esa-sceva/satcom-chunk-collection huggingface dataset:
# Removal of Contaminants in Water with Scallop Shell Waste
## 3 Results
### Potential Use of Scallop Shells Residues for Composite Materials  
Results obtained over the course of this work regarding the characterization of northern Chile scallop shell powders show that this waste has a low processing cost as well as high purity. Moreover, it is a promising material for the development of composite materials, as the morphology of these powders is similar to those used for coating protections [76] and for the development of a novel geopolymer matrix composite reinforced with basalt fiber [77]. [PERSON] and [PERSON] [78] obtained a novel material composed of an epoxy matrix reinforced with CaCO\({}_{3}\)-Al\({}_{2}\)O\({}_{3}\)-MgO-TiO\({}_{2}\)/CuO, wherein they demonstrated that an increase in CaCO\({}_{3}\) improved certain physical properties of the material, namely tensile and flexural strength, as well as impact resistance. DSC and TGA curves for the CaCO\({}_{3}\) used in their work are very similar to those obtained for northern Chile scallop shells. The DSC curves by [PERSON] et al. [34] shows that shell waste powder does not precipitate when used as reinforcement in the manufacture of epoxy resin matrix composite materials.  
[PERSON] et al. [79] increased CaCO\({}_{3}\) by 5%, obtaining a hardness increase of 3.14% and a 40% increase to the elastic modulus of a hybrid composite material composed of a polymeric matrix reinforced with glass fiber and polyphenylene sulfide. [PERSON] et al. [80] improved the mechanic properties of a bio-composite material containing bivalve mollusk seashell (Verenidae) extracted from the shores of Australia, and the CaCO\({}_{3}\) they used had a pore size comparable to that in the present work. [PERSON] et al. [81] obtained a 22% increase in tensile stress and a 17% increase in bending strength on a starch/fiber foaming composite material using a similar particle size to the ones in this work, as well as FTIR peaks of 875 cm\({}^{-1}\).
# SDN-Based Survivability Analysis for V2I Communications
## 4 V2I Network Survivability Model
The state \((n,j)\) to state \((n,j+1)\) indicates that the link fails, and the failure rate is \(\lambda_{1}\), correspondingly, the link repair rate is \(\mu_{1}\) times the number of fault links; the state \((n,j)\) to state \((n-1,j)\) indicates that there is a failure of a vehicle node, the failure rate is \(\lambda_{2}\) times the number of normal vehicles before the failure occurs, and its repair rate is set to \(\mu_{2}\); the state \((n,j)\) to state \((n-1,j-1)\) indicates that the vehicle node fault and link fault occur simultaneously. The original link fault is repaired from the \(j\) fault number to \(j-1\) fault number. The normal number of vehicle nodes changes from the \(n\) original number to \(n-1\) vehicle failure nodes. In this process, one link is recovered and one of the vehicles fails. The failure rate is set to the node failure rate \(\lambda_{2}\), which is recovered by the repair rate \(\mu_{2}\). According to the state transition relation of the model, we get the steady state and transient probability of the survivability of the model. Assuming that the steady state probability of the model is \(P_{su}\), then the probability has the following relation with the probability of each state:  
\[\frac{dP_{n}(t)}{dt}=\lambda_{1}P_{n-1}(t)+(n+1)\mu_{1}P_{n+1}(t)-(\lambda_{1 }+n\mu_{1})P_{n}(t),\ for\ n=1,2,\ldots,3N-1, \tag{2}\]1  
for the initial state \((n-1,j)\), the transient probability for \(t=0\) is \(P_{n-1,j}(0)=P_{n-1,j}\). Hence, we have:  
\[\frac{dP_{n}(t)}{dt}=\lambda_{1}P_{n-1}(t)+(n+1)\mu_{1}P_{n+1}(t)-(\lambda_{1 }+n\mu_{1})P_{n}(t),\ for\ n=1,2,\ldots,3N-1, \tag{2}\]2  
where \(j=0,\ 1,\ 2,\ldots n\ldots 3N-1\), let the transient probability of the model in \([0,\ t]\) time be \(P_{su}\ (t)\), then we can get:  
\[\frac{dP_{n}(t)}{dt}=\lambda_{1}P_{n-1}(t)+(n+1)\mu_{1}P_{n+1}(t)-(\lambda_{1 }+n\mu_{1})P_{n}(t),\ for\ n=1,2,\ldots,3N-1, \tag{2}\]3  
On the basis of the above survivability model, the following sections will focus on the probability model checking and validation of the survivability with PRISM. Moreover, according to the state transition and arrival situation, the relevant survival attribute formula is defined by CSL, and the survivability probability analysis and comparison are provided under various time boundary conditions.
## 5 Experimental Results and Analysis
### Comparison with DE, PSO, and Three State-of-the-Art DE Variants
#### 5.3.1 Optimization Results  
According to the results reported in Table 4, for \(D=10\) the proposed HE-DEPSO algorithm obtains the global optimal solution for functions \(F_{1}-F_{4}\), \(F_{6}\), and \(F_{9}\). For the unimodal function \(F_{3}\), DEPSO and SHADE obtain the global optimal solution. On the other hand, SHADE, CoDE, and DE obtain the global optimal solution in the simple multimodal function \(F_{9}\). For functions \(F_{5}\), \(F_{7}-F_{9}\), \(F_{11}\), \(F_{13}-F_{20}\), \(F_{23}\), and \(F_{30}\), the HE-DEPSO algorithm achieves the best result among all algorithms. DEPSO is the best in the functions \(F_{12}\), \(F_{21}\), \(F_{24}\), and \(F_{27}-F_{29}\). SHADE obtains the best results on function \(F_{10}\), while CoDE obtains the best results on functions \(F_{22}\), \(F_{25}\), and \(F_{26}\). The PSO does not show superior results in any function. The results of this table indicate that, for these tests, the proposed HE-DEPSO algorithm obtains the best ranking according to the mean value among all the algorithms. On the other hand, the results of the Wilcoxon rank sum test show that HE-DEPSO is superior to DEPSO, SHADE, CoDE, DE, and PSO in 17, 20, 24, 25, and 29 functions out of a total of 29 test functions, respectively.  
For \(D=30\), the statistical results presented in Table 5 show that the HE-DEPSO algorithm achieves the best results on the functions \(F_{1}-F_{5}\), \(F_{7}-F_{8}\), \(F_{10}-F_{18}\), \(F_{20}-F_{24}\), \(F_{26}-F_{27}\), and \(F_{29}\). On the other hand, DEPSO, SHADE, CoDE, and DE achieve the best results in the \(F_{27}\) function. DEPSO is the best in the functions \(F_{6}\), \(F_{9}\), \(F_{25}\), \(F_{28}\), and \(F_{30}\), while SHADE is shown to be the best in the function \(F_{19}\) in comparison to the rest of the algorithms. With these results, it is possible to identify that HE-DEPSO obtains the best ranking among all the algorithms for these tests. According to the Wilcoxon rank sum test results, among the 29 test functions, HE-DEPSO has 19, 20, 26, 26, 25, and 27 items that are better than DEPSO, SHADE, CoDE, DE, and PSO, respectively.
## 3 Results
### Evaluation of the Accuracy of the GPP Estimation Model
#### 3.2.1 Seasonal Variation Patterns of GPPRF  
Based on the SAI definition, the study area experienced a typical drought anomaly in 2009 (Figure 5a). Additionally, considering the delayed response of vegetation to drought conditions, GOSIF in the study area underwent a rapid decline during the 2010 growing season, predominantly from May to July, with a rate of decrease of 16.667%. Subsequently, from May to July 2011, vegetation growth resumed, and GOSIF exhibited a notable upward trend, with an average rate of increase of 17.133% (Figure 5b). This finding further substantiates that 2009 was a drought year for Xinjiang.  
From May to October, the monthly average vegetation GPP exhibited a seasonal ascending and subsequent descending pattern (Table 4). In spring, as vegetation gradually recovered and entered the growing season, GPP steadily increased, reaching an annual peak in July, with a monthly average of 82.189 gC m\({}^{-2}\)a\({}^{-1}\). Conversely, as vegetation growth activities slowed in autumn, GPP gradually declined until October, when it reached its lowest seasonal point, with a monthly average of only 27.464 gC m\({}^{-2}\)a\({}^{-1}\). The 2009 growing season exhibited a mean GPP of 313.125 gC m\({}^{-2}\)a\({}^{-1}\).  
Figure 4: GPPRF and GPPMODs accuracy verification.  
An examination of the spatial distribution characteristics of GPP estimated for the 2009 growing season (Figure 6) revealed spatial GPP heterogeneity in Xinjiang. Higher GPP values were observed in Iii Valley and the forest land and grass land areas of the northern Tianshan Mountains. Additionally, higher GPP values were found in the oasis regions of the Junggar and Tarim Basins. In contrast, regions south of the Tianshan Mountains and near the Kunlun Mountains exhibited lower GPP values.
# A Green AI Methodology Based on Persistent Homology for Compressing BERT
## 3 Our Proposal
### PBCE: Using Persistent Homology to Compress BERT
#### 3.2.1 Corpus Selection  
To measure the importance of each unit in the BERT model, an extensive text corpus is selected. Specifically, English Wikipedia [38] is chosen, consisting of more than 20 GB of sanitized text, including all entries from the Wikipedia in the mentioned language. To ensure that the texts, after tokenization, adhere to the input size constraint imposed by the model, the entries are split by periods, generating sentences, which will be the definite input for PBCE.  
This corpus is suitable because it contains a large number of texts on diverse topics, all written with high quality. Moreover, it is not directly related to any of the tasks in the GLUE benchmark, which will be used to evaluate the performance of the simplified models.  
**3.2.2**. Using Persistent Homology to Analyze BERT Layer Outputs  
Once the corpus has been selected for analysis, persistent homology is used to collect and analyze the topological characteristics expressed by each neuron in the network. Considering the output vectors generated by each neuron, the connected components are constructed. These connected components represent the clusters of data points that are topologically connected in some way, and their persistence features helps reveal meaningful hidden structures in the data. This information is crucial for understanding the topological features that each neuron contributes to the model. As can be seen in Figure 4, the connected components evolve as the radius \(r\) grows, until all collapse into one. The lowest value of \(r\) that provokes the union of all the connected components, denoted as \(r_{f}\), is crucial in the proposed methodology. The larger the \(r_{f}\), the farther the neuron's outputs are from one other, meaning that they exhibit a higher variability. Conversely, smaller values of \(r_{f}\) suggest that the neuron is less relevant, as its outputs are very close in the metric space generated by the unit, and can thus be removed. In cases where the neuron is removed, to avoid losing all the information generated by the neuron, the mean plus the standard deviation of the outputs is taken and added to the bias of the layer. This way, the contribution of the eliminated neuron is implicitly considered in the network's inference process.  
As mentioned in the previous section, the persistence diagram is used to analyze the evolution of the connected components. Since PBCE focuses on zero-dimensional persistent homology, the points in the diagram align along a line parallel to the Y-axis, since all the points share the abscissa (given the fact that all connected components are born simultaneously). Therefore, the values on the Y-axis are only considered. \(r_{f}\) can be readily identified in the persistence diagram as the value of \(r\) for which all connected components first merge.
## 2 Floating PV Power Generation Characteristics
### Operating Characteristics  
Figures 5-7 show the comparative results of the floating and land-based PV systems in terms of the amount of power generated, the PV module temperatures, and irradiation, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**PV Module and Array**} & \multicolumn{2}{c}{**PCS**} \\ \hline \multirow{2}{*}{\(\mathbf{P_{\text{max}}}\)} & \multirow{2}{*}{**Cell**} &
\begin{tabular}{c} **Array** \\ **Configuration** \\ \end{tabular} & \multirow{2}{*}{**Rated Output**} & \multirow{2}{*}{**Operating Voltage**} & \multirow{2}{*}{**Control Method**} \\ \hline
315 W & Polycrystal72 & \(4\times 2\) & \multirow{2}{*}{3.1 kW} & \(V_{\text{dc}}\): 100\(\sim\)500 V & Pulse–Width \\  & & & & \(V_{\text{ac}}\): 220 V, 60 Hz & Modulation (PWM) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specification of Power Generation Characteristics Comparison System (PGCCS).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Index**} & \multicolumn{2}{c}{**Array Output**} & \multicolumn{2}{c}{**Irradiation**} & \multicolumn{2}{c}{**Temperature**} & \multicolumn{2}{c}{**Wind Speed**} \\ \cline{2-9}  & \(\mathbf{V_{\text{dc}}}\) & \(\mathbf{I_{\text{dc}}}\) & **Horizontal** & **Slope** & **Air** & **Water** & **PV Module** & \\ \hline
**Unit** & V & A & W/m\({}^{2}\) & W/m\({}^{2}\) & \({}^{\circ}\)C & \({}^{\circ}\)C & \({}^{\circ}\)C & m/s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Measurement indexes of PGCCS.  
Figure 4: Configuration of PGCCS power circuit.  
Figure 5: Comparative results of power generation.  
Figure 6: Comparative results of module temperature.  
Figure 7: Comparative results of irradiation.  
During the survey period, the total solar irradiances were 622,262 and 615,696 W/m\({}^{2}\) for the land-based and floating PV systems, respectively, as shown in Figure 7. This means that the solar irradiance of the land-based PV system was 1.1% higher than that of the floating PV system. The reason for the solar irradiance of the floating PV system being slightly lower than that of the land PV system was presumed to be the mist at the water surface that occurred frequently. However, as shown in Figure 6, the daytime PV module temperature of the floating PV system was 4.9% lower than that of the land-based PV system. The outputs of the floating and land-based PV systems were 1408 and 1367 kWh, respectively, as shown in Figure 5. Therefore, the output of the floating PV system was 3% higher than that of the land-based PV system.
# Shear Strength Improvement of Clay Soil Stabilized by Coffee Husk Ash
## 3 Result and Discussion
### Direct Shear Result  
The clay internal friction angle and cohesion are obtained using the Mohr-Coulomb failure criterion based on the ultimate shear strengths of the soil at three vertical stress scenarios. Figure 10 illustrates the shear strength envelop of the Mohr-Coulomb criteria for natural soil and CHA-based composite soils. This figure demonstrates that shear stress rises correspondingly to normal stress, and the slope of the curve remains constant as the CHA content increases.  
Figure 11 illustrates the CHA influence on cohesiveness and frictional angle as the percent ash content increases. The angle of friction and cohesion of the treated clay can be used in geotechnical engineering to determine the stability and strength of this modified soil. The value of cohesiveness is observed to steadily increase from 80.1 kN/m\({}^{2}\) to 148.7 kN/m\({}^{2}\) with the addition of CHA. The angle of friction has a similar trend, gradually increasing from 16.1\({}^{\circ}\) to 25.8\({}^{\circ}\) upon CHA addition. The soil cohesion is proportional to the force that holds particles together. Water increases the bonding force immediately. However, this cohesion increase is limited. When the moisture content of the soil is excessive, there is more filtration distributed among the particles, which may reduce cohesiveness. Moreover, increased cohesiveness is a result of certain soil minerals transforming into clay minerals during decomposition [60]. Furthermore, the increase in friction angle can be attributed to the flocculation of soil particles caused by the addition of a micro-level of CHA, which resulted in filling the space between soil particles and a pozzol  
Figure 10: Direct shear failure envelope of the clay–CHA mixture.  
higher friction angle values. The clay soil becomes coarser following the addition of the CHA.
## 2 Laser Doppler Principle and TSD Testing Theory  
The Traffic Speed Deflectometer (TSD) has been widely adopted worldwide due to its high detection efficiency. This device utilizes the Doppler effect principle to measure the instantaneous deflection velocity of asphalt pavements under high-speed loading, effectively evaluating the pavement's bearing capacity.  
TSD is considered the most advanced pavement deflection measurement device currently available. Its primary components include the carrying vehicle, Doppler laser sensors, and a temperature control system. As shown in Figure 1a, the measurement vehicle of TSD is equipped with several Doppler laser sensors mounted between the two rear wheels of the trailer. These sensors are responsible for capturing the instantaneous deformation velocity of the pavement surface.  
To ensure measurement accuracy, one of the Doppler sensors, located at a distance of 3.5 m, is set as a reference point for calibrating the readings of the other sensors, as shown in Figure 1b. This setup allows TSD to perform rapid and continuous data acquisition  
Figure 1: (**a**) The sketch of TSD and its Doppler lasers); (**b**) schematic diagram of Doppler sensor location.  
without interrupting traffic flow, thereby improving the efficiency and accuracy of road inspection and maintenance work [22].  
During the measurement process, the vehicle moves at a high speed of \(V\), emitting a laser with a frequency of \(f_{1}\). After reflection, the frequency changes to \(f_{2}\). The deflection velocity of the pavement at this point can be calculated using Equation (1).  
\[V_{V}=\frac{(f_{2}-f_{1})\lambda}{2} \tag{1}\]  
After measuring the deflection velocity with TSD, it is converted into the deflection slope using Equation (2). In the equation, \(f_{1}\) (Hz) represents the emission frequency, \(f_{2}\) (Hz) is the received frequency, \(\lambda\) (m) is the emitted laser wavelength, \(V_{\mathrm{v}}\) (mm/s) is the deflection velocity, and \(S\) (mm/m) represents the deflection slope.  
\[S=\frac{V_{\mathrm{v}}}{V} \tag{2}\]  
Figure 2 illustrates the process by which deflection velocity is converted into pavement deflection. This is achieved through the integration of the deflection slope along the longitudinal direction.
## 4 Discussion
### Dependence of Hop Travel Time on Particle and Flow Features
For bed surface B, Figure 14 shows clearly that when there is an increase in flow Reynolds number from 44,060 to 51,488, which is about 15%, or an increase in the Shields number of about 10% from 0.099 to 0.111, with fixed particle density 1180 kg/m\({}^{3}\) in bed surface B, the particle will interact with the bed less and remain for longer durations and there will be an increase in the mean time interval of particle hop, which will increase by about 15% from 0.028 to 0.032 s, so there will be higher hopping (saltation length) with lower saltation movements and the average number of hops per second for particle movement decreases. According to Figure 14c, for a fixed Shields number (assume 0.1), mean hop time increases along with particle density increase (e.g., from density of 1132 to 1180 kg/m\({}^{3}\). Heavier particles respond slower to the same mean driving flow force (e.g., same mean bed shear stresses), leading to the particle having increased times between hop events. When particle density decreases by about 15% from 1224 to 1085 kg/m\({}^{3}\), the mean particle hop travel time will increase from 0.029 to 0.037 s, which is about 30% with a fixed flow Reynolds number of 47,379 surface B, which shows that the decrease in particle density  
Figure 14: Mean hop travel time for different instrumented particle densities: (**a**) against Reynolds number, for bed surface A, (**b**) against Reynolds number, for bed surface B, (**c**) against Reynolds number, for bed surface A, and (**d**) against Reynolds number, for bed surface B.  
results in big changes in particle hop travel time. The travel time of particle movement is short as the particle velocity is short and it increases as particle velocity increases. In conclusion, Figure 14 shows that the mean time interval of particle hops increases with higher flow velocity or particle velocity and decreases with higher specific density. Thus, with increasing channel flow Reynolds number or decreasing particle density, the particle will interact with the bed less and remain for longer durations and therefore achieve greater hop travel time. With increasing shear velocity, the total duration of the particle in motion will also increase.
## 5 Results
### Low-Magnitude Pruning + Quantization  
We combined both pruning and quantization methods. We chose to combine the two better results in terms of accuracy and occupied memory from the previous pruning simulations with both dynamic range quantization and FP16 quantization. Tables 17-20 show the results of this strategy for S-UNIWARD and WOW, for both 0.2 and 0.4 bpp.  
Table 17 presents the results for S-UNIWARD at 0.4 bpp. When combining pruning with a 50% pruning ratio and FP16 quantization, the model achieved accuracy of 87.13%, only 0.03% below that of the original model, while occupying three times less memory. Table 18 shows the results for S-UNIWARD at 0.2 bpp. Two different scenarios obtained better accuracy than the original model. At a 50% pruning ratio combined with FP16 quantization, the model exhibited 0.59% higher accuracy, achieving 74.19%, with a model size of 0.21 MB. A memory requirement that was about four times smaller than that of the original model was obtained, while achieving an increase of 1.59% in accuracy, when implementing a 70% pruning ratio, with the accuracy reaching 75.19%.  
Table 19 presents the results when combining quantization and pruning for the WOW algorithm at 0.4 bpp. By setting the pruning ratio at 70%, combined with dynamic range quantization, accuracy of 88.25% was achieved, resulting in a decrease of 1.55% when compared with the original GBRAS-Net model. The corresponding compressed model could save approximately 85% of memory, with 0.10 MB. By combining the same pruning configuration with FP16 quantization, accuracy of 89.49% and a 0.16 MB memory requirement for the model's storage were achieved.  
Table 20 presents the results for WOW at 0.2 bpp. By setting the pruning ratio at 80% and combining it with dynamic range quantization, a less memory-demanding model was obtained, with a requirement of 0.08 MB, i.e., an approximately 88% smaller memory storage requirement when compared with the original model. Accuracy of 78.12% was obtained, resulting in a decrease of 2.18% when compared to the original GBRAS-Net.
## 2 System Establishment and Modeling
### Modeling  
A steady-state model using a zero-dimensional approach utilizing mass and energy balance equations is sufficient to perform the necessary simulations for this investigation. Mathematical models are used to design, optimize, and predict the performance of each component within the hybrid system. The models also assist in understanding how the different components interact and affect each other within the system.
## 3 Results and Discussion
### Thermal Reflection Characteristic  
#### 3.2.1 Comparison of Heat Flux Reflected by Three Walls  
As depicted in Figure 10, applying a layer of paint to the block wall results in an increase in the heat flux reflection by about 0.15 \(\mathrm{mw/cm^{2}}\). Similarly, painting the brick wall increases its heat flux reflection by approximately 0.15 \(\mathrm{mw/cm^{2}}\), while for the earth wall, this increase is about 0.06 \(\mathrm{mw/cm^{2}}\). It is evident that the application of a thermal insulation coating significantly enhances the heat flux reflection of these walls.