esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Investigation of a Novel Grinding Device
## 2 Materials and Methods
### Numerical Setup
From the relative velocity at the beginning of the contact (which has normal and tangential components \(v_{\text{c,n}}\) and \(v_{\text{c,t}}\)), it is possible to compute the impact kinetic energy \(k\epsilon_{\text{r}}\) and the impact angle \(\theta_{\text{impact}}\):  
\[k\epsilon_{\text{r}}=\frac{1}{2}\mathbf{v}_{\text{c}}^{\text{ T}}\cdot\mathbf{M}\cdot\mathbf{v}_{\text{c}}=\frac{1}{2}(v_{\text{c,t}}\;\;v_{\text{c,n}}) \cdot\left(\frac{1}{m_{i}}+\frac{1}{m_{j}}\right)^{-1} \begin{pmatrix}2/7&0\\ 0&1\end{pmatrix} \cdot \begin{pmatrix}v_{\text{c,t}}\\ v_{\text{c,n}}\end{pmatrix}  \tag{5}\] \[\theta_{\text{impact}}=\arctan\left(\frac{v_{\text{c,n}}}{v_{ \text{c,t}}}\right) \tag{6}\] \begin{table}
\begin{tabular}{c c c c c} \hline \hline  &
\begin{tabular}{c} **Zirconia,** \\ **ZrO\({}_{2}\)** \\ \end{tabular} & **Silver, Ag** & **Glass** & **PTFE** \\ \hline Density [kg/m\({}^{3}\)] & 6067 & 10490 & 2510 & 2200 \\ Young’s Modulus [GPa] & 210.0 & 82.0 & 70.0 & 0.50 \\ Poisson ratio & 0.31 & 0.36 & 0.24 & 0.46 \\ Coefficient of restitution & 0.92 & 0.80 & 0.99 & 0.80 \\ Coefficient of friction & 0.15 & 0.55 & 0.27 & 0.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Model parameters and material properties (see [27] for zirconia and glass, [28] for silver, and [29] for PTFE). Reprinted with permission from Ref. [24], 2023, AIDIC Servizi S.r.l. where \(M\) is the inertia matrix of the system of two spherical particles [30]. Such matrix is symmetric, positive definite, and contains the coefficient \(2/7\) connected to the gyration radius of spheres. The simulations are initialized by randomly placing the beads in the vessel and allowing them to settle for \(0.5\,\mathrm{s}\), reaching a pseudo-steady-state condition. The contact sampling is then performed in the following \(5\,\mathrm{s}\), corresponding to \(50\) complete revolutions of the impeller at \(600\,\mathrm{r}\mathrm{p}\mathrm{m}\). A customized procedure, by monitoring the changes in particle-particle and particle-wall contacts at every time step, allows the identification of new contacts (i.e., collisions) and detachments and consequently collision frequency and duration [31]. The numerical integration is performed with a time step of \(10^{-7}\,\mathrm{s}\), sufficiently smaller than the actual collision time (whose scale is given by Rayleigh and Hertz times) [25]. This value ensures the stability of the numerical simulations and a proper resolution of the impact dynamics. To ensure the statistical invariance of the insertion method, the results reported in the following section are obtained by averaging over \(6\) simulations with distinct random seeds, which determine the particle starting positioning in the computational domain. To implement and execute the code, the open-source software LIGGGHTS(r) 3.8.0 was used (further information can be found in [32]).
## 1 Introduction
### The Conceptual Framework of the Viewing Process
#### 1.3.2 Regional Associations  
When an observer's gaze shifts between different visual regions, specific associations are formed along the gaze path that constitute structured relationship networks rather than simple sequential movements. Moving beyond conventional scanpath analysis that merely tracks eye movement sequences, our approach quantifies the associative strength between visual regions to reveal the underlying organizational logic of visual exploration. These transitions are not random wanderings; in goal-directed (top-down) visual search, they are shaped by task goals, semantic content, and prior fixation history, resulting in structured and predictable patterns of movement, whereas in stimulus-driven (bottom-up) exploration, they are primarily guided by salient visual features without explicit cognitive control [42; 43; 41]. In the context of art appreciation, [PERSON] [44] observed that viewers may strategically alternate their gaze between key areas within a work, forming individually repeated and deliberate visual paths that reflect their unique goals, interpretations, and interactions with the artwork. [PERSON] and [PERSON] [45] used topological analysis of eye movement trajectories to reveal differences in individual visual exploration styles, while [PERSON] and [PERSON] [46] found that gaze patterns during art viewing systematically vary with levels of aesthetic engagement. Similarly, [PERSON] et al. [47] demonstrated that educational interventions, such as a photo creation course, can significantly influence students' viewingstrategies. Their study showed that post-intervention, students engaged in more active perceptual exploration and exhibited increased global saccades, indicating a shift toward more deliberate and informed visual analysis. Building on these findings, this study introduces \"relation\" as the second analytical dimension of structured viewing behavior, used to characterize the transitions and linkages between visual regions during the viewing process. This represents a fundamental shift from describing \"where\" viewers look to understanding \"how\" visual regions interconnect in cognitive space. By analyzing fixation movements across regions, this structural layer offers new possibilities for the computational modeling of interregional relationships that capture the relational architecture of visual attention.
## Materials and Methods
### Methods
#### 2.3.3 Structural Equation Modelling (SEM)  
SEM is an advanced multivariate statistical methodology that integrates factor analysis with path analysis, enabling a rigorous examination of an independent variable's direct, indirect, and overall impacts on a dependent variable [46]. To determine the best-fit model, the chi-square test, comparative fit index (CFI), standardised root mean square residual (SRMR), and root mean square error of approximation (RMSEA) were used as evaluation criteria. The model was considered to fit well when the chi-square > 0.05, CFI > 0.9, SRMR < 0.05, and RMSEA < 0.05.
## 4 Results and Discussion  
### Triboelectric Charging Behaviors of a PES Single Film  
Figure 3a shows typical frictional charging behaviors of the PES single film under various humidity conditions (relationship between the film surface potential, \(V_{\mathrm{s}}\), and measurement time, \(t\)). The \(V_{\mathrm{s}}\) showed typical behavior of increasing in the negative direction with increasing \(t\) and eventually becoming saturated. This behavior implies that the area of the contact point increases as the film is rubbed [35]. In addition, \(V_{\mathrm{s}}\) or the charge amount of the film increased in the negative direction with increasing humidity. This can be explained using the CWP model described in Section 2. The value of \(V_{\mathrm{s}}\) at 3600 s was defined as the saturation potential, \(V_{\mathrm{sat}}\), and a plot of \(V_{\mathrm{sat}}\) versus humidity is shown in Figure 3b. In accordance with the CWP model, \(V_{\mathrm{sat}}\) showed a tendency to shift negatively with increasing humidity. The straight line in the figure is a regression line calculated by the least-squares method using Origin 2022b software (OriginLab, Northampton, MA, USA), while the absolute value of its slope indicates the film sensitivity toward humidity. This slope will hereafter be referred to as the humidity dependence. Single regression analysis of the relationship between \(V_{\mathrm{sat}}\) and the humidity afforded a value of 0.82 \(\pm\) 0.25 V/%RH as the humidity dependence. The error range of 0.50 V/%RH was calculated as twice the standard error of the slope and corresponds to a 95% confidence interval (95% CI). The value of the 95% CI is a measure of the variability of the data relative to the regression line, whereby the larger the value, the greater the variability of the data is likely to be. Possible reasons for this variation in data can be the diversity of the surfaces (surface variability, variations due to contamination, roughness, sample history, fluctuation, etc.), as noted [PERSON] [36] and [PERSON] et al. [20], and the effect of charge back flow caused by electron tunneling or air breakdown during the separation of the two surfaces [20,37].
# Vertex-Weighted Consensus-Based Formation Control with Area Constraints and Collision Avoidance
## 3 Experimental Results  
The applicability of the proposed approach is validated through simulations and real-world experiments. The performance of the proposed approach is compared against edge-weighted formation control with virtual relabeling [6] in both simulations and real-world experiments. In this work, the compared formation control is called a conventional approach.  
In the simulation experiments, a network of six holonomic mobile robots was considered to achieve the formation patterns shown in Figure 1(a),b. Moreover, a network of four holonomic mobile robots was considered to achieve the formation pattern shown in Figure 1(c). For real-world experimentation, four non-holonomic Turtlebot3(r) Waffle Pi mobile robots were considered to achieve the formation pattern shown in Figure 1(c).  
Formation control for non-holonomic mobile robots is achieved by considering the use of coordinate transformations, as defined in (2). Instead of employing the conventional \(\mathbf{p}_{i}=[x_{i},y_{i}]^{T}\) coordinates, we adopt \(\mathbf{p}_{i}^{L}=[x_{i}^{L},y_{i}^{L}]^{T}\) and utilize the input transformation outlined in (4).  
The \(k_{ij}\) and \(Z_{ijk}^{*}\) values for the hexagonal, rectangular, and square formation patterns are given in Table 1, Table 2, and Table 3, respectively. The value of \(k_{ij}\) is tuned to define the distance between agents \(\mathbf{p}_{i}\) and \(\mathbf{p}_{j}\); indeed, \(k_{ij}\) define the formation patterns. Moreover, \(Z_{ijk}^{*}\) is a desired signed area, which can be computed with (9).  
With respect to formation control settings, the safety distance was set to \(\delta=0.2\), the inter-robot influence was fixed to \(\alpha=0.7\), and the signed area control gain for all triplets was fixed to \(K_{\mathcal{C}_{ijk}}=10\). These parameters were experimentally selected.  
For simplicity, the physical parameter \(L_{i}\) is set to 0.05 meters for all Turtlebot3(r) systems. Additionally, the linear and angular velocities \((v_{i},\omega_{i})\) are bounded to \(-0.2\leq v_{i}\leq 0.2\)and \(-0.9\leq\omega_{i}\leq 0.9\). This saturation is imposed to protect the equipment, but only for real-world implementations.
## 2 Results and Discussion
### Immobilized Enzyme Reusability with RNase B  
A substantial advantage of enzyme immobilization is the potential reusability of the catalyst. Amino-mediated immobilized Endo BI-1 was subjected to ten 20-min reuse cycles at 45 and 65 \({}^{\circ}\)C to determine the effects of different temperatures on the activity and reusability of the immobilized enzyme (Figure 6). Enzyme activity was higher at 65 \({}^{\circ}\)C compared with 45 \({}^{\circ}\)C. At 45 \({}^{\circ}\)C, the immobilized enzyme activity corresponded to approximately 65% of the enzyme activity at 65 \({}^{\circ}\)C. Despite higher enzyme activity at 65 \({}^{\circ}\)C, a higher decrease in the enzyme activity was observed after each reuse cycle. At 65 \({}^{\circ}\)C, enzyme activity was reduced to 63% of its original value after one cycle of reuse, and by the sixth use cycle, it had been reduced to and stabilized at 10 to 15% of its original activity. Conversely, at 45 \({}^{\circ}\)C, the enzyme activity remained consistent up to three reuse cycles. Despite the initial higher enzyme activity at 65 \({}^{\circ}\)C, from the third cycle onward reactions at 45 \({}^{\circ}\)C had greater residual enzyme activity than reactions performed at 65 \({}^{\circ}\)C. These results indicate that although 65 \({}^{\circ}\)C is optimal for initial glycan release, it does not favor repeated enzyme use for longer reaction times. It is possible to reuse the immobilized enzyme up to seven cycles at 45 \({}^{\circ}\)C with significant retention of its activity (50% from the initial activity). Our results are in agreement with several reports in the literature, where the use of lower temperatures (e.g., 37 \({}^{\circ}\)C) has favored high reusability, even though higher temperatures may favor more rapid catalysis [38; 39]. Regarding an industrially-relevant enzyme with macromolecular substrates, [PERSON] reported a loss  
Figure 5: Lineweaver Burk (1/[S] (mL/mg) vs. 1/\(v\) (mL \(\times\) min/mg)) (a) and Hanes–Wool ([S] (mg/mL) vs. \(s/r\) (min)) (b) linearized plotting techniques for the estimation of kinetic parameters of free and immobilized EndoBI-1 on RNase B.  
of over 85% of polygalacturonase activity after 6 cycles using adsorption onto celite [40]. Additionally, reaction time and temperature affect the selective release of \(N\)-glycans from more complex and diverse glycoproteins, which may determine the choice of the reaction temperature thus affecting reusability of the immobilized enzyme [41]. Importantly, fouling with a protein-based substrate may also impact reusability [42].
# Comparative Analysis of Marine Alternative Fuels for Offshore Supply Vessels
## 2 Methodology
### Environmental Impacts (Step 4)  
The LCA study examined fuel production and usage in ship propulsion systems through WTT and TTW aspects. Fuels were categorised as grey, blue, or green based on production methods. Propulsion systems were classified as mechanical, electric, fuel cell, or battery-powered. Mechanical systems mainly use diesel, emitting various pollutants. Electric propulsion is less harmful but still contributes to climate change. Fuel cells primarily use hydrogen, emitting only water and heat, but their impact depends on fuel production. Battery-powered systems do not emit pollutants, but their environmental impact depends on the electricity source used for charging.
# Semi-Autogenous Mill Power Consumption Prediction Based on CACN-LSTM
## 4 Experiments  
### Data Description  
The data acquisition system of the deep copper series SAG mill of a certain ore beneficiation plant in Yunnan is shown in Figure 6. Various operating parameters of the semi-autogenous mill are collected in real time through various sensors. The key operating parameters captured in the dataset used in this paper include ore feed rate (OFR), sand return rate (SRR), feed end pressure (FEP), discharge end pressure (DEP), grinding noise (GS), water feed rate (WFR), inlet side bearing temperature (ISBT), outlet side bearing temperature (OSBT), motor temperature (MT), and power consumption (PC).  
In this paper, real-time data from a SAG mill (CITIC Heavy Industries, Luoyang, China) of a deep copper series in a beneficiation plant in Yunnan, covering the period from 1 June 2023, at 1:00 AM to 14 December 2023, at 4:00 PM, are utilized for the experiments. Table 2 lists the statistical characteristics of the selected key parameters. The data acquisition system of this beneficiation plant records data at one-hour intervals, resulting in a total of 4610 samples over six months. For this paper, five months of data are employed as the training set, while the remaining month is used as the test set. The training set consists of 4281 samples, and the test set comprises 329 samples.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline
**Attribute** & **OFR** & **SRR** & **FEP** & **DEP** & **GS** & **WFR** & **ISBT** & **OSBT** & **MT** & **PC** \\ \hline count & 4610 & 4610 & 4610 & 4610 & 4610 & 4610 & 4610 & 4610 & 4610 & 4610 & 4610 \\ mean & 342.03 & 32.31 & 4.36 & 4.87 & 73.96 & 113.77 & 33.47 & 34.96 & 78.17 & 2755.39 \\ std & 23.29 & 8.86 & 0.09 & 0.10 & 6.47 & 14.10 & 0.19 & 0.18 & 1.75 & 331.14 \\ min & 270.5 & 8.5 & 4.10 & 4.59 & 41.05 & 81.05 & 32.9 & 34.4 & 74 & 1744.96 \\ max & 391.62 & 56.17 & 4.62 & 5.16 & 82.32 & 143.50 & 34.0 & 35.5 & 82 & 3412.20 \\ \hline \end{tabular}
\end{table}
Table 2: Statistical Characterization of Key Parameters of the SAG Mill Grinding Process.  
Figure 6: Mineral processing plant data acquisition system.
# Effect of Radial Height of Helical Static Blade on the Cavitation Performance of Inducer
## 3 Simulation Method
### Turbulence Model  
As a supplement to the governing equations, the calculation uses the standard \(k\)-\(\varepsilon\) turbulence model to solve fully turbulent flows. As for the near wall area, standard wall function is adopted. The standard \(k\)-\(\varepsilon\) model computes the turbulent viscosity \(\mu_{t}\) through the turbulence kinetic energy \(k\) and its rate of dissipation \(\varepsilon\) in the following equations:  
\[\mu_{t}=\rho C_{\mathrm{r}}\frac{k^{2}}{\varepsilon} \tag{3}\]  
\[\frac{\partial(\rho k)}{\partial t}+\frac{\partial(\rho ku_{i})}{\partial x_{ i}}=\frac{\partial}{\partial x_{j}}\Bigg{[}\left(\mu+\frac{\mu_{t}}{\sigma_{k}} \right)\frac{\partial k}{\partial x_{j}}\Bigg{]}+G_{k}-\rho\varepsilon \tag{4}\]  
\[\frac{\partial(\rho\varepsilon)}{\partial t}+\frac{\partial(\rho\varepsilon u _{i})}{\partial x_{i}}=\frac{\partial}{\partial x_{j}}\Bigg{[}\left(\mu+\frac {\mu_{t}}{\sigma_{k}}\right)\frac{\partial\varepsilon}{\partial x_{j}}\Bigg{]} +C_{1\varepsilon}\frac{\varepsilon}{K}G_{k}-C_{2\varepsilon}\rho\frac{ \varepsilon^{2}}{k} \tag{5}\]  
where \(G_{k}\) is the turbulent kinetic energy generation term, \(\sigma_{k}\) and \(\sigma_{\varepsilon}\) are the turbulent Prandtl numbers, and \(C_{\mu}\), \(C_{1\varepsilon}\), and \(C_{2\varepsilon}\) are constants, the values of which are 0.09, 1.44, and 1.92, respectively.
## 3 Results
### Bacterial Community Composition  
We analyzed the bacterial community composition at the phylum level across the six groups (Figure 2). The circlize plot represented the average relative abundance of each bacterial taxon. In all six groups, _Frimicutes_ was the dominant phylum, as 52.49%, 56.62%, 54.69%, 54.52%, 49.33%, and 44.51% in the control, A, B, C, D, and E groups, respectively. The secondly abundant phylum was _Bacteroidetes_ (33.18%, 30.71%, 32.31%, 35.39%, 32.63%, and 38.51%) in these six groups, respectively. In addition, the phyla _Chlamydine_, _SR1_ and _WPS-2_ were present only in the Control, A, and E groups, but absent in the B, C, and D groups. _OP1_ was scarcely found in the Control and A groups. _Fusobacteria_ was absent in group E, while _Deferibatecres_ and _Synergistetes_ were undetected in group B. Moreover, the biggest differences among the 6 groups of phyla were _Verrucomicrobia_ and _Spirochaete_.  
On the PCoA plot, each symbol represented the microbiota of a camel. Overall, no significant differences in community structure were observed between the six groups, except for Group E based on weight or unweight distance analysis (Figure 3a,b). Interestingly, the microbiome of the E group was distinct from the other five groups in which almost both samples were clustered together. In addition, the two camels of Group A (A1 and A2 in Figure 3) fed with a different diet showed dispersal pattern in the PCoA plot.  
Figure 1: Two alpha diversity measures Chao1 and Shannon’s diversity index. Community diversity represented by Chao1 and Shannon diversity index for samples from each group. Each dot stands for a discrete individual while each color represents (**a**) the samples from the camels infected with different parasites, (**b**) the samples from the camels with non-, single-, co-infection with parasites as the label showed.  
Figure 3: Principal Coordinate Analysis (PCoA) with weighted/unweighted UniFrac distance analysis. PCoA plot of **(a)** the weighted UniFrac distances, **(b)** the unweighted UniFrac distances of fecal microbiota from each Bactrian camel. Control means negative samples of parasites, Single means samples infected with one of _Cryptosporidiumum_ spp., _E. bienenesi._, and _G. duodenalis._, Co-infection means samples infected with more than one parasite. As the label shows, exactly infection and indiscriminate positive samples are differentiated by colors and shapes (circle, triangle, and square, respectively).  
Figure 2: Bacterial composition of different groups at the phylum level. The circlize plot shows the average relative abundance of each bacterial taxon (phylum level). There are 21 phyla for each group exhibited here, and _Firmicutes_ and _Bacteroidetes_ are the dominant phyla for each group.
## 2 Materials and Methods  
### Microwave Plasma Jet Set Up  
A low power solid state microwave power supply operating at frequency of 2.45 GHz (Sairem, GMS 200 W) was connected by a flexible coaxial cable to a commercial compact electromagnetic surface wave (SW) launcher (Sairem, SURFATRON 80). The discharge was created in a quartz capillary (real dielectric permittivity \(\varepsilon_{r}\) = 3.2541, imaginary dielectric permittivity \(\varepsilon_{i}\) = 0.0062, i.d. 3 mm, o.d. 8.0 mm) in argon (purity of 99.9965%) at a constant mass flow rate of 5 \(\mathrm{Slm}\), controlled by the Bronkhorst FG201-CV mass flow controller. The experimental set up is shown in Figure 1. The experiments were performed at 2 different powers (9 W, 12 W; maximal reflected power was below 0.5 W), thanks to which the surface temperature of the agar plates stayed within the temperature tolerance of the living tissues [3]. Based on our previous results obtained by the thermal camera, the temperature of the treated object did not exceed 40 \({}^{\circ}\mathrm{C}\) even after 30 s of the treatment [34], but higher temperatures at treatment times above 30 s were possible. The applied powers of 9 W and 12 W were selected based on our experience presented in the same study. The rotational temperature (indicating the real temperature of the plasma core) was obtained from the emission spectrum of the OH radical and it reached \(650\pm 50\) K at the end of the quartz capillary and decreased linearly to \(600\pm 50\) K on the treated surface [34]. The visible end of the discharge was directed perpendicularly to the treated area approximately at the center of the agar in the sterile petri dishes so that the visible end of the beam did not touch the surface. In the case of 9 W, the distance from the top of the inoculated culture was 5 mm while using 12 W; the discharge torch was longer so the distance between its visible end and the treated area was reduced to 3.5 mm. The distribution of active particles and calculated plasma parameters at similar operating conditions were described in detail in [34].