esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### ANN-GA Model  
The surface roughness of the machined Al6061 aluminum blocks has been predicted using the hybrid ANN-GA algorithm. A total of 24 and 6 data have been, respectively, employed to train and test the model. The feedforward backpropagation and the number of layers obtained in Section 3.1 have been adopted for the actual model training. All 30 data were used for the training of the model. The results generated by different ANN-GA configurations are reported in Table 6 in terms of the root mean square error (RMSE) and regression values (R2). Figure 12: ANN-PSO regression line for training. Figure 13: ANN-PSO regression line for testing. employed to train and test the model. The swarm population size values and the acceleration factors C\({}_{1}\) and C\({}_{2}\), and the configuration \"triangular MF shape--grid partition FIS generator--hybrid optimization method\", reported in Section 3.2 and 3.3 have been adopted for the model training. The results generated by different ANFIS-PSO configurations are reported in Table 7 as RMSE and R\({}^{2}\). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**POP** & **c\({}_{1}\)** & **c\({}_{2}\)** & **RMSE** & **R\({}^{2}\) Training/R\({}^{2}\) Testing** \\ \hline \multirow{8}{*}{1.0} & & 2.0 & 0.0187 & 0.9484/0.016 \\ \cline{3-5}  & & 2.25 & 0.02821 & 0.9533/0.8134 \\ \cline{3-5}  & & 2.5 & 0.024989 & 0.9785/0.8496 \\ \cline{3-5}  & & 2.75 & 0.024574 & 0.9799/0.7399 \\ \cline{3-5}  & & 3.0 & 0.052431 & 0.9147/0.3561 \\ \cline{3-5}  & & 2.0 & 0.023474 & 0.9787/0.1024 \\ \cline{3-5}  & & 2.25 & 0.039257 & 0.9555/0.1503 \\ \cline{3-5}  & & 2.5 & 0.038532 & 0.95/0.3027 \\ \cline{3-5}  & & 2.75 & 0.080054 & 0.8763/0.4759 \\ \cline{3-5}  & & 3.0 & 0.053106 & 0.8492/0.6129 \\ \cline{3-5}  & & 2.0 & 0.05086 & 0.8915/0.623 \\ \cline{3-5}  & & 2.25 & 0.07433 & 0.8697/0.2659 \\ \cline{3-5}  & & 2.5 & 0.11021 & 0.6779/0.7602 \\ \cline{3-5}  & & 2.75 & 0.082424 & 0.7561/0.4472 \\ \cline{3-5}  & & 3.0 & 0.090767 & 0.5891/0.6155 \\ \cline{3-5}  & & 2.0 & 0.062678 & 0.8877/0.3566 \\ \cline{3-5}  & & 2.25 & 0.098959 & 0.7556/0.3534 \\ \cline{3-5}  & & 2.5 & 0.073766 & 0.7975/0.4726 \\ \cline{3-5}  & & 2.75 & 0.087366 & 0.8252/0.6249 \\ \cline{3-5}  & & 3.0 & 0.11701 & 0.6371/0.3199 \\ \hline \multirow{8}{*}{2.5} & & 2.0 & 0.10659 & 0.6907/0.6168 \\ \cline{3-5}  & & 2.25 & 0.081914 & 0.7529/0.5442 \\ \cline{3-5}  & & 2.5 & 0.094932 & 0.7488/0.3495 \\ \cline{3-5}  & & 2.75 & 0.1099 & 0.6476/0.5399 \\ \cline{3-5}  & & 3.0 & 0.10732 & 0.6062/0.5814 \\ \cline{3-5}  & & 2.0 & 0.022436 & 0.9726/0.7476 \\ \cline{3-5}  & & 2.25 & 0.034272 & 0.9601/0.8235 \\ \cline{3-5}  & & 2.5 & 0.045889 & 0.9401/0.856 \\ \cline{3-5}  & & 2.75 & 0.047704 & 0.9193/0.2898 \\ \cline{3-5}  & & 3.0 & 0.045248 & 0.8934/0.6458 \\ \cline{3-5}  & & 2.0 & 0.035986 & 0.9644/0.4331 \\ \cline{3-5}  & & 2.25 & 0.044615 & 0.9438/0.3437 \\ \cline{3-5}  & & 2.5 & 0.072488 & 0.8512/0.5891 \\ \cline{3-5}  & & 2.75 & 0.048492 & 0.9126/0.4927 \\ \cline{3-5}  & & 3.0 & 0.066796 & 0.8516/0.4622 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: ANFIS-PSO results.
## 3 Results and Discussion
### Study Limitations and Future Research Needs  
Numerical methods were employed to investigate possible crack initiation locations and crack propagation life in the cable-girder anchorage zone. Although the finite element model was validated against experimental investigation to prove its effectiveness concerning stresses, strains, and crack growth patterns, the fatigue life of the inclined crack initiated from the extra stiffener was not validated with experimental observation. Although the calculation of fatigue crack propagation life with the Paris equation is well established [2,11], comparing analytical results with two crack front positions observed at 2.6 million and 3 million loading cycles in Figure 12 would be interesting.  
Although model I crack is dominant for the cable-girder anchorage zone in the case study bridge, mixed mode cracks similar to the inclined crack observed in the fatigue test could occur under the combination of complex geometry details, stress concentration, welding residue stress, and fatigue load. A mixed mode consideration is undoubtedly more desirable to evaluate the potential fatigue cracking and brittle fracture failure risk of anchorage structures in a low ambient temperature environment.  
This study did not consider the details of welding residual stresses and their effect on the fatigue crack initiation and propagation simulation. Instead, while keeping the stress range the same, a higher stress ratio (\(R\) = 0.5) was assumed when obtaining material properties such as the fatigue crack growth rate. If the welding residual stress field is known from measurement or analysis, it should be superimposed to the stress caused by alternating fatigue load when appropriate.  
Our work demonstrates that XFEM can effectively simulate crack growth trends and significantly simplify crack modeling. However, SIF calculation for complicated structural details still needs to be improved regarding computation cost and accuracy. A tool capable of analyzing three-dimensional crack propagation, considering the coupling effect of multiple cracks, and modeling the dynamic extension of fatigue cracks needs to be developed.  
Figure 17: Sensitivity analysis of initial crack size and critical crack size: (**a**) Influence of initial crack size; (**b**) Influence of critical crack size.  
Finally, it should be pointed out that fatigue assessment in this paper was based on a deterministic approach. Nevertheless, factors affecting fatigue performance, such as material properties, residual stresses, loads, temperatures, and plate thicknesses, are random variables. The probabilistic fracture mechanics method is warranted to evaluate fatigue performance accurately.
# Virtual Reality or Videoconferencing for Online Learning? Evidence from Comparative Meta-Analyses
## 2 Literature Review
### Possible Moderator Variables
#### 2.3.3 VC Technological Features  
Camera option.The camera options for a VC platform can affect online learning. When the camera is turned on, teachers can assess students' understanding and emotions based on their facial expressions to provide immediate feedback [64]. This can enhance students' engagement and sense of community, which can lead to increased concentration and participation in class [65; 66]. However, it is important to consider potential issues when deciding whether to turn on the camera [67]. In situations where the internet connection is weak, turning off the camera can ensure a smoother online learning experience [68]. Privacy is also a crucial concern, especially in sensitive environments such as dormitories or shared living spaces. Turning off the camera can provide students with a sense of security and comfort.  
Platform.The platform used for the VC environment is a key moderating variable that affects learning outcomes. VC platforms differ in terms of interface design, functional features, and means of interaction. These factors may affect participants' engagement, communication effectiveness, and continuance intention [69]. Some VC platforms offer high-definition video communication [65], which allows remote students to see and hear their teachers' expressions and voices more clearly. Other platforms offer rich interactive features--such as instant polling, screen sharing, and screen annotation [70; 71]--that promote communication and collaboration among students and improve online learning efficiency and outcomes. This study therefore focused on the choice of VC platform and its potential impact on learning outcomes.
## 2 Materials and Methods
### Calibration Protocols  
#### 2.3.1 Optical Density Calibration  
A manual calibration procedure was initially established to characterize the Chi.Bio sensor and obtain specific digital output values across its operational digital range, interval of digital output values. We used the preliminary methodology described in [43]. For this purpose, an _E. coli_ culture was used with an initial OD\({}_{600}\) of 3.5 measured using Cytation 3. Serial dilutions were manually prepared in standard 30 mL flat-bottom vials (Fisher Scientific, Waltham, MA, USA, part number 11593532). Measurements were concurrently obtained using both Chi.Bio (650 nm LED) and Cytation 3 (monochromator-based), with the latter being a calibrated instrument that served as the reference standard. Chi.Bio provided digital output values (\(x\) in Equation (1)), while Cytation 3 yielded reference OD measurements. These data were used to formulate a mathematical model (Equation (1)) that relates the two sets of readings. It should be noted that phosphate-buffered saline (PBS) was used as a blank to normalize the digital output values by dividing them by the digital output value of the blank sample (details are outlined in Section S1.1 in the Supplementary Materials). Finally, the OD values of the Chi.Bio, OD\({}_{chi.Bio}\), where obtained by identifying the reference OD values obtained with Cytation 3 to the calibrated Chi.Bio ones using the nonlinear regression model:  
\[\text{OD}_{Chi.Bio}=\frac{ax^{n}}{x^{n}+k^{n}}, \tag{1}\]  
where \(x\) is the natural logarithm of the normalized digital output value of the sensor. In this model, \(a\) represents the saturation level of the sensor, \(k\) corresponds to the midpoint of its dynamic sensor range, and \(n\) determines how sharply the sensor response transitions from the linear to the saturated regime. All these parameters were estimated by fitting the model to the experimental data. This sigmoid model was used to compensate for the saturated response of the sensor at high optical densities. Additionally, the nonlinearity of the OD model helps capture the nonlinearity behavior given by the Beer-Lambert law at high concentrations [26].  
The OD\({}_{chi.Bio}\) values were further converted to particle counts using  
\[\text{Particles}=10^{p_{0}+2}(0.55\cdot\text{OD}_{chi.Bio})^{p_{1}}, \tag{2}\]  
with parameters \(p_{0}\) and \(p_{1}\) taken from prior characterization using silica beads [28]. Notice that this last step must be carried out for the spectrophotometer device used as base reference so as to obtain the corresponding parameters \(p_{0}\) and \(p_{1}\). In our case, these can be found in Table S1 (OD curve fitting parameters and the graph of the fitting curve) and in Figure S6 (fitting OD calibration curve (log-transformed) at 600 nm obtained from the Chi.Bio platform in the Supplementary Materials).
# On the Rollout of Network Slicing in Carrier Networks: A Technology Radar
## 3 Impact of Network Slicing
### TN Slicing
#### 3.3.2 Transport Technologies  
To convey slice traffic in the transport network, multiple forwarding plane technologies can be used. Depending on their isolation capabilities, these technologies can be clustered into two main categories: soft slicing (the traffic loading on one slide may degrade the performance of other slices, because of the use of statistical multiplexing and service classes) and hard slicing (the traffic loading on one slice has no impact on the traffic from any other slice, including QoS effects).  
The soft slicing category uses packet-based technologies to provide traffic-engineered and traffic-managed isolation of resources. This category encompasses Layer 2 and Layer 3 technologies, including tunnelling (e.g., VxLAN, MPLS) and virtualization (e.g., VPN, VLAN) based technologies. The mutual impact of QoS of slices sharing the same infrastructure resources may be mitigated by traffic engineering including, for example, limiting the statistical multiplexing ratio, or traffic policing on each network slice.  
Hard slicing can be guaranteed through independent circuit switched connections (e.g., dedicated wavelength, dedicated TDM time slot) for the exclusive use of a single network slice. Unlike soft slicing, this category is thought of as being implemented at Layer 1, using  
Figure 7: On the use of logical transport interfaces for TN slicing support. (**A**) Traffic segregation and mapping to S-NSSAIs; the BR is also referred to as Provider Edge (PE) router. (**B**) Schema for the EP_Transport IOC.  
techniques much more closely coupled to the hardware itself, such as optical transport network switching [34] or novel Ethernet-based solutions like Flex-Ethernet [35; 36].  
Depending on the customer requirements, the operator may go for soft slicing or hard slicing, or a mixture of the two. Indeed, it is possible to combine them as shown in Figure 8, with hard slicing ensuring a dedicated capacity chunk for the customer, and soft slicing providing traffic segregation among the services belonging to this customer. This approach preserves a cost-efficient solution to the customer that has multiple services, and wants them not to be impacted with traffic congestion or faults issued by services from other customers.
## 3 Methodology
### Problem Formulation  
Let us define trajectory prediction as a sequential forecasting problem. Given an observed trajectory sequence \(X=\{x_{1},x_{2},...,x_{T}\}\) where each point \(x_{t}\in\mathbb{R}^{5}\) represents the state of the aircraft at time \(t\) including 3D position coordinates (x, y, z) and 2D wind components (windx, windy), our objective is to predict the future trajectory \(Y=\{y_{T+1},y_{T+2},...,y_{T+H}\}\) for a horizon \(H\), where each \(y_{t}\in\mathbb{R}^{3}\) represents the predicted 3D position of the aircraft.  
Unlike deterministic approaches that output a single trajectory, our model produces a probability distribution over possible future trajectories, represented as:  
\[p(Y|X)=\prod_{t=T+1}^{T+H}p(y_{t}|X,y_{T+1},...,y_{t-1}) \tag{1}\]This formulation explicitly recognizes the inherent uncertainty in trajectory prediction, providing both expected future positions and their associated confidence levels. This probabilistic view addresses a fundamental limitation of deterministic approaches, which fail to capture the range of possible trajectories that might emerge from complex interactions in non-towered airspace.
## 2 Materials and Methods
### Sapwood Area Measurement  
DBH at breast height (1.3 m aboveground) was measured on 42 trees spanning the range of tree sizes (14-23 cm DBH) present at the study site, and tree cores were collected from individual trees to determine the sapwood thickness of white birch. The boundary between sapwood and heartwood was identified by color differences. Sapwood thickness was obtained by measuring the length of the sapwood, and sapwood thickness was then converted to sapwood area (A\({}_{\rm s}\), cm\({}^{2}\)). A\({}_{\rm s}\) was calculated from stem diameter, sapwood thickness, and mean bark thickness. The relationship between DBH and A\({}_{\rm s}\) was calculated with a power function (1):  
\[\rm A_{s}=0.2 DBH^{2.2351},\ R^{2}=0.88 \tag{1}\]  
This relationship was used to estimate the A\({}_{\rm s}\) of sample trees across the site on the basis of DBH measurements, shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Sample Tree No.**} & **Tree Height** & **Diameter at Breast** & **Sapwood Thickness** & **Sapwood Area** \\  & & **(m)** & **Height (cm)** & **(cm)** & **(cm\({}^{2}\))** \\ \hline \multirow{4}{*}{2015, 2017} & 1 & 14.5 & 15 & 3.06 & 85.06 \\  & 2 & 13.6 & 17.6 & 3.67 & 121.58 \\  & 3 & 15 & 19.1 & 4.03 & 145.97 \\  & 4 & 15.2 & 21 & 4.49 & 180.44 \\ \hline \multirow{4}{*}{2018} & 5 & 11.3 & 14.1 & 2.85 & 74.07 \\  & 6 & 15.1 & 17.4 & 3.62 & 118.52 \\  & 7 & 14.5 & 19.9 & 4.22 & 159.99 \\  & 8 & 16.2 & 21.3 & 4.56 & 186.25 \\ \hline \multirow{4}{*}{2021} & 9 & 15.8 & 14.3 & 2.90 & 76.44 \\  & 10 & 13.2 & 15.5 & 3.17 & 91.53 \\ \cline{1-1}  & 11 & 14 & 20.9 & 4.46 & 178.52 \\ \cline{1-1}  & 12 & 15.6 & 22.7 & 4.90 & 214.73 \\ \hline \end{tabular} Note: The same four sample trees of different diameter classes were used for observations in 2015 and 2017, and another four sample trees of different diameter classes were reselected for observations in 2018 and 2021.  
\end{table}
Table 1: Characteristics of sample trees selected for the measurement of sap flow.
# Stability Assessment of Gob Side Entry at the Steeply Inclined Mining Face
## 3 Results
### Support Effect  
Analyze the stability of the surrounding rock by considering the roadway located 10 m ahead of the mining face. The distribution characteristics of displacement and plastic zones within the roadway are illustrated in Figure 10. Anchor cables are secured in the stable strata. The maximum displacement of the roadway roof measures 103 mm; compare with the left side displacing 30 mm and the right side 70 mm. The depth of the roof's tensile plastic zone is approximately 800 mm. The plastic area of the left side is smaller than that of  
Figure 9: Roadway support parameters (unit: mm).  
the right. The findings indicate that the implementation of anchor cable support effectively mitigates the deformation of the surrounding rock.  
The deformation of the surrounding rock in the roadway is shown in Figure 11. The data in the figures show the following:  
1. During the initial phase of 3308 track entry driving, the displacement variation is significant, exhibiting a rapid upward trajectory. The support resistance escalates with deformation. The roadway deformation stabilized after 70 days of support application, signifying that the surrounding rock stress attained equilibrium.
2. The change size of roadway displacement is as follows: roof > solid coal side > floor > pillar side. Subsequent to the stabilization of the surrounding rock deformation, the roof subsidence measures 120-130 mm. The floor heave measures 50-60 mm. The lateral displacement of the solid coal is approximately 70-80 mm. The pillar displacement measures 30-40 mm. The deformation of the two sections is asymmetric, although the displacement remains within the acceptable limit.
3. Following the excavation of the roadway, the stability of the roof is augmented. Ten days prior to the roadway excavation, the expansion rate of the separation layers is considerably high. As monitoring time increases, the internal deformation rate of the surrounding rock progressively diminishes. The roof crack's maximum width is around 12 mm. The monitoring results indicate that the support strategy can significantly diminish layer separation.  
Figure 11: \(Cont.\)  
Figure 10: Roadway deformation analysis. (**a**) Displacement; (**b**) plasticity state.
## 2 Overview and Modeling of the AUH
### Modeling of AUH  
In order to establish the simple dynamic and kinematic models of the AUH, the body-fixed coordinate system and the inertial coordinate system are defined. Figure 5 exhibits the coordinate system of the AUH mentioned in this paper.  
In Figure 5, the absolute linear position in the inertial coordinate system is \(\xi=[x,y,z]^{T}\), and the angular position is \(\eta=[\phi,\theta,\varphi]^{T}\). In the body-fixed frame, the origin is deemed as the center of mass of the AUH. Moreover, in the ontological coordinate system, the linear velocity is defined as \(\mathbf{V}=[u,v,w]^{T}\), and the angular velocity is \(\mathbf{v}=[p,q,r]^{T}\).  
As the depth and heading control of the AUH at low speeds are mainly considered in this paper, the simplified kinematic and dynamic models are as follows [7].  
\[\left\{\begin{array}{l}\dot{X}=u\cos\varphi-v\sin\varphi\\ \dot{Y}=u\sin\varphi+v\cos\varphi\\ \dot{Z}=w\\ \dot{\Psi}=r\end{array}\right. \tag{1}\]  
\[\left\{\begin{array}{l}\ddot{w}=-\frac{Z_{w}}{m+Z_{w}}w-\frac{Z_{|w|}}{m+Z_{ \dot{w}}}|w|w+\frac{1}{m+Z_{\dot{w}}}(\tau_{Z}+\tau_{env})+\frac{W-B}{m+Z_{ \dot{w}}}\\ \dot{\varphi}=r\\ \dot{r}=-\frac{N_{r}}{t_{z}+N_{r}}r-\frac{N_{|r|r}}{t_{z}+N_{r}}|r|r+\frac{1 }{t_{z}+N_{r}}(\tau_{N}+\tau_{env})+\frac{Y_{z}-X_{\dot{w}}}{t_{z}+N_{r}}uv \end{array}\right. \tag{2}\]  
where \(m\) refers to the mass of AUH; \(Z_{\dot{w}}\) and \(N_{r}\) represent the hydrodynamic-added mass and hydrodynamic-added mass moment of inertia, respectively. \(Z_{w}\) and \(N_{r}\) are the linear damping coefficients; \(Z_{|w|w}\) and \(N_{|r|r}\) are the second-order damping coefficients. \(I_{z}\) denotes the moment of inertia about the z-axis; \(\tau_{Z}\) and \(\tau_{N}\) are the thrust of the vertical propellers and the horizontal propellers, respectively; \(\tau_{env}\) refers to the environmental disturbing force. \(W\) represents gravity and \(B\) denotes buoyancy.
anti-interference effect. However, ADRC is initially in a nonlinear form and has numerous parameters, making parameter setting and implementation challenging in engineering.  
LADRC [24] is an extension of ADRC, which uses a linear extended state observer (LESO) to estimate the state and disturbance of the system. It has a simpler structure than ADRC and is easier to analyze and set parameters.
## 3 Results and Discussion
### Level Analysis of Influencing Factors of SSCFC's Fluidity and Compressive Strength
#### 3.4.1 Effect of W/C Ratio on Fluidity and Compressive Strength of SSCFC  
Figure 8a elucidates the evolution of SSCFC's fluidity with the W/C ratio. Experimental data show a 14.3% fluidity increase when the W/C ratio rises from 0.4 to 0.45, followed by a marginal 0.9% improvement at a W/C ratio = 0.5. This trend aligns with the literature [54] on air-void content and viscosity synergism in cementitious rheology. A higher W/C ratio reduces the solid-phase concentration, weakening water adsorption and decreasing viscosity. Water's dual role emerges: (1) as a dispersant reducing interparticle friction [55], and (2) enhancing foam stability through bubble wall toughening. Notably, bubbles act as gas phase components and bearing-like entities, reducing SS particle contacts and optimizing rheology [56; 57]. As shown in Figure 9, individual bubbles in SSCFC slurry experience foam friction (Ff), foam confinement (Fc), foam buoyancy (Fb), foam drainage (Fd), foam surface tension (Fst), and foam internal pressure (Fip) [58]. Foam stability  
Figure 8: Graph of SSCFC performance: Fluidity (a), 7d Compressive stress (b), 14d Compressive stress (c), 28d Compressive stress (d) against factor levels.  
requires equilibrium among these six forces. Additionally, foam surface tension's capillary action anchors ceramsite particles, suppressing buoyancy and enhancing homogeneity.  
Figure 8b-d demonstrate the W/C ratio's effects on SSCFC's compressive strength at 7d/14d/28d. Experimental data reveal consistent W/C ratio impact patterns across curing ages, leading to the 28d strength being prioritized for engineering analysis. The compressive strength shows two-phase decay with W/C ratio increases (0.4\(\rightarrow\)0.5): 7% reduction at 0.4-0.45 versus 39.7% at 0.45-0.5. Mechanistically, an increased W/C ratio reduces the paste viscosity, compromising foam stabilization and promoting bubble coalescence during flow. Excess free water evaporation creates capillary pores, significantly increasing the specimen's porosity. High porosity prevents dense matrix formation, causing strength deterioration [59; 60; 61]. A W/C ratio above 0.45 triggers pore network percolation, obstructing stress transfer and inducing critical interfacial stress concentration. Therefore, W/C ratio elevation improves fluidity but requires careful design to prevent mechanical property degradation.