esa-sceva/satcom-chunk-collection huggingface dataset:
# Thermo-Viscoelastic Characterization of 3D Printing Polymers
###### Abstract  
Polymer materials used in 3D printing exhibit degradation of material mechanical properties when exposed to thermal environments and thermal expansions can induce residual stresses in products or molds, which may result in dimensional instability and subsequent structural failures. In this study, based on linear thermo-viscoelastic principles, material degradation master curves, shift functions, and glass transition temperatures for four different polymers used for 3D printing techniques such as Multijet Printing and Digital Lighting Process were measured by using a dynamic mechanical analyzer. Based on the single frequency test, the glass transition temperature was measured. In addition, dynamic measurements were carried out over a frequency range at isothermal condition and storage modulus vs. frequency curves were obtained. Then, the storage moduli curves measured at different temperatures were superposed into master curves using the frequency-temperature superposition principle and shift factors were calculated as a function of temperature. Subsequently, the complex moduli curves that were measured in the frequency were curve-fitted onto generalized Maxwell models by using the least squares method and the master curves of relaxation moduli at reference temperature were obtained. The effects of temperature, frequency, and time on dynamic moduli and relaxation behaviors of four polymers used for 3D printing were evaluated. Experimental results showed that Polymers C and D could be suitable to use at the service temperature above 100 \({}^{\circ}\)C and Polymer C was highly crosslinked and showed low modulus reduction after about a year. The master relaxation curves obtained through this process can be utilized to predict the long-term performance of polymer molds made by 3D printing at a given environmental condition.  
3D printing polymer; thermo-viscoelasticity; master relaxation curves +
Footnote †: journal: Applied Sciences
## 1 Introduction  
Obesity presents an urgent public health challenge. Whilst obesity is a multi-factorial disease, increased access to energy-dense food is an often-cited factor. However, this type of food is now an integral part of the modern food supply chain across the entire socio-economic spectrum. Therefore, additional strategies to reduce the negative effects of high-carbohydrate and high-fat diets are required.  
Polyphenols are plant-derived compounds linked to a variety of health outcomes including weight loss. A number of anti-obesity pathways have been identified which may have a positive influence on weight loss and health [1]. These compounds are abundant in plants and can be safely added to food. Plant-derived compounds from cranberries [2], coffee [3], green tea, [4; 5] and cocoa [6], amongst others, have been observed to have an effect against obesity.  
The purpose of this study is to understand the effect of polyphenols derived from sugarcane on weight loss. This may seem like a paradoxical botanical source to explore for anti-obesity compounds, due to the notoriety sugar has received for its role in global obesity rates. However, sugarcane polyphenols are quite well characterized in terms of composition [7; 8; 9; 10] and many of these individual compounds have demonstrated anti-obesity effects, as previously reported [11; 12; 13; 14]. It is a globally abundant crop that if shown to be effective against obesity could have a rapid effect on this condition.  
Previous studies involving the feeding of sugarcane plant extracts to rodent models indicated the presence of factors in sugarcane that may have anti-obesity effects. An early study on \"the non-sugar fraction of crude black sugar\" showed positive effects on both carbohydrate and lipid metabolism of rats [15; 16]. From this study, specific glucosides were isolated from sugarcane using a detailed extraction regime including methanol, water and charcoal chromatography. These compounds were capable of inhibiting insulin and fructose concentration and reduced the absorption of glucose in the intestine.  
The methods outlined by [PERSON] et al. [15; 16], whilst effective at a laboratory scale, are not scalable for industrial and food grade purposes. We have previously reported a food-grade polyphenol-rich sugarcane extract (PRSE) that has distinct regulatory functions on pathways involved in carbohydrate metabolism [17]. It was hypothesised that PRSE may have anti-obesity properties. To explore this hypothesis we measured weight loss, feed intake, energy expenditure, mRNA gene expression, glucose tolerance, leptin and adiponectin concentrations. We demonstrated that 12-weeks supplementation at 4% PRSE is sufficient to induce a significant decrease in body weight. This weight loss appeared to be mediated by a combination of factors including reduction in carbohydrate absorption, feed digestibility and upregulation of a number of metabolic pathways.
# Autonomous Navigation of Robots: Optimization with DQN
## 6 Discussion of Results  
The control algorithm was evaluated in a three-dimensional environment using the Turtlebot3 Burger mobile robot. The training process involved \(400\) iterations where the robot learned to navigate towards a goal while avoiding obstacles. The level of reward obtained for each executed action was measured, reflecting the effectiveness of the trained network.  
The rewards obtained with different optimizers were collected from the DQN network based on reinforcement learning. Figure 18 illustrates the reward trends, which were represented using a smoothing technique incorporating weighted adjustments derived from the least-squares method.  
Among the optimization algorithms tested, RMSprop demonstrated the best results. When utilizing RMSprop, the robot exhibited a higher success rate in reaching the goal and effectively avoided obstacles. The reward values steadily increased over time, indicating that the network was functioning optimally and the algorithm was learning from its experiences.  
Throughout the training process, the goal positions were randomly generated within the arena, ensuring the robot encountered different scenarios. The linear speed of the robot remained constant at \(v=0.15,\mathrm{m/s}\), while the angular speed varied based on the chosen action, as presented in Table 1. Initially, the robot frequently collided with obstacles and struggled to reach the goal. However, as the training progressed through multiple episodes,  
Figure 16: MSE function and RMSprop optimizer.  
Figure 17: MSE function and SGD optimizer.  
the agent learned how to interact with the environment more effectively. It autonomously determined its trajectory, avoiding collisions, and achieved the goal by attaining positive scores.  
The experimentation conducted served to validate the effectiveness of the model. By successfully training the robot to navigate and make decisions based on reinforcement learning, the study demonstrated the potential and applicability of the developed approach.  
The performance of the simulation is an important factor during the training of the model, because if the hardware has adequate resources, then less time will be needed to reach the number of episodes proposed; in turn, obtaining information from the environment will be optimal, allowing the algorithm to perform an adequate control.
# New Quality Control Algorithm Based on GNSS Sensing Data for a Bridge Health Monitoring System
## 4 Concluding Remarks  
This investigation presented an attempt to prove the accuracy of the GNSS positioning solution for use in a BHMS based on GNSS. The applicability of GNSS for BHMS has already been validated through prior research, however, specific problems were exposed in the delivery of a reliable GNSS solution. These problems were due to the lack of consideration of outliers; the occurrence of such outliers can provide false information to the BHMS when the positioning solution is used in deflection and dynamic characteristic analyses, such as for natural frequencies and damping ratios. An experiment was performed on a long-span cable-stayed bridge. The results confirmed that approximately 110 outliers were present in a sample of real data, which had a clear influence on the GNSS positioning solution. However, when the proposed algorithm for quality control was used as a DIA procedure to minimize the effect of outliers, 93.7% (103) of all outliers were eliminated, and the results were shown to be much more reliable.  
This research was supported by the Ministry of Land, infrastructure, and Transport in Korea under the Urban Planning and Architecture (UPA) research support program and was supervised by the Korea Agency for Infrastructure Technology Advancement (KAIA).  
[PERSON] and [PERSON] originated this work and prepared the manuscript; [PERSON] designed the experiments and contributed to the theory studies; improve the quality of this work. All authors reviewed the manuscripts.  
The authors declare no conflict of interest.
## 4 Discussion
### Legacy in the Regional GPP Variability  
Reduced precipitation, continental freshwater discharge [76], and evapotranspiration [77] over most land areas are associated with El Nino, whereas wetter land occurs during La Nina [39]. It is clear that El Nino years induce more droughts and a restricted amount of terrestrial moisture, which increases plant moisture stress through higher moisture demand and the corresponding rise in continental temperature. We show that El Nino results in a decrease in GPP, while La Nina results in an increase GPP, which is consistent with [77; 78], who demonstrated that the global terrestrial biosphere reduces in both net and total production with warm ENSO (El Nino) occurrences, but the capacity of the terrestrial carbon sink rises with cool ENSO (La Nina) events. It has proven that the ENSO phases (La Nina, neutral, and El Nino years) strongly impact the vegetation productivity on a continental scale [79].  
In this study, we also found that the ENSO shows lagged effects on GPP and varies continently. For the GPP aggregated in each continent as defined in Figure A7, different time lags are obtained. North America, Asia, and Europe (mainly distributed in the NorthernHemisphere) show a >3-month time lag and South America, Africa, and Oceania (mainly distributed in the Southern Hemisphere) show just a <3-month time lag (Figure A8c). A clear lag effect of ENSO indices on global GPP variability was discovered by [80] when they evaluated the effects of ENSO on global GPP dynamics from 1982 to 2016. Our results are similar to those of [PERSON], et al. [81], which illustrated that South Africa, Australia, and Southeast Asia are the regions most impacted by El Nino phases and, in those regions, dry conditions are observed at the beginning of events over short time scales (1-3 months). However, in large areas of America and Eastern Europe, the effects were more noticeable over longer time scales and were impacted by La Nina phases. Additionally, [PERSON], et al. [82] demonstrated that because of the response of the Southern Hemisphere's ecosystems, notably in tropical and subtropical regions, ENSO explains more than 40% of global net primary production (NPP) variability.
# Social Media Analytics for Disaster Response: Classification and Geospatial Visualization Framework
## 4 Results
### Social Media Text Classification
#### 4.1.2 Metrics  
Model performance was evaluated using the weighted average F1 score (F1), a metric specifically selected to address issues related to class imbalance within the dataset. Unlike standard accuracy measures, which can be misleading in imbalanced datasets, the weighted F1 score accounts for the proportional representation of each class, ensuring that underrepresented categories still contribute meaningfully to the overall evaluation. By utilizing the weighted F1 score, the evaluation process became more nuanced, preventing the model from being overly biased toward majority classes. This ensured that the model could effectively identify critical instances that are highly relevant for disaster response efforts. The model that consistently demonstrated the highest weighted F1 score across these runs was selected as the final model for deployment, ensuring reliability in real-world disaster response applications.  
The F1 score and weighted F1 score provide a balanced evaluation by considering both precision and recall. The F1 score is the harmonic mean of precision and recall, defined as follows:  
\[\text{F1}=2\times\frac{\text{Precisioin}\times\text{Recall}}{\text{Precision}+\text{Recall}} \tag{1}\]  
Precision (P) is the proportion of correctly classified positive instances among all instances predicted as positive:  
\[\text{P}=\frac{\text{True Possitives (TP)}}{\text{True Positives(TP)}+\text{False Positives (FP)}} \tag{2}\]  
Recall (R) is the proportion of correctly classified positive instances among all actual positive instances:  
\[\text{R}=\frac{\text{True Possitives (TP)}}{\text{True Positives(TP)}+\text{False Negatives (FN)}} \tag{3}\]  
The weighted F1 score accounts for class imbalances by calculating the F1 score for each class and averaging them based on the number of samples in each class:  
\[\text{Weighted F1}=\sum_{i=1}^{n}w_{i}\times\text{F1}_{i} \tag{4}\]  
* \(\text{F1}_{i}\) is the F1 score for class \(i\).
* \(w_{i}\) is the weight for class \(i\), defined as the proportion of samples belonging to that class.  
* \(n\) is the total number of classes. During model training, the evaluation step monitored the F1 score at each epoch and compared it against the highest previously recorded score. Whenever an epoch achieved a higher F1 score, the corresponding model was automatically saved as the best-performing model. This best model was subsequently selected for deployment in our visualization system.
# The Effect of Ageing on Chemical and Mechanical Properties of Asphalt Mortar
## 4 Conclusions  
Based on the findings and analysis presented in this paper, the following conclusions can be drawn: (1) Asphal mortar--a mixture of bitumen, filler, and sand--has all functional groups as its three component materials. Carbonyl group that is present in the mortar originates only from the presence of bitumen, whereas sulfoxides can be present in all three components. (2) Ageing increases the complex shear modulus and decreases the phase angle of bitumen and asphalt mortar. The high difference in stiffness observed between bitumen and asphalt mortar is due to the presence of filler and sand particles. The less difference observed between the phase angle values of bitumen and mortar is due to the fact that the phase angle is mainly governed by the bitumen component. (3) Ageing leads to higher strength and smaller displacement at failure compared to unaged sample. In other words, aged samples tend to become more brittle. (4) Both chemical and mechanical test results have shown that the sand and filler particles present in the mortar may slow down the diffusion rate of oxygen. The hypothesis is that the presence of particles increases the length of the diffusion path and therefore slows down the ageing process. This will help to understand the interaction between components during ageing and develop a more suitable protocol to simulate ageing procedure of bituminous materials in the field.  
Figure 11: Tensile strength of mortar_AM and mortar_MA with different ageing times.  
Figure 10: Normal force vs. displacement of mortar_AM with different ageing times.  
The results of this study will help to understand the ageing process of asphalt mortar and more accurately predict evolution of its properties due to ageing. Based on the results, the relationship between the chemical and mechanical properties of asphalt mortar can be determined, and a chemo-mechanical model of asphalt mortar can be developed and implemented using the finite element method.  
[PERSON], [PERSON], and [PERSON] conceived the experimental plan and contributed to the interpretation of the results. [PERSON] carried out the experiments. [PERSON] and [PERSON] supervised all project activities.  
This research received no external funding.  
The authors gratefully acknowledge the Dutch Ministry of Transport, Public Works, and Water Management for funding this project.  
The authors declare no conflicts of interest.
## 3 Description of Methods and Tools
### Optimization and Data-Based Tools  
Classical parameter identification is based on optimization or soft computing tools [10; 11; 12]. In fact, the parametrized model provides predictions of the response for every choice of the parameters. Provided that the parametrization is able to model the expected damages, the parametrized results are compared with the measurements, and the values of the parameters are updated. Responses of more than one loading can be used together, fused, in order to enhance effectiveness. Moreover, the parameter identification problem is generally ill-posed, in the sense that a multiplicity of solutions and inefficient scaling arises [13]. Therefore, quite complicated regularization techniques are needed in order to facilitate the usage of numerical optimization. For this reason, the usage of machine learning and data-based techniques that address the identification question on a reduced-order model or surrogate model is beneficial [14; 32]. Another promising approach is the usage of filtering, such as Kalman-type filters, for the solution of parameter identification problems [10; 15].  
Processing big data and extracting knowledge or simple predictions is a challenging task. Cooperation with experts and the usage of appropriate tools is a must. Google realized savings of 40% on its own data centers using DeepMind. In mechanics, solid models can assist the user in evaluating and understanding the data. For example, the blind evaluation of data using neural networks (see, e.g., [33; 34]) can be assisted with the knowledge of the mechanical or mathematical model that governs the underlying relation, such as the physics-informed neural networks (PINNs) developed for the solution of partial differential equations arising in mechanics [35; 36]. Similarly, a blind attempt to extract knowledge within a digital twin system is facilitated by the usage of computational mechanics models that create data for several possible scenarios, leading to model-assisted, cognitive digital twins [29].
## 5 Results and Discussion
### Sample Restoration Experiment Based on Autoencoder
First, the AE might have successfully learned the true underlying distribution of the original data and effectively removed the  
Figure 8: Comparison of accuracy for various adversarial examples and their restoration sample. Figure 9: Comparison of classifier accuracy before and after restoration for different optimization attacks. (**a**) Comparison of classifier accuracy before and after restoration under different C parameters for CW attack; (**b**) comparison of classifier accuracy under different overshoot parameters for Deepfool attacks. artificially introduced perturbations from the adversarial examples. Consequently, the reconstructed adversarial examples are closer to the original samples in terms of their features, allowing the classifier to make more accurate identifications. Second, the presence of the AE could be exerting a regularization effect on the classifier, reducing its sensitivity to perturbations and enhancing its ability to identify key features. Overall, this observation suggests that the AE can serve as an effective tool to assist the classifier in detecting adversarial examples, thereby improving the model's recognition accuracy.
# Bistatic Sea Clutter Suppression Method Based on Compressed Sensing Optimization
## 3 The Proposed Method Description
### Optimal Design of Sensing Matrix
Construct a new Gram matrix \(G=\ SD^{T}D\widetilde{S}^{T}\);
6. Obtain initial sensing matrix \(\Gamma^{0}\) from Gram matrix \(G,=\ \Big{(}\Gamma^{0}\Big{)}^{T}\Gamma^{0}\);
7. Iterative optimization of sensing matrix \(\Gamma\) Output: Optimized sensing matrix \(\Gamma\)
```  
**Algorithm 2**Optimization of Sensing Matrix Based on Eigenvalue Decomposition and Gradient Descent
## 4 Procedures and Results of Diagnosis
### Comparison with Other Machine Learning Algorithms  
The performance of the proposed method was compared with other traditional methods, such as support vector machine (SVM), random forest (RF), decision tree (DT), k-means clustering (k-MC), and Gaussian mixture models (GMMs). The bearing fault of H-SS-FS was selected, and the diagnosis was performed. The parameters of each method were tuned optimally for proper comparison with the proposed method.  
In the case of SVM, the accuracy is affected by three factors: the threshold function, the cost parameter (C), and the kernel function. In this study, radial basis function (RBF) was selected. The cost parameter was tuned through programming by selecting the optimum ranges. Based on these factors, an optimal hyperplane was selected, and data training was carried out for diagnosis. Furthermore, in the cases of DT and RF, considering the bearing fault feature selection, trees with branches and leaf nodes were constructed. Each node represented a feature (attribute), each branch represented a decision (rule), and each leaf represented an outcome (categorical or continuous value). The constructed DT reached its optional depth depending on parameter n, which was selected as 5 in the present study. For the case of RF, multiple DTs are constructed and, in the present study, three classes of DTs were selected and framed to form the RF. Between the three classes of DTs, selection of the variable was performed randomly, and the major voting was performed using the tree-bagging function to carry out the diagnosis. Similar optimal actions were executed for k-means clustering (k-MC) and Gaussian mixture models (GMM).  
The results summarized in Table 2 show the proposed CNN model had the highest accuracy rate when compared to other traditional methods. Its superiority arose from two main aspects: (1) its proper optimization reduced the error in the training process and (2) the constructed deep learning structure could be modified flexibly during diagnosis.  
\begin{table}
\begin{tabular}{c c} \hline
**Diagnosis Method** & **Accuracy Rate (\%)** \\ \hline Proposed method using CNN & 83.08 \\ Support vector machine (SVM) & 81.29 \\ Random forest & 71.40 \\ Decision tree algorithm & 69.29 \\ K-means Clustering & 48.32 \\ Gaussian mixture models (GMMs) & 31.67 \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of diagnosis results.