esa-sceva/satcom-chunk-collection huggingface dataset:
# In Vitro Simulation of Human Colonic Fermentation:
## 4 Available Colonic In Vitro Fermentation Models  
As anticipated, studying colonic fermentation in vivo entails several restrictions. Thus, colonic in vitro fermentation models can be a valid and worthwhile alternative and are increasingly used in the food research field. These models emerge with methodological advantages since they allow the possibility of sampling at any point of the process, assessing digestion of any food or nutrient and establishing its effect on microbiota profile, or determining the products generated by the microbiota metabolism [7; 11]. On the other hand, the limitations related to in vitro models is that they do not take into consideration the contribution of the host in digestion, and thus, they could be inherently incomplete.  
For the simulation of colonic fermentation in vitro, four basic elements are required: a microbial inoculum, a culture medium, a study substrate, and a bioreactor. Depending on the complexity of the conditions of the simulation or the inoculum, different types of models are currently available and are described hereafter.
## 2 Study Area and Methods
### Methods
#### 2.3.1 [PERSON] et al. (2019) discovered that the eigenspace among TS and _NVDI_ had a series of soil moisture contours and that the slopes between the two were negatively correlated with the crop moisture index. This depends on the study of triangular distribution characteristics of scatter graphs of _NDVI_ and Ts (26; 27). In order to represent the severity of the drought, _TVDI_ was created utilizing this common connection. Below is the formula:  
\[TVDI=(P_{S}-P_{\text{Smin}})/(P_{\text{Smax}}-P_{\text{Smin}}) \tag{1}\]  
Figure 3: The technical flowchart.  
where \(P_{Smin}\) is the smallest surface temperature at the equivalent _NDVI_ value for the wet side, \(P_{Smax}\) is the greatest surface temperature at the equivalent _NDVI_ value for the dry side, and PS is the recorded surface temperature in each pixel. Formula relating to wet and dry sides are obtained by linearly fitting all image elements with the same _NDVI_ values and corresponding LST maxima and minima:  
\[\begin{split} P_{Smax}&=d_{1}+g_{1}\times NDVI\\ P_{Smin}&=d_{2}+g_{2}\times NDVI\end{split} \tag{2}\]  
where \(\lambda\) indicates the _TVDI_ change tendency, \(Y_{m}\), \(Y_{n}\) is the corresponding _TVDI_ value for \(\text{m},\text{n}\) year, \(\text{m},\text{n}\) indicates the duration order, and median is a median function. _TVDI_ exhibits a growing trend when \(\lambda>0\) and a falling trend otherwise and is used in conjunction with Mann-Kendall to assess the trend's significance; the significance level \(\alpha\) is taken as 0.05 and 0.01, respectively. Table 1 displays the trend's precise division.  
For TVDI, drought grade classification adheres to [28] classification requirements for TVDI, which are provided in Table 2.
## 3 Results and Discussion
### Regression Equations  
Table 9 shows the calculated coefficients of the regression equations for the prototypes of fiber-reinforced concrete.  
Based on the results of studies by the least squares method, the basic regression equations were obtained, which are presented in the form of polynomials of the 2 nd degree:  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**The Title of the Output Parameter of**} & \multicolumn{2}{c}{**Equation**} & \multicolumn{5}{c}{**Equation Coefficients**} \\ \multicolumn{2}{c}{**the Equation**} & \multicolumn{1}{c}{**Number**} & \multicolumn{1}{c}{\(\mathbf{R_{0}}\)} & \multicolumn{1}{c}{\(\mathbf{k}\)} & \multicolumn{1}{c}{\(\mathbf{\mu}\)} & \multicolumn{1}{c}{\(\mathbf{\mu}\)} & \multicolumn{1}{c}{\(\mathbf{k}\)} & \multicolumn{1}{c}{\(\mathbf{k}^{2}\)} & \multicolumn{1}{c}{\(\mathbf{\mu}\)\({}^{2}\)} \\ \hline \multirow{3}{*}{\(\mathrm{R}_{c}\)} & 1 & 52.57 & \(-0.1241\) & 0.2649 & \(-0.5250\) & 2.0421 & \(-4.0579\) \\  & Compressive strength, MPa & 2 & 56.92 & \(-0.2389\) & 0.3408 & \(-1.2000\) & \(-0.9252\) & \(-0.3752\) \\  & 3 & 56.80 & \(-0.0574\) & 0.2167 & 1.4750 & 4.2415 & \(-0.0585\) \\ \hline \multirow{3}{*}{\(\mathrm{R}_{\mathrm{phen}}\)} & 1 & 19.70 & 0.6668 & 0.1908 & 0.4250 & 5.1970 & \(-13.1530\) \\  & Tensile strength in bending & 2 & 17.21 & \(-0.0908\) & 0.1704 & \(-0.9750\) & \(-0.1859\) & \(-0.6359\) \\  & test. MPa & 3 & 16.96 & 0.0259 & 0.1445 & 0.0500 & 1.5308 & \(-1.0692\) \\ \hline \multirow{3}{*}{\(\mathrm{\varepsilon}_{\mathrm{bR}}\)} & Ultimate deformations & 1 & 1.36 & 0.0031 & \(-0.0109\) & \(-0.0150\) & 0.1781 & \(-0.0519\) \\  & during axial compression. & 2 & 1.29 & 0.0035 & \(-0.0132\) & 0.0400 & 0.0881 & 0.0481 \\  & mm/m *10\({}^{-3}\) & 3 & 1.29 & \(-0.0007\) & \(-0.0087\) & \(-0.0075\) & \(-0.0902\) & 0.0448 \\ \hline \multirow{3}{*}{\(\mathrm{\varepsilon}_{\mathrm{bR}}\)} & Ultimate deformations & 1 & 0.71 & 0.0037 & \(-0.0087\) & \(-0.0025\) & 0.1132 & \(-0.0118\) \\  & during axial tension. & 2 & 0.64 & 0.0033 & \(-0.0078\) & 0.0250 & 0.0232 & 0.0132 \\  & mm/m *10\({}^{-4}\) & 3 & 0.65 & \(-0.0004\) & \(-0.0063\) & \(-0.0025\) & \(-0.0934\) & 0.0466 \\ \hline \multirow{3}{*}{\(\mathrm{E}_{\mathrm{fc}}\)} & 1 & 42.99 & \(-0.1648\) & 0.3315 & 0.0250 & \(-2.4231\) & \(-1.2231\) \\  & Elastic modulus. MPa *10\({}^{3}\) & 2 & 44.38 & \(-0.0889\) & 0.2723 & \(-1.1250\) & \(-1.2400\) & \(-1.1900\) \\ \cline{1-1}  & 3 & 44.07 & 0.0278 & 0.3075 & 0.3000 & 1.9434 & \(-1.7066\) \\ \hline \hline \end{tabular}
\end{table}
Table 9: Calculated coefficients of regression equations for prototypes of fiber-reinforced concrete.  
\[E_{fc3}(\mathbf{k},\mu)=44.07+0.0278\cdot\mathbf{k}+0.3075\mu+0.3\cdot\mu\cdot \mathbf{k}+1.9434\cdot\mathbf{k}^{2}-1.7066\cdot\mu^{2} \tag{17}\]
# Tag-to-Tag Interference Suppression Technique Based on Time Division for RFID
## 4 Anti-Collision for RFID Based on TD
### Data Architecture for RFID Anti-Collision Based on TD  
In this subsection, we propose an efficient data architecture that is based on TD to prevent RFID collisions. Here, we allocate specific time slots in the RFID data frame, and a fixed number of data bits for the preamble data and the payload. Within a particular time interval, the reader recognizes data bits from a particular tag, which consists of preamble data followed by the payload. In order to prevent collisions between tags in the same reader interrogation zone, a specific time interval is reserved for the specific tags.  
The functions of the preamble and payload are summarized as follows:
## 1 Introduction
Their probabilistic approach to analyzing various parameters (such as rotating speed and aspect ratio), and their well-established model, accurately predict the dynamic behaviors of the rotating system. This has significant guiding value for the optimization of materials and geometric sizes in rotor design. [PERSON] et al. (2019) studied vibration in a four-disk flexible hollow shaft rotor system under disk unbalance and bearing pulse excitation. They developed a simple finite element model using Lagrangian methods and Timoshenko beam theory, verified against 3D FEM. Their analysis of disk position effects on vibration sensitivity and the impact of in-shaft damping provides valuable insights for aero-engine rotor design and vibration control. The Monte Carlo method used in the article offers a robust way to account for uncertainties in rotor parameters, providing a probabilistic view of rotor behavior. This is particularly useful for understanding the range of possible outcomes in complex rotor systems. The machine learning technique offers a more general approach for non-linear systems, potentially applicable to a wider range of machinery but requiring a substantial amount of training data, prolonging the analysis process ([PERSON] _et al._, 2019). Modal analysis and wavelet transform methods provide specific insights into rotor behavior, with modal analysis offering real-time capabilities and wavelet transforms, in conjunction with FFT, excelling at detecting transient phenomena. The main aim of this work is to quantify and compare the differences in critical speeds and orbital patterns between the two rotor configurations. By doing so, we provide insights that can inform more robust design practices for rotating machinery, particularly in applications where off-center or variable mass distributions are unavoidable. Our findings highlight the significant impact of disk position on rotor behavior, demonstrating how off-center placement can lead to more complex orbital patterns and shifted critical speeds. These results have important implications for the design and operation of rotating machinery, particularly in applications where the precise control of rotor dynamics is crucial for performance and safety [14]. The choice between the methods mentioned above would depend on the specific application and the available resources. If a comprehensive analysis of multiple and combined faults is required, the approach by [PERSON] et al. [7] might be more suitable. However, if computational resources are limited, or the focus is on vibration-based fault detection, the method presented in this article could be a viable alternative.
## 3 Explanatory Application of the Method
### Monitoring Strategy 2 (Noticeable Event)  
Strategy 2 followed the same steps of Strategy 1, apart from the choice of the warning cluster. In fact, in this case, a recorded noticeable event was chosen as the reference behavior, and the cluster which it belonged to was referred to as \"warning\". As an example, the event shown in Figure 9 characterized by a comparatively high displacement in a short time was taken as a noticeable behavior (the representative early-stage pattern is shown in red). The 216 th week was assumed as the monitoring instant. In total, we found 11 warnings over 411,581 target points. Figure 10 displays the 11 warnings (black) and the noticeable behavior (blue). In red are plotted the monitoring windows, while the monitoring instant  
Figure 8: **(a)** Time diagram with the window (in red) belonging to the reference cluster; (b) time diagram of the warning point found though monitoring Strategy 1 (monitoring window in red); (c) position of the two points in the map.  
(216) is highlighted by a green vertical line. Figure 10 shows that all the points belonging to the warning cluster exhibited an anomalous behavior after the monitoring instant. This shows the ability of the proposed method to detect likely critical events at their early stage. The 11 warning points are shown in the map of Figure 11. It is to be stressed again that a more in-depth study of the geological nature of the detected phenomena is beyond the purpose of the present study.  
Figure 10: Noticeable behavior (diagram at the top left, see Figure 8) and corresponding warnings (in black) at the monitoring instant (216 th week; green line). The monitoring windows are displayed in red.  
Figure 9: Noticeable behavior (early stage in red).
## 2 SPARC: Metabolites and Homeostasis
At the molecular level, studies indicated how ECM (or its remodeling) regulates lipid metabolism [42] and glucose metabolism [43]. Moreover, the ECM-associated protein SPARC has been shown to be implicated in the regulation of the glucose transporter type 4 expression (controlling the glucose uptake) [44]. All these elements show how targeting the ECM or its associated proteins could impact the energy balance and the metabolic paths. Fortunately, cellular and animal models, including those of the knock-out of _Sparc_, shed light on the roles the ECM and its associated proteins (such as collagen)  
Figure 2: Secreted protein acidic and rich in cysteine (SPARC) metabolic and homeostatic implications. SPARC is involved in a variety of metabolic as well as homeostatic processes in diverse tissues, which suggest that SPARC simultaneously interacts with metabolism and homeostasis in order to synchronize with both of them. have within different contexts [45; 46; 47; 48]. These data will contribute to further mapping the metabolic puzzle related to the ECM and its associated proteins. More importantly, the implications of SPARC and the ECM in both metabolism and functions like growth, differentiation, and morphogenesis could indicate that the ECM and SPARC might synchronize diverse cellular functions so that the metabolic needs meet the biological changes the tissues undergo (Figures 2 and 3). This means that the ECM remodeling and SPARC expression would simultaneously impact parameters such as cell growth and differentiation as well as metabolic and other homeostatic patterns. This would result in a cellular profile adapted to the changes that tissues undergo in terms of energy usage and storage and homeostatic needs. Moreover, the implications of the ECM [46] or SPARC [49; 50; 51; 52] in other nonstructural functions such as inflammation, immunity, and cell growth would not only emphasize the importance of uncovering more pathways that ECM and SPARC govern but would also strengthen knowledge of metabolites, especially of links that have been established between cytokine (involved in inflammation and immunity) and metabolism [53; 54; 55]. Herein, cancer would be an illustrative example of such a \"cell development-metabolism synchronization\". Indeed, whereas cancer represents a status in which ECM remodeling is required and observed [56; 57; 58], cancer cells and the tumor microenvironment have specific metabolic profiles and unique bioenergetic properties in different form noncancer cells [59; 60]. Interestingly, SPARC was suggested to play a homeostasis-regulatory role in cancer. More precisely, while SPARC is overexpressed during cancer development, it inhibits tumor growth without inducing apoptosis in normal cells (selective inhibition), indicating a possible feedback effect towards a homeostatic regulation of cell growth in the context of cancer [61]. This regulatory role does not seem to be limited to development but would also control the metabolic profile of the tumoral cells in order to reach a balance/synchronization of both the structural changes and the metabolic needs.
# Effect of Adding Waste Polyethylene and GGBFS on the Engineering Properties of Cement Mortar
## 3 Results and Discussions
### Flow  
As shown in Table 3 and Figure 4, the flows of cement mortar with different added volumes of waste PE were 19.9 cm, 19.7 cm, 19.4 cm, 18.9 cm, and 18.7 cm. When the added volume was increased from 1% to 4%, the flow decreased by 1.01% to 6.03%, which indicated that the flow decreased as the added volume of waste PE increased. When the W/B was 0.5, 2% waste PE was added with 0%, 10%, and 20% GGBFS to replace cement, and the flows were 17.8 cm, 18.2 cm, and 19.4 cm, respectively. As demonstrated, with the increase in the replaced volume of GGBFS, the flow of cement mortar increased. Due to GGBFS's fineness and its particle shape, a lower relative density increases workability. When the replaced volume of GGBFS increased, the flow also increased.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Test Items** & \multicolumn{2}{c}{**Test Specifications**} \\ \hline
1. & Slump & ASTM C143 \\
2. & Flow & ASTM C230 \\
3. & Setting time & ASTM C403 \\
4. & Compressive strength & ASTM C109 \\
5. & Flexural strength & ASTM C348 \\
6. & Tensile strength & ASTM C190 \\
7. & Water absorption rate & ASTM C1585 \\
8. & Ultrasonic velocity & ASTM C597 \\
9. & Resistivity & ASTM C876 \\
10. & Resistance to sulfate attack & ASTM C1012 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The test methods and specifications.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**GGBFS** & **PE** & **Slump** & **Flow** & **Initial Setting** & **Final Setting** \\ \hline
**RM (\%)** & **AM (\%)** & **(cm)** & **(cm)** & **(min)** & **(min)** \\ \hline \multirow{6}{*}{0} & 0 & 2.2 & 18.2 & 317 & 428 \\ \cline{2-6}  & 1 & 1.9 & 17.9 & 304 & 416 \\ \cline{2-6}  & 2 & 1.8 & 17.8 & 291 & 407 \\ \cline{2-6}  & 3 & 1.6 & 17.7 & 278 & 392 \\ \cline{2-6}  & 4 & 1.5 & 17.4 & 269 & 379 \\ \hline \multirow{6}{*}{10} & 0 & 2.5 & 18.9 & 368 & 496 \\ \cline{2-6}  & 1 & 2.4 & 18.6 & 360 & 488 \\ \cline{2-6}  & 2 & 2.2 & 18.2 & 346 & 473 \\ \cline{2-6}  & 3 & 2 & 17.8 & 333 & 462 \\ \cline{2-6}  & 4 & 1.9 & 17.4 & 318 & 447 \\ \hline \multirow{6}{*}{20} & 0 & 2.8 & 19.9 & 401 & 518 \\ \cline{2-6}  & 1 & 2.6 & 19.7 & 387 & 505 \\ \cline{2-6}  & 2 & 2.5 & 19.4 & 375 & 496 \\ \cline{2-6}  & 3 & 2.3 & 18.9 & 367 & 488 \\ \cline{2-6}  & 4 & 2.1 & 18.7 & 356 & 474 \\ \hline \end{tabular}
\end{table}
Table 4: Fresh properties of cement mortar with the addition of different ratios of waste PE and GGBFS replacement.  
Figure 3: The slump of cement mortars with the addition of different ratios of waste PE and GGBFS replacement.
## 4 Example
### Constructing the Knowledge Graph  
Based on the telemetry engineering parameter table, anomaly event reports, anomaly handling plans, and manual monitoring experience of a certain type of domestic spacecraft Control Moment Gyroscope (CMG) system, we constructed a knowledge graph. The CMG, as a critical spacecraft electromechanical device, plays a crucial role in the stable control of the spacecraft. Using 42 months of historical telemetry data of the CMG system in orbit, this study carried out feature data extraction and effectively integrated it with the knowledge graph. To ensure data security, all data have been encrypted.
# Suppression Effect of Waterborne Polymer on Soil Used for Backfilling at Construction Site
## 3 Results
### Interaction between Polymers and Soil for Backfilling
#### 3.1.2 Zeta Potential of the Stabilized Backfill Powder  
The interaction between particles drives the particle size to increase, mainly via capillary and chemical cementing forces [29], both of which are related to Zeta potential. The influence of the mass ratio of hydrophilic monomers MMA/AA on the Zeta potential of soil dust was studied at a constant monomer dosage. As shown in Figure 2b, the Zeta potential of the unstabilized soil for backfilling in a neutral aqueous medium was \(-10.5\) mV, and it increased after stabilization.  
The carboxyl group content of the polymers decreased with the mass ratio of MMA/AA. At a certain chemisorption capacity of soil dust, the higher the MMA/AA mass ratio, the greater the adsorption capacity, causing a slow increase in the negative Zeta potential value to \(-46.9\) mV when the MMA/AA mass ratio was 3. However, as the MMA/AA mass ratio further increased, the carboxyl group content was further reduced, and the adsorption capacity and the thickness of the hydrated layer decreased, while the Zeta potential decreased instead. This indicates that the carboxyl groups of polymers were cross-linked with Ca\({}^{2+}\), Mg\({}^{2+}\), and Fe\({}^{3+}\) ions on the surface of the soil dust particles, promoting the chemisorption of polymers on the surface of the particles and improving the dispersion of the soil dust particles. Therefore, a polymer with an AA content of 2.0% and an MMA/AA mass ratio of 3 was selected for the following study.
# Audio-Visual Speech Recognition Based on Dual Cross-Modality Attentions with the Transformer Model
## 4 Experimental Results and Discussions
### Datasets  
We used LRS datasets, the largest existing public AVSR datasets [20]. Unfortunately, due to the license restriction of MV-LRS [40], we used only LRS2-BBC and LRS3-TED datasets for training, validation, and testing. To improve robustness in noisy environments, we simulated noisy reverberant signals with the signal-to-noise ratios (SNRs) of \(-5\) to \(20\) dB and the reverberation time (RT\({}_{60}\)) of \(0.4\) s by adding babble noise samples acquired at cafeterias and restaurants and imposing reverberation generated by the image method [41] to clean speech signals from the datasets. The added noise samples were different for training and testing.
## 4 Discussion  
As increasing threats continue to degrade coastal habitats around the world, governments and conservationists greatly benefit from more accurate maps that can strategically guide decision making, such as adopting new policies, expanding protected areas, increasing resilience, and restoring habitats at broad scales [66]. For years, many countries and territories across the Caribbean have relied on coarser global-scale marine datasets to inform conservation and management decisions, which are often not appropriate for small-island scale planning. These regional-scale benthic maps fill a data void and provide the first seamless and consistently mapped high-resolution (4 m) spatial database of benthic habitats for the shallow waters of the Insular Caribbean, a place identified as a high global biodiversity area and priority for coral reef protection [67]. This baseline will be fundamental to shaping policies on the sustainable use and protection of these \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & \begin{tabular}{c} **Boulders** \\ **and Rocks** \\ \end{tabular} & **Coral/Algae** & \begin{tabular}{c} **Hardbottom** \\ **Algae** \\ \end{tabular} & \
\begin{tabular}{c} **Muddy** \\ **Bottom** \\ \end{tabular} & **Sand** & **Seagrass** &
\begin{tabular}{c} **User’s** \\ **Accuracy** \\ \end{tabular} \\ \hline
**Boulders and Rocks** & **0.04** & 0.01 & 0.00 & 0.00 & 0.00 & 0.00 & 85.7\% \\ \hline
**Coral/Algae** & 0.02 & **5.75** & 1.46 & 0.00 & 0.16 & 0.44 & 73.5\% \\ \hline
**Hardbottom Algae** & 0.16 & 2.43 & **32.69** & 0.00 & 2.35 & 4.62 & 77.4\% \\ \hline
**Muddy Bottom** & 0.00 & 0.00 & 0.00 & **0.65** & 0.00 & 0.09 & 87.5\% \\ \hline
**Sand** & 0.17 & 1.62 & 0.87 & 0.29 & **26.94** & 1.79 & 85.0\% \\ \hline
**Seagrass** & 0.05 & 1.08 & 1.35 & 0.24 & 0.53 & **14.19** & 81.4\% \\ \hline
**Producer’s Accuracy** & 9.2\% & 52.8\% & 90.0\% & 55.3\% & 89.9\% & 67.1\% \\ \hline \end{tabular}
\end{table} 
Table 10: Accuracy assessment results of the six-class major benthic habitat types after combining sparse and dense seagrass as well as sparse and dense hardbottom algae. The numbers in the diagonal cells (depicted in bold) represent the proportion of total area correctly classified for each benthic class. The reported overall accuracy for this analysis is 80% (not shown in the table). All numbers reported in each table represent the proportion of total area.