esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This study proposes a method for separating asphalt and aggregates in recycled asphalt pavement (RAP) materials using surfactants as solvents. This method utilizes surfactants to soften the asphalt by reducing its surface tension, separating the RAP clusters, and washing away the asphalt from the RAP. The wastewater is recycled during the emulsification-separation process without discharge. Factors affecting the separation effect of RAP, including the type of anionic surfactants, the surfactant concentration, the emulsion-to-RAP ratio, temperature, the rotation rate and time, and the RAP's particle size, were investigated in depth, and the separation effect and its influence on the aggregate properties were evaluated. The experimental results indicate that when using the optimal process to mix and treat 13.2 mm and 9.5 mm RAP clusters, it is possible to achieve 100% separation of the coarse RAP above 4.75 mm, with a 64.58% reduction in the asphalt content. The angularity of the aggregate remained unchanged after separation. It was observed from scanning electron microscopy (SEM) images that the asphalt on the surface of the coarse aggregate had been eluted, and the morphology of the aggregate surface was completely exposed. This environmentally friendly separation method provides new possibilities for high-content RAP recycling in pavement engineering.  
RAP clusters; anionic surfactant; asphalt-aggregate separation; morphological properties; sustainable recycling 2025
# Spatiotemporal Modeling of the Smart City Residents' Activity with Multi-Agent Systems
## 6 Conclusions and Future Work
* The results of the research suggest that the context does play a significant role in shaping the effect of social trust on social participation. * The approach proposed by authors may facilitate constructing more and more holistic models of cities. It is also worth emphasizing that the developed model can be (after minor modifications) also used in other applications, e.g., stimulating the residents' activity to install photovoltaic panels [53], real estate or multilateral negotiations for building plots in distributed multi-agent environment [54]. As future work, it is worth considering to supplement the proposed multi-agent, agent-based model with a game theoretical treatment, in particular to identify possible social dilemmas, such as e.g., public goods, tragedy of the commons and trust dilemma, and their potential impact on the development of public trust in the considered urban agglomeration of groups of interacting individuals with different interests. The authors of the article also plan to expand the model with elements of gamification between residents to model different ways of social activity of city residents; specifically, it is planned to model the social gamification in a smart city, which is likely to stimulate the installation of the photovoltaic panels. A smart city, understood not as intelligent city infrastructure but as a smart, open geoinformation society, is shaped by the \"power of the powerless,\" which can be reinforced. The developed model is universal; it can be easily parameterized on the basis of any input data, e.g., social, sociological, or economic. The model will be verified and tested in other agglomerations and different cities. These will include cities characterized by a higher baseline level of trust (Scandinavian cities) and culturally different areas (Singapur, Masdar City). The code of the project in gaml language with included data used to support the findings of this study have been deposited in the git repository: [[https://gitlab.com/[PERSON]/smartcitygrowthgama.git](https://gitlab.com/[PERSON]/smartcitygrowthgama.git)]([https://gitlab.com/[PERSON]/smartcitygrowthgama.git](https://gitlab.com/[PERSON]/smartcitygrowthgama.git)). Conceptualization, R.O. and P.P. ; methodology, P.P, R.O., A.T., and B.K. ; software, M.B. and P.P. ; validation, A.T and T.P. ; formal analysis, R.O. and T.P. ; investigation, R.O, P.P., and A.T; resources, A.T.; data curation, P.P and B.K. ; writing--original draft preparation, R.O, P.P., A.T., and B.K. ; writing--review and editing, A.T.; visualization, R.O, A.T, and P.P. ; supervision, R.O. ; project administration, A.T. This work was supported by the FabSpace 2.0 project funding from the EU's Horizon 2020 research and innovation program under the grant agreement No. 693210. The authors declare that there is no conflict of interest regarding the publication of this paper.
# Adaptive Thermal-Aware Routing Protocol for Wireless Body Area Network
## 4 Performance Evaluation
### Simulation Results
#### 4.1.4 Analysis of Network Throughput with varying Nodes Densities  
Figure 19 represents the throughput comparison of ATAR, SHR [26], TARA [25] and MRRP [47] protocols having a varying number of nodes, i.e., 5-13 nodes with simulation time 1000 s. The immediate observation from figure is that even with the varying network size, ATAR shows improved performance compared to MRRP in term of network throughput. Moreover, it can be seen that ATAR protocols have the consistent network throughput. On the other hand, in case of MRRP, the network throughput is dropped to 50%. In ATAR, the throughput of the various network simulations with a different number of nodes at 1000 s. It can be observed from the graph that throughput of ATAR is 83% at 1000 s with five nodes. As the throughput is investigated with different networks and node density, it does not drop from the value of 83%, because of steadiness in a temperature rise of the nodes.  
Figure 18: Maximum temperature rise versus simulation time.
# Classification of Speed Change and Unstable Flow Segments Using Geohash-Encoded Vehicle Big Data
## 2 Literature Review
[PERSON] [21] generated regional model data for traffic flow and speed, mapping vehicle GPS data on a fixed grid. [PERSON] [18] studied spatial patterns of urban traffic congestion through a grid-based analysis of node capacity shortages, bottlenecks, and intersection congestion trends. Nonetheless, because these existing studies rely on a fixed grid size, their accuracy may vary depending on the grid configuration. Moreover, reducing grid size to enhance precision might inadvertently delay data processing. The central aim of this study was to create dynamic homogeneous speed zones from DTG data and to use them to facilitate the generation of speed information pertinent to micro-segments. These homogeneous speed zones were determined by comparing TMS and SMS. Typically, drivers are presented with an average speed for a designated segment, which is further partitioned into TMS and SMS. TMS and SMS represent, respectively, the arithmetic mean of vehicle speeds traversing a point or small road segment within a specified timeframe, and the quotient of speed by the distance covered by all vehicles transiting a road segment during the same period [8]. Usually, TMS is lower than SMS and both would be equal if all vehicles within a segment maintained an identical pace. Thus, the similarity between TMS and SMS serves as a metric for assessing the uniformity of vehicle speeds within a given segment. The present study evaluates TMS-SMS congruence based on the correlation between the two speeds. Past research has drawn upon empirical analyses to elucidate the relationship between TMS and SMS in the context of vehicles on expressways or national highways [22]. [PERSON] [23] established an empirical linear linkage between TMS and SMS within expressway settings. [PERSON] [24] also determined a linear relationship between these two speeds on a Malaysian national highway.
## 5 Discussion
### Phenology-Based Methods in GPP Estimation with LUE Models--Strengths and Limitations
When tracing back to the results of phenology extraction using the HGAM method at this grassland site, we found that the predefined threshold may have crossed the reconstructed VI three times rather than twice, as in earlier research (Supplementary Materials, Figure S1.19-S1.21, which suggests that the third crossing in the DOY may have been the SOS rather than the first intersection. Then, the third crossing and the second crossing in the DOY were set for the SOS and EOS, and we recalculated the GPP using the same procedures as mentioned above. The results showed that the R\({}^{2}\) between the EVI-based GPP\({}_{\text{phe-based}}\) and GPP\({}_{\text{EC}}\) was not substantially promoted (0.04 vs. 0.02). Therefore, it was still a challenge to use the phenology-based method in GPP estimation for the grassland ecosystem due to its greatly divergent or sharp waves in its VI time series after filtering [18]. Another factor that affects phenology extraction is extreme events (e.g., drought) under Mediterranean climates, resulting in vegetation activity that is significantly higher in winter than in summer, which generates an inflection point which may be potentially misidentified as the SOS or EOS [18]. Additional work should focus on using more grassland sites from additional years to provide a more comprehensive comparison of different phenology methods. Figure 5: _Cont._  
Figure 5: Comparison of the relationship between the five GPP products and GPP\({}_{\rm EC}\). The shaded areas represent the 95% confidence interval of the correlations. The short-dashed line is a 1:1 line. The unit of RMSE is g C m\({}^{-2}\) 8 day\({}^{-1}\). The method used to estimate \(\varepsilon_{0}\) is another limitation that should not be overlooked. Fitting light response equations is one of the commonly used methods. Specifically, \(\varepsilon_{0}\) is estimated from the Michaelis-Menten light response equation to fit observed NEE, PAR, and ecosystem respiration, which was adopted in this study. This method is largely dependent on the choice of equation forms. [PERSON] et al. [67] found great variations of \(\varepsilon_{0}\) when comparing its estimates from low light regression and the light response equation across eight biomes. Future work is needed to synthesize these methods to offer a comprehensive vision of \(\varepsilon_{0}\) estimation.
## 3 Results and Discussion
### Relative Density (RD)
#### 3.1.1 Machine Learning Prediction Models of RD  
Several ML approaches, such as linear regression, regression trees, random forest regression, Gaussian process regression, and artificial neural networks, were used to predict the properties of the AMC. The experimental data were used to generate random samples for the training and assessment datasets, which respectively comprised 87% and 13% of the input for each dataset. Both groups fell within the same statistical range when looking at the data from the experiment. Following the training, the performance of the model was evaluated utilizing the RMSE as well as the R\({}^{2}\)-score. The ideal RMSE and R\({}^{2}\) values for the RD parameter were 0.19 and 0.98, respectively, for the training set; for the testing set, these values were 0.24 and 1.  
As can be seen in Figure 3, both the RD training set and the testing set proved the model's ability to predict values that were quite similar to the actual data that were given to it. An ANN was enhanced using input, output, two hidden layers, rectified linear unit (ReLU) activation functions, and the levels of input and output in order to obtain these results. The other algorithms, as shown in Table 3, behaved adequately during the training and testing phases; nevertheless, the precisely adjusted ANN that was described earlier performed better than the other algorithms that were picked. The differences in error that can be seen between the predicted findings and the experimental results for the RD of the AMCs are presented in Table 4 and show very minimal differences.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**ML**} & \multicolumn{2}{c}{**Training Set**} & \multicolumn{2}{c}{**Testing Set**} \\ \cline{2-5}  & **RMSE** & **R\({}^{2}\)** & **RMSE** & **R\({}^{2}\)** \\ \hline Linear regression & 0.68 & 0.82 & 0.55 & 0.89 \\ Regression trees & 0.28 & 0.97 & 0.74 & 0.79 \\ Random forest regression & 0.5 & 0.91 & 0.73 & 0.81 \\ Gaussian process regression & 0.27 & 0.97 & 0.58 & 0.87 \\ Artificial neural networks & 0.19 & 0.98 & 0.24 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Evaluation metrics for the training and testing groups of RD% for ML models.
## 2 Testing of Dynamic and Static Elastic Parameters
### Synchronized Testing Methods for Dynamic and Static Parameters  
The synchronous testing apparatus used for the U.S. GCTS RTR1500 (GCTS, Tempe, AZ, USA) high-temperature and high-pressure triaxial rock mechanics test system is shown in Figure 3. The sample acoustic wave velocity is measured using the ultrasonic transmission method in the system. The system can simulate the highest temperature of 150 \({}^{\circ}\)C, a maximum loading confining pressure of 140 MPa, a maximum axial load of 1500 KN, and an axial loading frequency of 10 Hz; the maximum pore pressure can reach 140 MPa and a sample diameter of 25-100 mm.  
The end faces of the test rock specimens are polished, and the samples are treated in a natural dry state. The rock samples sealed with heat-shrinkable sleeves are placed in a high-temperature and high-pressure triaxial chamber, and high-precision extensometers are installed on the samples to measure the longitudinal and transverse deformation. For rock static testing, the sample is first subjected to confining pressure (hydrostatic pressure) to a specific value and then heated to a set temperature. After stabilizing the confining pressure and temperature, axial stress (differential stress) is applied at an equal axial displacement rate until the specimen is destroyed. The rock hydrostatic parameters, compressive strength, elastic modulus, and Poisson's ratio under the corresponding temperature and pressure conditions can be obtained by recording the axial stress, axial deformation, and transverse deformation data. When conducting the synchronous testing of rock statics and acoustic velocity, a pressure and temperature-resistant ultrasonic transmitter and receiver transducer (with a center frequency of 1 MHz) installed in a triaxial chamber is used instead of a conventional mechanical pressure head; during the process of measuring the static deformation of rocks, the P-wave and S-wave velocities are simultaneously measured by the ultrasonic system under the same temperature and stress variation conditions. The experiment simultaneously acquired rock static deformation data and P-wave and S-wave velocities under the same sample, temperature, and stress variation conditions [41]. Based on the measured P-wave and S-wave velocities, the rock dynamic mechanical parameters under the corresponding temperature and stress conditions are calculated, as shown in Equations (3) and (4).  
Figure 2: Experiment schematic diagram.
## 2 Materials and Methods
### Data Anaylsis  
Sorption isotherms are models that predict the nature and mechanism of interaction between sorbates and sorbent. The obtained sorption data were analyzed using the linear and two-parameter models: Freundlich and Langmuir isotherms. The isotherm constants were obtained by linearizing the model using linear regression as the simplest method for data analysis.  
Through the linear model, the sorption coefficient \(K_{d}\) is expressed as the ratio between the amount of drug in the sorbent (\(q_{e}\), mg/g) and the solution (\(C_{e}\), mg/L) [30]:  
\[q_{e}=K_{d}\times C_{e} \tag{1}\]  
The empirical Langmuir model (2) was used to describe whether the sorption process is a homogenous process that forms in a monolayer on a fixed number of active sites with no interactions between the sorbed molecules. It is given in a linear form [31]:  
\[\frac{1}{q_{e}}=\frac{1}{q_{m}}+(\frac{1}{q_{m}K_{L}})\frac{1}{C_{e}} \tag{2}\]  
where \(q_{m}\) (mg/g) and \(K_{L}\) (L/mg) represent the maximum sorption capacity (mg/g) and the equilibrium constant referred to sorption energy and the affinity between TiO\({}_{2}\) and pharmaceuticals. Calculating \(R_{L}\), a dimensionless constant, the nature of sorption can be inferred; \(R_{L}\) values above 1 indicate an unfavorable process, whereas sorption is favored at \(R_{L}\) between 0 and 1 [32].  
The Freundlich isotherm can be applied to describe the sorption equilibrium for heterogeneous surfaces with possibility of multilayer formation, where all sorption from all sites occurs at the point with the highest binding energy [31; 33].  
\[q_{e}=K_{F}C_{e}^{\frac{1}{2}} \tag{3}\]  
\(K_{F}\) is the Freundlich constant indicating the relative sorption capacity ((ug/g) (mL/ug)\({}^{1/n}\)) and \(1/n\) is the heterogeneity parameter which indicates the sorption intensity with values ranging from 0 to 1 [34]. Values above 1 indicate cooperative sorption, while lower values than 1 are caused by chemisorption [35].
# Optical Chirality Determined from Mueller Matrices
## 4 Results
### Structural Optical Activity in Cuticle from Cetonia Aurata
#### 4.3.2 Method II  
Differential decompositions of the data in Figure 6 are shown in Figures 7 and 8 and reveal the bianisotropic properties. The circular effects \(C_{\mathrm{B}}\) and \(C_{\mathrm{D}}\) dominate but some linear effects are seen. Sample CA1 has larger optical activity with a maximum of \(C_{\mathrm{D}}=0.5\) compared to sample CA2 with a maximum of \(C_{\mathrm{D}}=0.38\). Sample CA1 has an azimuth such that the biaxial properties are close to the \(\pm 45^{\circ}\) direction of the instrument reference coordinate system resulting in small \(L_{\mathrm{B}}\) and \(L_{\mathrm{D}}\), and most of the information about linear anisotropy is found in \(L_{\mathrm{B}}^{\prime}\) and \(L_{\mathrm{D}}^{\prime}\). In sample CA2, linear birefringence and dichroism are distributed between \(L_{\mathrm{B}}\) and \(L_{\mathrm{B}}^{\prime}\) and \(L_{\mathrm{D}}\) and \(L_{\mathrm{D}}^{\prime}\), respectively, due to another azimuth. Sample CA2 has larger depolarization compared to sample CA1 as seen in the \(\mathbf{L}_{\mathrm{u}}\)-matrices in Supplementary Materials.  
Figure 6: Transmission Mueller matrices of elytra from two specimens of _C. aurata_. The blue and red solid curves are data recorded on specimens with low (sample CA1) and high (sample CA2) depolarization, respectively. The yellow dashed curve is the best fit to data from CA1 using regression analysis in a bianisotropic model (method III).  
Figure 7: Birefringence and dichroism of the elytron from _C. aurata_ (sample CA1).
# A Transfer Learning Method for Pneumonia Classification and Visualization
## 4 Proposal  
### Image Preprocessing  
Pretrained models use always squared images as input. CXRAY images from Pneumonia dataset are not squared, the biggest image has dimensions of \(2916\times 2583\) pixels, and the smallest is \(384\times 127\) pixels. If we only resized the images to match the input dimension for the CNN, we would have enlarged images that could probably suppress the features of interest. Figure 6 shows an example of a resized image compared with the original.  
We use a simple preprocessing technique, proposed by [PERSON] et al. [39] to maintain as much information as possible of the region of interest of the CXRAY images.  
Steps are as follows:  
1. Remove any possible black band from the edges of the image.
2. Resize the image to achieve that the smaller edge is (in our case) 299 pixels long.
3. Extract the central \(299\times 299\) region. An example of the preprocessing technique is shown in Figure 7.  
Figure 6: Examples distortion on the images by applying only resizing to the dataset in order to adjust them to the input shape of the Convolutional Neural Networks (CNN) model. (**a**) shows the original image of a person with pneumonia ([[https://data.mendeley.com/datasets/rscbjbr9](https://data.mendeley.com/datasets/rscbjbr9) js/2]([https://data.mendeley.com/datasets/rscbjbr9](https://data.mendeley.com/datasets/rscbjbr9) js/2)); (**b**) shows the resized image to \(299\times 299\) pixels.  
With this preprocessing technique, we are trying to use all the information on the image and feed the network with only pixels that are of interest and avoiding black spaces that do not provide any information to extract useful features for the classification task.  
The media value of the training set was computed and subtracted from the training, validation and test sets, and all three were divided by the standard deviation of the training set. In other words, the distribution of the dataset was established with media \(\mu=0\) and standard deviation \(\sigma=1\). This process is known as Standardization.
## 3 Results and Discussion
### Effect of Surface Reaction Shape  
Figure 1 exhibits the geometry of the two biosensors having different shapes of reaction surface and electrode arrangement. Figures 3 and 4 show the transient evolution of the CRP complex of these two biosensors for \(\mathrm{u_{ave}}=100~{}\mathrm{\SIUnitSymbolMicro m}/\mathrm{s}\) without applied voltage (\(\mathrm{V_{rms}}=0~{}\mathrm{V}\)) and with applied voltage (\(\mathrm{V_{rms}}=15~{}\mathrm{V}\)), respectively. The geometric parameters of the two biosensor models are given in Tables 1 and 2.  
was taken equal to S = 800 \(\upmu\)m\({}^{2}\). The electrodes angle \(\theta\) and \(\alpha\) for the first biosensor model were equal to 40\({}^{\circ}\).  
Figures 3 and 4 present the evolution of the normalized complex concentration C\({}_{\rm s}\) during the association and dissociation phases. During the first phase (association), the complex concentration increases almost linearly until reaching saturation. At this stage (saturation), all the ligands are occupied by the analytes injected at the inlet of channel. After a given time (\(t\) = 800 s), the injection of analyte is stopped and therefore the kinetic bond dissociation will take place.  
In the case of V\({}_{\rm rms}\) = 0 V (Figure 3), the binding reaction rate related to the circular reacting surface without the application of the electrothermal force was smaller than that related to the reaction surface having the form of a circular ring.  
In the case of V\({}_{\rm rms}\) = 15 V (Figure 4), a significant improvement in the binding reaction was observed for the second model compared to the first model. The electrothermal force induced by the electrodes for the second model contributed effectively to the increase of the reaction rate. The shape of the reaction surface and the electrode arrangement have an important role in improving the biosensor response.