esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Some Additives on the Properties of OPC Solidified Sandy Silt
## 3 Results
### Indirect Tensile Strength  
The trend of the indirect tensile strength changes was similar to those of unconfined compression, as shown in Figure 6 and Table 5. All specimens showed an increasing rate up to the age of 60 days and then a constant rate of change. For gypsum, the effect of a different content on tensile strength was not similar to that in the early stage of curing. The strength of the control group was similar to that of the specimen with 2% gypsum in the early stage, while after 28 days, the strength of the latter was higher than in the control group. For example, the strength of SS2 increased by 28.26% compared to SS1 at the age of 90 days. Overall, the OPC and gypsum solidified sandy silt establishes higher indirect tensile strength than the others. For the clay particles, the indirect tensile strength of the specimens decreased slightly with various displacement ratios. It is noted that the strength of SS1 increased by 15.0%, 12.2%, and 7.0% compared to that of SN2, SN3, and SN4, respectively, after 90 days. For lime, the change of indirect tensile strength was not very different from the compressive strength, with the results indicating that lime is not an ideal reinforcement for solidified sandy silt, which even has negative effects. The inclusion  
Figure 4: Curve of unconfined compressive strength with different replacement ratios of the additives. of fly ash has little effect on the indirect tensile strength of the specimens at the early stage. For example, the increase in the strength of the specimens was 5.0% and 7.5% with the 3% and 4% addition of fly as compared to SS1 at 28 days. However, the increase reached 15.2% and 26.1% after 90 days. The results indicate that the fly ash activity is low in the early reaction stages, and the fly ash activity is exerted in the late stage when the OPC hydration forms alkaline environment [28]. Figure 7 also illustrates indirect tensile strength with the optimal content of each additive in order to complete the comparison. Overall, the unconfined compressive strength of the solidified sandy silt increased in the order of 2% lime < 4% clay particle < 4% fly ash < 2% gypsum for 90 days.
## 3 Results and Discussion
### Characteristics of Filter Residues
#### 3.6.4 Correlation Between XRD Analysis and Magnetic Properties of Filter Residues  
Figure 8 presents XRD patterns of samples under different treatment conditions after thermal processing at 100 \({}^{\circ}\)C and 650 \({}^{\circ}\)C. All samples exhibited characteristic diffraction peaks of Fe\({}_{3}\)O\({}_{4}\) including (220), (311), (440), and (422) crystal planes. However, significant  
Figure 7: Magnetic hysteresis loops of filter residues under different treatment conditions: (**a**,**b**) without PVA, (**c**,**d**) sample B1, and (**e**,**f**) sample B2 after drying at 100 \({}^{\circ}\)C and calcination at 650 \({}^{\circ}\)C, respectively. The measurements show distinct magnetic property evolution patterns and saturation magnetization values.  
differences in crystallinity and phase composition were observed under different treatment conditions. After 100 \({}^{\circ}\)C thermal treatment (Figure 8a), sample B2 demonstrated the most complete Fe\({}_{3}\)O\({}_{4}\) characteristic peaks, with clearly visible and intense diffraction peaks at 20 = 30.1\({}^{\circ}\) (220), 35.5\({}^{\circ}\) (311), and 62.6\({}^{\circ}\) (440), corresponding to its excellent magnetic performance (10.30 emu/g). In contrast, samples A1 and B1 showed relatively weak peak intensities despite displaying Fe\({}_{3}\)O\({}_{4}\) characteristic peaks, explaining their lower magnetization values (0.123 and 0.152 emu/g, respectively). Upon heating to 650 \({}^{\circ}\)C (Figure 8b), significant structural changes occurred. Fe\({}_{3}\)O\({}_{4}\) characteristic peaks became more distinct in samples A1 and B1, with B1 showing notably increased magnetization to 2.43 emu/g, consistent with its enhanced crystallinity. However, although sample B2 maintained good crystallinity after high-temperature treatment, its magnetization dramatically decreased to 0.679 emu/g. This anomalous phenomenon might result from partial transformation of Fe\({}_{3}\)O\({}_{4}\) to \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) at high temperatures, evidenced by the emergence of a new diffraction peak at 33.2\({}^{\circ}\). These findings reveal the complex influence of preparation conditions and thermal treatment temperatures on the final product's structure and magnetic properties.
## 3 Materials and Methods
### MS-SSIM Filtering  
Subsequently, to further refine the dataset, we utilized the Multi-Scale Structural Similarity Index (MS-SSIM) technique, setting a threshold of 0.5, as depicted in Figure 4. This approach helped in removing redundant images with similar features, thus ensuring a diverse and information-rich dataset that enhanced the model's learning capability. Lastly, this filtration step refined our dataset to 20,689 distinct images, which constituted our final high-quality dataset for the pig face recognition tasks. MS-SSIM is defined in Equation (2):  
\[l_{\text{ms-ssim}}=1-\prod_{m=1}^{M}\left(\frac{2\mu_{p}\mu_{g}+c_{1}}{\mu_{p }^{2}+\mu_{g}^{2}+c_{1}}\right)^{\beta_{m}}\left(\frac{2\sigma_{pg}+c_{2}}{ \sigma_{p}^{2}+\sigma_{g}^{2}+c_{2}}\right)^{7_{m}}, \tag{2}\]  
Figure 3: Sample image visualization from the pig face detection dataset.  
\(M\) refers to different scales. \(\mu_{p}\mu_{g}\) are the mean values of the predicted image and the ground-truth, respectively. \(\sigma_{p},\sigma_{g}\) represent the standard deviations between the predicted image and the ground-truth. \(\sigma_{pg}\) stands for the covariance between the predicted values and the ground-truth. \(\beta_{m},\gamma_{m}\) signify the relative importance between the two components. \(c_{1},c_{2}\) are constants used to prevent division by zero. In our study, we set 20 values of 6.5025 and 58.5225 for \(c_{1},c_{2}\), respectively.
## 3 Results and Discussion
### Air and CO\({}_{2}\) Reactivities
#### 3.3.2 Carbon Dioxide (CO\({}_{2}\)) Reactivity  
Carbon dioxide (CO\({}_{2}\)) is produced during the electrolysis due to the reaction of oxygen in alumina with the anode's carbon. The produced carbon dioxide infiltrates into the pores of the anode and reacts with the carbon, producing carbon monoxide (CO) [27]. This is called the Boudouard reaction (CO\({}_{2}\) + C \(\rightarrow\) 2 CO). This side reaction consumes carbon that otherwise could be used to produce aluminum [3; 27; 28; 29]. The CO\({}_{2}\) reactivities of the anodes are presented in Figure 5. The addition of biocoke and additives increases the CO\({}_{2}\) reactivity compared to that of the standard anode. This might be due to the lower densities (higher porosities) of the anodes containing biocoke, which permits the diffusion of CO\({}_{2}\) into the interior of the anode and/or possibly the impurities present in biocoke. Anodes containing biocoke modified with A(1) had the highest CO\({}_{2}\) reactivity (i.e., B3A(1)3). Among the anodes containing biocoke, the utilization of A(3) presented the lowest reactivity (i.e., B3-A(3)3). This is in agreement with the XPS results. Biocoke modified with A(3) had the highest oxygen content (heteroatom), which increased the wettability of biocoke by pitch and, hence, improved the properties.  
Figure 4: Air reactivity of the anodes.
## 4 Numerical Examples
### Validation on Marmousi Model
Figure 14c,f,i,l shows the separated upgoing and downgoing waves and their frequency-wavenumber spectra obtained by the KGCAE while Figure 14b,e,h,k shows the corresponding results from the dual CAE method, demonstrating that, compared to the dual CAE, the KGCAE more effectively utilizes the axial and numerical distribution information from the knowledge representation of the wavefield. This leads to clearer axial characteristics in the separated wavefields, better preservation of the zero-mean properties, and more effective elimination of high-frequency wavefields. Figure 14: Comparison of VSP wavefield separation results on the Marmousi model using three methods. (**a,d**) predictions of upgoing and downgoing wavefields using the F-K filtering method; (**b,e**) predictions of upgoing and downgoing wavefields using the dualCAE method; (**c,f**) predictions of upgoing and downgoing wavefields using the KGCAE method; (**g,j**) frequency–beam spectra of (**a,d**); (**h,k**) frequency–beam spectra of (**b,e**); and (**i,l**) frequency–beam spectra of (**c,f**). frequency noise, thereby maintaining the frequency characteristics of the original signal. This indicates that the KGCAE exhibits superior performance in maintaining physical consistency and authentic signal properties when processing complex wavefields. Although the dualCAE method achieves relatively clean separation results through morphological prior constraints with clear and continuous axial characteristics in the target wavefield, it has certain limitations. Specifically, the first arrival portion of the upgoing wavefield appears chaotic; DC noise contamination is evident at the wavefield boundaries due to edge effects (as shown in Figure 14b,e). The frequency-wavenumber spectra further confirm these observations, revealing the limitations of dualCAE in managing boundary effects, particularly in complex geological structures, where the separation performance is less stable and precise than that of KGCAE. Furthermore, Figure 15 presents a comparative analysis of the reconstruction loss, \(L_{rec}\), for both methods, with the red curve representing KGCAE and the blue curve representing dualCAE. Analysis revealed that the proposed method demonstrated faster convergence and greater stability during training. This indicates that constraining the wavefield separation through physics-based knowledge representation leads to a more efficient optimization, thereby more rapidly achieving stable separation results.
# Design and Evaluation of a Web- and Mobile-Based Binaural Audio Platform for Cultural Heritage
## 5 Evaluation
### Part 3--Soundscape Experience
#### 5.3.2 Results  
During this experience, most participants have indicated feeling \"interested\" (7 participants), \"engaged\" (6 participants), and \"captivated\" (6 participants). Some indicated to have felt \"excited\" (2 participants), \"indifferent\", \"satisfied\", \"inspired\", \"immersed\", \"calm\", \"overwhelmed\" or \"confused\". The average score for personal resonance and emotional connection was 5.2 out of 7 (see Table 3a). The average score for learning and intellectual stimulation was 4.4 (see Table 3b). The average score for shared experience and social connectedness was 5.8 (see Table 3c). This means future improvements should be made to increase scores for personal resonance and emotional connection and learning and intellectual stimulation. It is important to underline how the soundscape adopted for  
Figure 6: Participant using the Soundscape Mobile app for part 3 of the evaluation.  
the evaluation did not contain any speech or narration describing the material presented, which might help to explain the score for learning and intellectual stimulation.
## 2 Antenna Modelling
### FR4 Substrate Material and Impact on the Antenna's Performance  
The antenna performance depends on the substrate material. FR4, which is inexpensive and electrically conductive, is a popular substrate material. Antenna designs must include specifics regarding the FR4 substrate material's dielectric characteristics, thickness, and performance. 1. Dielectric Properties: Highly dielectric FR4 fiberglass-reinforced epoxy laminate. Typically, the antenna's dielectric constant (\(\varepsilon\)r) ranges from 4.4 to 4.7 and is vital for its electrical properties. The antenna's effective electrical length and impedance matching are affected by a greater dielectric constant's substrate wavelength reduction. The dielectric constant of the FR4 material employed is crucial for antenna construction and analysis. 2. Thicker FR4 substrates affect the antenna's radiation pattern, impedance, and bandwidth. Thicker substrates increase mechanical strength but may impair antenna effectiveness. Thinner substrates can enable smaller designs but may limit power and bandwidth. Specifying the FR4 substrate's thickness is critical for understanding how it affects the antenna's electrical properties. The choice of FR4 substrate material affects antenna performance, including gain, radiation pattern, bandwidth, and impedance matching. Engineers and researchers can better evaluate the antenna's trade-offs and optimise its design to satisfy performance objectives by giving substrate material information. A thinner FR4 substrate may be better for compact designs but require careful impedance matching, whereas a thicker substrate may be better for high-power applications but narrower bandwidth.  
The antenna dimensional view and side view is presented in Figure 1a-c. The dimension values are in mm and are showcased in Table 1. The antenna basic parameters like reflection coefficient, impedance, VSWR, and smith chart representation are presented in Figure 2.
# Parametric Optimization of a New Gear Pump Casing Based on Weight Using a Finite Element Method
## 4 Discussion
### Optimization of Gear Pump Parts
#### 4.2.3 Optimization of the Gear Pump Casing  
As the modeling has shown, the groove on the bottom of the casing, formed by a chamfer on the outside, is a stress concentrator. As this stress concentrator cannot be removed or moved, it is necessary to increase the cross-section of the groove by rounding it (Figure 23). Similar groove modifications have also brought about benefits in stress distribution in other studies [54; 55].  
The main parameters of the case are D = 186 mm, h = 64 mm, H = 118 mm, D\({}_{1}\) = 166 mm, D\({}_{2}\) = 158 mm.  
By replacing the 2 \(\times\) 45\({}^{0}\) chamfer with a rounding with radius R4, a decrease in stress concentration was obtained in this zone.  
The stress distribution pattern remains the same; however, at the bottom of the aluminum gear pump casing the stresses are (163 \(\div\) 167) MPa; at the bottom of the cast iron casing--(140 \(\div\) 142) MPa; at the bottom of the polycarbonate casing--(182 \(\div\) 189) MPa.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Initial Mass, m\({}_{0}\), kg** & **Final Mass, m, kg** & **Safety Factor, \(n\)** & **Optimization Criteria, F** \\ \hline \hline \multirow{4}{*}{3.14} & \multirow{4}{*}{1.59} & Aluminum & \multirow{4}{*}{2.2} \\  & & Cast iron & \\ \cline{1-1}  & & Polycarbonate (PC) & \\ \cline{1-1}
2.08 & 1.64 & & 3.1 \\ \hline \hline \end{tabular}
\end{table}
Table 10: The optimality criterion for the front pump cover.  
Figure 23: Zone of maximum stress in the gear pump casing and a new technical solution.  
Figure 22: Comparison of the size of the front cover before optimization (100%) and after optimization (80%).  
When comparing the results before and after optimization, it was found that in the aluminum casing the stresses decreased by 10 MPa; in the cast iron casing the stresses decreased by 12 MPa; and in the polycarbonate casing the stresses decreased by 11 MPa.  
When parametrically optimizing the gear pump parts, the total weight of the aluminum pump was reduced by 2.19 kg. The weight of the cast iron pump was decreased by 3.63 kg. The weight of the polycarbonate casing decreased by 0.6 kg.  
According to the obtained data, the polycarbonate gear pump has less weight. However, aluminum should be chosen because aluminum was selected for the front and back covers based on the optimality criterion. In addition, it is technologically easier to manufacture aluminum gear pump parts, particularly the internal surfaces of the casing, which have a complex profile. Currently, cast iron [56] and aluminum [57] are still popular materials for pump bodies, but intensive work is underway on the introduction of plastics [47].
# Multi-Tier Cellular Handover with Multi-Access Edge Computing and Deep Learning
## 7 Results and Discussion
### Handover Performance  
#### 7.2.1 Total Handovers  
Figure 5a shows the total number of handovers for both algorithms. Interestingly, it can be noticed that the proposed algorithm has a higher number of handovers than its 3 GPP counterpart. In order to gain better insights into this phenomenon, we also analyzed the number of handover triggering events for each algorithm, as shown in Figure 5b. It was found that in the proposed algorithm, each handover was triggered only once (when it needed to proceed to the handover procedure phase), while in the 3 GPP scheme, each handover was triggered 2.8 times on average. This is due to the 3 GPP scheme only taking the current time instance to compare the RSRP, subjecting its handover decisions to greater impact by random channel effects.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Number of**} & \multicolumn{4}{c}{**Learning Time (s)**} & \multicolumn{4}{c}{**Prediction Accuracy (\%)**} \\ \cline{2-10}  & **Run 1** & **Run 2** & **Run 3** & **Mean** & **Run 1** & **Run 2** & **Run 3** & **Mean** \\ \hline
5 & 241 & 239 & 254 & 245 & 81.35 & 76.94 & 73.35 & 77.21 \\
10 & 273 & 284 & 294 & 284 & 88.44 & 71.08 & 86.32 & 81.95 \\
20 & 352 & 353 & 360 & 355 & 99.45 & 99.86 & 99.47 & **99.59** \\
40 & 540 & 550 & 533 & 541 & 94.12 & 98.82 & 99.27 & 97.40 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Effects of the number of hidden units (1000 epochs).  
Figure 5: (a) Total number of handovers and (b) number of triggering events.  
The higher total handovers by the proposed algorithm can be attributed to its focus on providing links with the best possible CQI to improve the user data rates, i.e., users could be handed over to achieve higher data rates, not only when they are at risk of losing the link to their current serving BSs due to mobility.
## 4 Results and Discussion
### Indices Selection and Seasonal Variation of All Indices
#### 4.3.1 Importance of Bio-Indicators for C Fluxes  
All indices that are of importance to C fluxes (GPP and Re) are identified in VIP values in Table 3. Eleven indices have layer VIP values than 1 and are considered as effective bio-indicator indices.  
Correlation coefficients were calculated between C fluxes and the 19 indices (Table 4). These indices had various sensitivities to GPP and Re. Thirteen indices had significant correlations (\(p<0.01\) or \(p<0.05\)) with GPP and Re simultaneously. The coefficients were generally higher for Re than for GPP. Most narrowband indices showed higher sensitivity to GPP and Re than most broadband indices. MCARI/OSAVI[705,750] had the highest correlation coefficients to GPP (R = 0.904) and the Vogelmann Index had the highest correlation coefficients to Re (R = 0.937).  
\begin{table}
\begin{tabular}{l c c c} \hline
**Index** & **GPP** & **Re** & **NEE** \\ \hline BLUE & \(-\)0.422 & \(-\)0.502 * & \(-\)0.044 \\ GREEN & \(-\)0.441 & \(-\)0.516 * & \(-\)0.027 \\ RED & \(-\)0.528 * & \(-\)0.600 ** & 0.008 \\ NIR & 0.664 ** & 0.633 ** & \(-\)0.277 \\ NDVI & 0.792 ** & 0.879 ** & \(-\)0.056 \\ \hline \end{tabular}
\end{table}
Table 4: Correlations of 19 indices to GPP, Re and net ecosystem exchange (NEE).  
\begin{table}
\begin{tabular}{l c c} \hline
**Index** & **VIP Value for GPP** & **VIP Value for Re** \\ \hline MCARI/OSAVI[705, 750] & 1.327 & 1.249 \\ Vogelmann index & 1.303 & 1.292 \\ Chlorophyll Index RedEdge 710 & 1.303 & 1.288 \\ EVI & 1.259 & 1.216 \\ SPRI*EVI & 1.254 & 1.189 \\ SPRI*NDVI & 1.225 & 1.229 \\ Leaf Chlorophyll Index & 1.214 & 1.241 \\ Dmax*1000 & 1.208 & 1.178 \\ NDNI & 1.195 & 1.141 \\ NDVI & 1.163 & 1.213 \\ G32 & 1.110 & 1.077 \\ NIR & 0.974 & 0.874 \\ RED & 0.775 & 0.827 \\ Dattl & 0.709 & 0.554 \\ GREEN & 0.648 & 0.712 \\ BLUE & 0.620 & 0.692 \\ Chlorophyll vegetation index & 0.337 & 0.579 \\ PRI(528,569) & 0.318 & 0.547 \\ Canopy Chlorophyll Content Index & 0.261 & 0.419 \\ \hline \end{tabular}
\end{table}
Table 3: Importance (VIP values) of 19 indices to GPP and Re calculated from partial least square regression (PLSR).
## 2 Materials and Methods
### Data Processing
#### 2.4.3 Estimation of \(f_{esc}\) and \(\Phi_{F}\)  
We further obtained the canopy fluorescence quantum yield at the \(\lambda\) band (\(cSIF_{yield\_\lambda}\)), which was equal to the canopy SIF at the \(\lambda\) band (\(cSIF_{\lambda}\)) divided by \(APAR\):  
\[cSIF_{yield\_\lambda}=\frac{cSIF_{\lambda}}{APAR} \tag{8}\]  
In addition, to study the physiology of fluorescence in relation to photosynthesis, we reduced the effects of the \(f_{esc}\). The \(f_{esc}\) at 760 nm (\(f_{esc\_760}\)) has been extensively studied [41; 52; 53; 51]. [PERSON] et al. [54] proposed a simple formula that performs well at estimating of far-red \(f_{esc}\) with extensive radiative transfer simulations, which can be expressed as:  
\[NIR_{\text{p}}=Ref_{NIR}\times NDVI \tag{9}\]  
\[f_{esc\_760}\approx\frac{NIR_{\text{p}}}{fPAR} \tag{10}\]  
where \(NIR_{\text{p}}\) is expressed as NIR reflectance multiplied by \(NDIV\) to represent the vegetation portion. \(NIR_{\text{p}}\) minimizes the influence of soil reflectance on the retrieved reflectance value [55], and can be applied under a wide array of field conditions, including over sparse canopies and regardless of soil brightness. However, it assumes that leaf albedo remains constant and is only applied in the far-red region, as it is not robust in the red region. Although an empirical approach based on a random forest regression method (RF) was applied to downscale red SIF, it was trained using simulations conducted with the SCOPE model [41]. In addition, a simple solution using the square of the _NDVI_ to correct the observed reflectance was used to calculate \(f_{esc\_688}\), which largely reduces the strong influences of soil reflectance at the red band [21; 56], which can be expressed as:  
\[Red_{v}=Ref_{red}\times NDVI^{2} \tag{11}\]  
\[f_{esc\_688}\approx\frac{Red_{v}}{fPAR} \tag{12}\]  
where \(Red_{v}\) is the red reflectance of vegetation and can be calculated with red reflectance (\(Ref_{red}\)) and the square of the NDVI (\(NDVI^{2}\)). Based on the simulation results (Figures S7 and S8), we can see that \(\frac{Red_{v}}{fPAR}\) was a good proxy for the escape efficiency of SIF at the red band. However, the estimation of \(f_{esc}\) in different bands should be explored because the complex changes in pigment content and the mixing of vegetation and soil background in the red band are much more complex than those in the far-red band [52].  
Based on the above equations, we can calculate \(\Phi_{F}\) in the red band (\(\Phi_{F\_688}\)) and far-red band (\(\Phi_{F\_760}\)) by removing the influences of \(f_{esc}\) as:  
\[\Phi_{F\_688}=\frac{cSIF_{688}}{PAR\times Red_{v}} \tag{13}\]  
\[\Phi_{F\_760}=\frac{cSIF_{60}}{PAR\times NIR_{v}} \tag{14}\]  
where \(cSIF\) is the observed canopy SIF, and the subscripts indicates the corresponding wavelength. In addition, the total SIF (\(tSIF_{A}\)) in the \(\lambda\) band can be calculated by multiplying \(APAR\) and the corresponding \(\Phi_{F}\) in the \(\lambda\) band:  
\[tSIF_{688}=APAR\times\Phi_{F\_688} \tag{15}\]  
\[tSIF_{760}=APAR\times\Phi_{F\_760} \tag{16}\]