic, can process imprecise or uncertain expert assessments, and maintains the integrity of uncertainties throughout calculations [21,50].  
The DEMATEL and ISM methods rely on expert opinions rather than large-scale statistical surveys, typically employing alternative means to enhance reliability, such as fuzzy, rough, or grey numbers, to capture uncertainty in expert opinions. Our proposed model uses grey numbers and the Aczel-Alsina function to aggregate input data, representing an integrated approach that constitutes this study's improvement. For validation, we asked a decision-making team to confirm the findings, and experts indicated results that aligned with their practical experience, suggesting credible outcomes. The DEMATEL and ISM analysis data derived from expert surveys regarding pairwise influence comparisons between factors. This survey method differs from other MCDM approaches, making it impossible to use identical survey data for analysis and comparison with other models. Currently, most MCDM models (AHP, ANP, TOPSIS, and VIKOR) focus primarily on calculating factor weights or ranking alternatives, with no similar methods available for investigating influential relationships between factors. This limitation reinforces the uniqueness and necessity of our approach in examining inter-factor relationships rather than simple weighting or ranking.  
Integrating DEMATEL and ISM methodologies offers two key advantages: it eliminates the need for additional surveys and enables direct transitivity analysis using the DEMATEL-derived influence matrix through ISM concepts. This integration creates stronger consistency between the factor hierarchy and DEMATEL's network relationships. By combining the ISM hierarchical structure with the INRM network diagram, decision-makers can better understand factor relationships, distinguish between causes and effects, and establish improvement priorities under resource constraints. This provides manufacturers with a more efficient transformation strategy.
# Optical Wireless Fronthaul-Enhanced High-Throughput FC-AE-1553 Space Networks
## 4 Network Resource Allocation Algorithm Design for the HSN
### HSN Scheme Transmission Process  
This paper introduces a time-slot-based, multi-priority, multi-channel scheduling strategy for the terminals of the HSN. It incorporates the centralized control communication features of FC-AE-1553 while also capitalizing on the distributed autonomous communication attributes of Li-Fi. The whole HSN scheme transmission process is shown in Figure 10.  
For periodic traffic, their inherent time determinism means their scheduling is not subject to dynamic time zones. In response, the NC strives to evenly distribute the traffic transmissions within smaller cycles according to their frequencies in a larger cycle. When scheduling this periodic traffic, pre-arranged tasks are allocated to specific time slots for execution. This approach ensures the predictability of certain important tasks with low throughput in the space information network, achieved through periodic transmissions.  
Bursty traffic, on the other hand, presents a challenge due to the uncertainty of its data throughput, frequency, and delay. As such, each small cycle requires dynamic layout adjustments based on the current network conditions. The experimental process is primarily composed of three parts: task selection, channel selection, transmission, and statistics. Each small cycle initiates a complete process until all time within that cycle has been utilized or all tasks have been serviced.  
The first step is task selection. During this phase, the system polls the bursty traffic queue for each sending node. If there is unfinished traffic in the preemptive traffic queue, it is treated as a priority. If none exists, the system addresses the multi-priority traffic queue and eventually selects a task for transmission.  
The second step is channel selection. Provided a Li-Fi path is accessible, the comprehensive evaluation algorithm for channel selection is initiated. By taking into account the unique bandwidth, delay, and packet-loss weight coefficient of the chosen task, as well as the parameter values of different channels, the highest-scoring channel is selected for task transmission. If the Li-Fi path is inaccessible, the FC is chosen. Additional factors such as the timelines must also be considered. If time is insufficient, no arrangement is made. Furthermore, for each sending node, the FC timeline only permits the arrangement of one type of bursty traffic.  
The third step involves transmission and statistics. Once the channel is confirmed, tasks are transmitted based on the selected channel. The system then logs the actual throughput, delay, and packet loss of this transmission, recording the relevant data into the path matrix and the overall statistics module.
# Prediction of Swelling Index Using Advanced Machine Learning Techniques for Cohesive Soils
## 2 Materials and Methods
### Methodology  
In order to find the most appropriate model to predict \(C_{s}\) of soil using previously mentioned geotechnical parameters as an input, the methodology consisted of the following steps:  
1. Creation of a geotechnical database of Algerian soil, collected from different laboratories around the geotechnical constructions projects in progress or completed before.
2. Selecting the optimal input variables using Principal component analysis (\(OSA\)), Gamma Test (\(GT\)), and Forward selection (\(FS\)) has been used.
3. Analyzing selected optimal inputs using several machine learning methods. The \(ELM\), \(DNN\), \(SVR\), \(RF\), \(LASSO\), \(PLS\), \(Ridge\), \(KRidge\), \(Stepwise\), and \(PG\) methods have been used in this step for proposing 30 models.
4. Determine the most appropriate model for predicting the \(C_{s}\) value between the thirty proposed models using important statistical performance indicators as \(MAE\), \(RMSE\), \(IOS\), \(NSE\), \(R\), and \(IOA\).
5. Assessing the predictive capacity of the best model to overcome under-fitting and over-fitting problem by using the K-fold cross validation approach with \(K=10\).
6. Doing a sensitivity analysis by utilizing the step-by-step method to know the most or less influenced input on \(C_{s}\) through the proposed model.  
The research methodological for determining the most appropriate model to estimate \(C_{s}\) is systematically described in Figure 3.
## 2 Tissue-Engineered Scaffolds with GBM and Neural Tissues
### 3D Printing and Bioprinting  
Both three-dimensional (3D) printing and bioprinting are counted as the additive manufacturing process that can print precise complex design to repair damaged tissues or organs; however, the main difference between these two methods is that 3D bioprinting builds customized structures from cells and supporting biomaterials (bioink), while 3D printing solely uses biomaterials for printing [12]. Although there is a high volume of research on 3D printing and bioprinting, the literature on their applications in NTE is still relatively limited. The addition of GBMs can lead to a higher viscosity (due to the shear thinning properties), hence improving printability. In addition, water-dispersible GBMs can also be used as a component of bioink; however, the concentration plays a significant role and therefore needs careful evaluation [15; 81; 82; 83]. A study was performed using waterborne biodegradable polyurethane with soft segments that mainly included poly(\(\varepsilon\)-caprolactone) and 20 mol% of shorter poly(D,L-lactide) chains. The polyurethane dispersed (25 ppm) in a cell culture medium then underwent a sol-gel transition near 37 \({}^{\circ}\)C with a proper gel modulus. Afterwards, G or GO was mixed with polyurethane to prepare a G-based bioink for neural stem cell printing, which resulted in a suitable bioink for printing and cell survival (Figure 7). Interestingly, the addition of G or GO at a very low content (25 ppm) not only resulted in a promising bioink but also significantly improved oxygen metabolism (2- to 4-fold increment) and neural differentiation. On the basis of these results, it can be concluded that the optimum sample was PU/G since it presented better efficacy, especially for cell proliferation and differentiation and oxygen metabolism [69]. It is worth mentioning that in order to achieve these precise conduits, 3D printing can be applied alongside the other techniques, and the related studies are given in the conduit section [25; 83; 82]. Table 1, highlight the recent studies in application of graphene based materials for nerve regeneration.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Biomaterial(s)** & **GBMs Concentration** & **Target and Cell Type** & **Outcomes** & **Year** \\ \hline Silk/rGO and SF/rGO (Post reduction of Silk/GO) & 5\% and 10\% & PNS Neuromonia NG108-15 & Conductivity: 4 \(\times\) 10\({}^{-5}\) S/cm (hydrated) & 2021 \(\uparrow\) metabolic activity and CPM in SF/rGO & 2021 \(\downarrow\) metabolic activity and CPM in SF/rGO & [45] \\ \hline Polydopamine/carboxylic GO/PLLA (PDA/CG/OPPs-PLLA) & 0.03\% wt & PNS Schwann cells & Elastic modulus: 260 MPa, \(\uparrow\) CPM & 2020 \(\uparrow\) email proteins expression & 2020 \(\uparrow\) email proteins expression & [63] \\ \hline PCL/G & 1\% and 2\% & NTE Mouse E12 & \(\uparrow\) concentration = \(>\) \(\uparrow\) fiber diameter, elastic modulus, max stress, and differentiation & 2019
## 4 DE-KELM-Based Gearbox Fault Classification
### Gearbox Fault Diagnosis Experimental Data  
The gearbox fault diagnosis experimental datasets were also acquired from the Case Western Reserve University [49]. The data were measured in the same operating conditions as presented in Section 4.3. The datasets included vibration signals of a gearbox in eight operating conditions (healthy and faulty), which are listed in Table 9. The measured vibration signals of these conditions all lasted 75 s, and the sampling frequency was 5120 Hz; thus, in each working condition, the vibration signal included 384,000 sampling points. One datum was taken every 0.5 s as a sample; thereby, 150 samples were obtained in each situation. The sampled vibration signals are plotted and shown in Figure 18.  
Figure 17: Complete workflow of the proposed hybrid gearbox fault diagnosis method.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Gearbox** & **Classification** & **Operating** & **Sample** \\
**Elements** & **Labels** & **Conditions** & **Numbers** \\ \hline \multirow{4}{*}{Bearing} & 1 & healthy bearing & 150 \\  & 2 & inner ring fault & 150 \\  & 3 & outer ring fault & 150 \\  & 4 & roller fault & 150 \\ \hline \multirow{4}{*}{Gear} & 5 & healthy gear & 150 \\  & 6 & pitting fault & 150 \\  & 7 & broken teeth & 150 \\  & 8 & tooth root cracks & 150 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Eight working conditions of a gearbox included in the experimental datasets [43].  
Figure 18: Sampled vibration signals in eight operating conditions. (**a**) Healthy bearing, (**b**) bearing inner ring fault, (**c**) bearing outer ring fault, (**d**) bearing roller fault, (**e**) healthy gear, (**f**) gear pitting fault, (**g**) gear broken tooth fault, (**h**) gear tooth root crack faults.  
Contrast Experiment I--Gerbox Fault Diagnosis with Contrasting Vibration Signal Decomposition Methods: GWO-VMD and VMD  
To verify that using the GWO-VMD method proposed in this paper to decompose signals can improve the final fault classification accuracy, in this section, GWO-VMD and VMD were both applied to process the same vibration signals. Then, the same signal reconstruction, feature vector calculation, and fault classification methods were followed to diagnose bearing and gear faults. The experimental process is illustrated in Figure 19.
## 4 Sensor Integration Platform
### Satellite
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Main Categories** & **Sub Categories** & **Benefits** & **Constraints** \\ \hline \multirow{3}{*}{Earthquake Emergency Vehicles} & Rescue & Quick response time and mobility. & High cost and limited use \\ \cline{2-4}  & Commanding & Emergency communication systems. & Maintenance challenges \\ \cline{2-4}  & Telemedicine & Equipped with specialized tools. & Safety concerns \\ \hline \multirow{3}{*}{Base Stations} & Fixed & Long-term and immediate monitoring & Damaged after destructive \\  & & data for early warnings. & earthquakes and false alarms \\ \cline{2-4}  & Mobile & Light weight and easy and quick to & Limited coverage, technical \\  & & deploy monitoring and analysis. & challenges, and cost \\ \hline \multirow{6}{*}{Unmanned Vehicles} & Mobile robot: urban search & \multirow{3}{*}{Decreases human risk.
\begin{tabular}{} \end{tabular} } \\  & and rescue snake robots, & & \\ \cline{1-1}  & evacuation robots, on-site & & \\ \cline{1-1}  & diagnosis robots, etc. & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1} \cline{2-4}  & Unmanned Aerial Vehicle & Improves efficiency and can cover large & Weather sensitivity \\ \cline{1-1}  & (UAV) & area in short period of time. & Privacy concerns \\ \cline{1-1}  & & Provides long-term light in & Technical challenges \\ \cline{1-1}  & & dark evenings. & High cost \\ \cline{1-1}  & & Creates 3D maps for monitoring. & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The benefits and limitations of earthquake sensor integration platforms for decision making.
# Data Aggregation Based on Overlapping Rate of Sensing Area in Wireless Sensor Networks
## 4 Implementation of AggOR Scheme
### Data Routing  
In the process of data routing, member nodes send data to their gathering nodes through one hop transmission, while gathering nodes aggregate data collected in their gathering areas and then send them to the sink through the energy-efficient paths. Additionally, independent nodes send their own data to the sink without aggregation. The energy-efficient paths are established according to Rule 3, in which the non-gathering nodes are the first choice for relay nodes and then the candidate parent nodes with the most residual energy are selected as forwarders.  
An instance of the construction of gathering areas and the data routing is depicted in Figure 6, which is the part in dotted circle of Figure 5. We suppose that \(v_{4}\) has more residual energy than \(v_{3}\), and \(OR_{3,4}=0.7>OR_{T}\). All the nodes in the network are initialized as free nodes, i.e., \(FN_{3}=\{v_{6},v_{7}\}\), \(FN_{2}=\{v_{3},v_{4},v_{5}\}\) and \(FN_{1}=\{v_{1},v_{2}\}\). At the beginning, \(v_{6}\) and \(v_{7}\), at the highest level 3, calculate the overlapping rates of sensing area with their candidate parent nodes. We get \(OR_{4,6}>OR_{3,6}>OR_{T}\) and \(OR_{4,7}>OR_{T}>OR_{5,7}\). Thus \(CG_{6}=\{v_{3},v_{4}\}\) and \(CG_{7}=\{v_{4}\}\). Due to more residual energy, \(v_{4}\) is selected by \(v_{6}\) and \(v_{7}\) as their gathering node, \(GA_{4}=(v_{4},\{v_{6},v_{7}\})\). Then \(FN_{3}=\emptyset\) and \(FN_{2}=\{v_{3},v_{5}\}\). Next the free nodes \(v_{3}\) and \(v_{5}\) at level 2 construct their gathering areas. Similarly, regarding the candidate parent nodes, \(OR_{1,3}<OR_{T}\), \(OR_{2,3}<OR_{T}\) and \(OR_{2,5}=OR_{T}\). Therefore, \(CG_{3}=\emptyset\) and \(CG_{5}=\{v_{2}\}\). Node \(v_{5}\) chooses \(v_{2}\) as it gathering node, \(GA_{2}=(v_{2},\{v_{5}\})\). Since \(CG_{3}=\emptyset\),\(v_{3}\) further calculates overlapping rate of sensing area with its neighbor node \(v_{4}\), and gets \((2L_{3}-1)\times OR_{3,4}=3\times 0.7>2\). Because there is no other neighbor node, \(CG_{3}=\{v_{4}\}\), and \(v_{3}\) chooses \(v_{4}\) as its gathering node, \(GA_{4}=(v_{4},\{v_{3},v_{6},v_{7}\})\). After that, \(FN_{2}=\emptyset\) and \(FN_{1}=\{v_{1}\}\). \(v_{1}\) at the level 1 becomes an independent node, and \(FN_{1}=\emptyset\). For the data transmission, \(v_{3}\), \(v_{6}\) and \(v_{7}\) send their data to \(v_{4}\), \(v_{4}\) aggregates data in \(GA_{4}\) and then chooses \(v_{1}\) (non-gathering node) to relay its data to the sink. Node \(v_{5}\) sends data to \(v_{2}\) which aggregates and forwards data to the sink, while \(v_{1}\) sends its data to the sink directly.
# Recent Development of Morphology Controlled Conducting Polymer Nanomaterial-Based Biosensor
## 2 Polypyrrole-Based Electrodes  
Polypyrrole (PPy), a class of conducting polymers possessing five-membered heterocyclic rings, is renowned for their high electrical conductivity, reversible redox activity, and environmental stability. Ease of synthesis and tunable conductivity make PPy particularly useful for applications involving nanoscale architectures [67; 68] as their nanostructures can be readily prepared via electrochemical and chemical routes in both aqueous and non-aqueous solutions [69; 70]. Electrochemical polymerization is commonly used to fabricate deposited films on substrates, whereas chemical polymerization is used to produce powdered products. Structurally, the PPy chain is intrinsically planar and linear because of \(\alpha\)-\(\alpha\) coupling. However, conformational defects (e.g., \(\alpha\)-\(\alpha\) bonds with non-regular rotation) and structural defects (i.e., the presence of \(\alpha\)-\(\beta\) bonds, hydroxyl moieties, and carbonyl groups) induce structural changes that facilitate their application for the production of biosensors [71; 72].
## 1 Introduction
Similar to the [3] concept, the German Geotechnical Society (DGGT) proposed a correlation between modulus ratio and static modulus retrieved from oedometer test results [6]. The relationship between dynamic and static modulus is explained by comparing the measured constraint modulus (M\({}_{\rm dyn}\) for dynamic constraint modulus, M\({}_{\rm stat}\) for static constraint modulus). Following [3], ref. [7] proposed a correlation illustrating the relationship between static and dynamic modulus in the \"Recommendations of the Soil Dynamics Working Committee\" of the DGGT. The correlation between dynamic and static moduli is expressed in terms of the oedometer modulus for one-dimensional compression (zero lateral deformation), with M\({}_{\rm stat}\) = M\({}_{\rm oedo}\). Ref. [7] further contributed to the DGGT findings by expanding the E\({}_{\rm S}\) range from approximately 1 MPa to 300 MPa, while the dynamic to static ratio increases significantly according to DGGT (Figure 1). It can be observed that [PERSON]'s proposal exhibits limitations in estimating the \(\text{Eq}_{\text{dyn}}/\text{E}_{\text{stat}}\) when compared to the curves presented by both the [6,7]. This paper presents the results of a comprehensive experimental study aimed at assessing the small to medium strain stiffness parameters of the different types of sands. The experimental outcomes of five different types of sandy soils are evaluated and analyzed in conjunction with the Alpan concept. To achieve this, the index and classification properties of the sandy specimens were determined. Subsequently, the relative density of the specimens was defined in accordance with traditional density classifications, ranging from medium dense to dense. The specimens were subjected to both monotonic compression in static tests (MTX) and high-frequency vibration in resonant column tests (RCT). While there are uncertainties regarding the applicability of the empirical correlations derived from these relationships (Figure 1) in terms of soil type, index properties and stress conditions, the experimental program carried out covers sands with different firmness (loose and tight) and uniformity under different environmental stresses.
## 4 Discussion
Due to the lack of repelling force from the rigid wall and the insert of the repulsive force between liquid and gas molecules, the driving force for outflow decreases. Thus, the liquid phase tends to be trapped inside the nanochannel permanently. The trapped state of the nonwetting liquid appears as a result of a thermodynamic equilibrium among the interaction of the neighboring liquid-gas clusters and the liquid-solid interface. In the undegassed system, the alternative distribution of the liquid column/chain and gas cluster/molecule is anisotropic and energetically unfavorable or unstable. The system stability is especially poor for the undegassed system under a higher loading rate (0.1 mm/s) and system temperature (348 K), as shown in Figure 4c. The fluctuations of the \(P\)-\(\Delta V\) curves are probably caused by the uncompleted dissolution of small bubbles with a fast increased pressure under a high loading rate. The alternative distribution of the liquid column and the gas cluster is more likely to be formed in a nanochannel rather than a liquid column with gas molecule inserted distribution. Besides, this distribution is more unfavorable and energetically unstable. Through molecular thermal motion, gas, and liquid molecules adjacent will keep colliding with each other with high velocity and frequency in a random way, and hence, a gradual gas-liquid molecular site exchange can occur, despite in a much slower way than the diffusion process in the bulk phase in the sense of probability statistics. The higher the system temperature, the more furiously molecules collide each other, and the less stable the system is. In relatively large pores, gas channeling may also occur. These could be factors causing the fluctuation of \(P\)-\(\Delta V\) curves in Figure 4e when the system temperature is 348 K.  
To sum up, the influence of gas on a liquid/nanoporous system is mainly on the liquid outflow. On one hand, the originally existing gas in the nanochannel is compressed into a gas cluster, which will promote the liquid outflow during unloading. On the other hand, the excessively dissolved gas may break the continuity of the liquid molecular chain in the nanochannel and then the driving force for liquid outflow is reduced correspondingly, which in turn reduces the system's reversibility. Moreover, the increase of the amount of gas in the system will lead to instability if the excess small bubbles are not dissolved in time, especially under working conditions of a high loading rate or high system temperature. Figure 7: Cartoon of status of gas liquid in a nanochannel of degassed and undegassed systems. (a) gas cluster, (b) liquid column/chain.