esa-sceva/satcom-chunk-collection huggingface dataset:
# Applications of Plant Polymer-Based Solid Foams: Current Trends in the Food Industry
## 2 Food Industry Applications
### Plant Polymer-Based Foams as Edible Materials
#### 2.1.1. Saponins  
Saponins (Figure 2) are amphiphilic glycosidic secondary metabolites produced by a wide variety of plants. Soapwort (_Saponaria officinalis_) is a natural source of saponins, which are known for their surface properties and capacity to form foams [20]. [PERSON] and [PERSON] [21] studied the chemical and physical properties of soapwort extract as well as its foaming properties under common food processing conditions, such as in the presence of sodium chloride and sucrose. The saponin extract exhibited high foaming capacity and stability. In addition, low pH did not significantly affect foam properties, while heating the extract improved the foaming capacity and stability. Testing the saponin extract at concentrations below 30% ethanol slowly lowered its foaming capacity. Meanwhile, heating increased foam capacity and stability. All these results confirm that the saponin extract from soapwort is a potential alternative foaming agent for use in several food systems, especially in hot food applications.  
For example, the soapwort concentrate and extract powder enriched with saponin developed by [PERSON] and [PERSON] [22] through optimized extraction, enrichment, and drying processes was used as a foaming agent in the production of traditional Turkish delight. The foams produced from saponin concentrate and extract powder showed good stability and  
Figure 1: Examples of sources of plant polymers utilized to produce solid foams.  
Figure 2: Saponin structure: (**a**) Trit terpenoid type; and (**b**) Steroid type.  
resistance to heating process. In terms of chewiness, taste, and texture, they had similar results to commercial soapwort extract at the same soluble solid content, though these foams scored slightly lower than the latter. However, a positive aspect is that taste and odor problems caused by microbial proliferation, along with quality losses due to the lack of standardization in the extracting process, are less frequent than when commercial soapwort extract is used, which creates an opportunity for industrial production. Characterizations of the foam structure, which might help to improve the final product were not systematically conducted, thus further research is required for understanding the textural analysis results.
# Black Loaded Columns--State of the Art Review
## 4 Discussion and Conclusions  
### Experimental Testing  
Most of the experiments were conducted for the scenario of an attack by an auto-bomb located near a bridge or building column. Experimental tests on building columns are mostly full-scale, while tests on bridge columns predominate on scales of 1:3 and 1:4, due to the high cost of performing such experiments, the need for trained personnel to handle explosives, large quantities of explosives, and the field where such tests can be carried out.  
Experiments showed that even a minimal increase in the cross-sectional dimensions of a column could favorably affect the behavior of the column under the blast load. Moreover, a minimal increase in the standoff distance reduces the impact and intensity of the blast load, and therefore it is necessary whenever possible to fence the column, increase visibility around the column, and reduce its accessibility. In addition to the dimensions of the column, the shape of the column plays a significant role. Circular columns retain less impulse from the blast load than square ones of the same dimensions. Squares have a larger cross-sectional area and, therefore, can better withstand shear. On the action of the contact explosion, the circular column suffered less damage than the square. However, comparing a square and a rectangular column, when the width dimension of the rectangular column is greater two and more times from depth, damage of the column is lower. The shape of the column on the blast load needs to be further investigated. In RC columns, position, quantity, anchoring, and reinforcement overlap have a great impact. Therefore, seismically designed columns have better blast load behavior than standard designed columns.  
Comparing steel jacked columns, CFST and CFDST with RC columns, all showed better ductility, less cracking of the concrete, and the absence of flying debris. However,CFDST showed the best behavior, as the inner steel tube contributes to the prevention of direct shear. With this type of column, it is important that the ratio of the cavity to the column cross-section is not greater than 0.5 and that the thickness of the steel tube is well determined (a thickness of 3.8 mm gave satisfactory results).  
Columns with Ultra-High-Performance Concrete and with various Fiber-Reinforced Polymers showed better load-bearing capacity than High-Strength Reinforced Concrete.  
The influence of axial load up to 30% of the total load capacity has shown a favorable effect on the reduction in the maximum displacement, but this percentage needs to be further investigated.  
Recent research applies smart aggregates to measure internal cracks and internal damage because the column can be damaged and reduced load-bearing capacity without being visible on the outside. Therefore, post-blast tests are performed to determine the residual strength and ductility of the column.
# Granular Description of Uncertain Data for Classification Rules in Three-Way Decision
## 3 Granular Uncertainty Description
### Datasets with Multiple Classes  
As we know, the above description deals with the simple case in which there is only one uncertain region between two classes. In real-world scenarios, it is very common that datasets have multiple classes. For example, a dataset contains \(c\) categories of data points, there will be a maximum of \(c(c-1)/2\) uncertain regions. Therefore, we extend the study on constructing information granules to describe all possible uncertain regions. To implement this idea of forming better-performed classification rules in multi-class datasets, we propose to utilize the \"one-vs-one\" method in uncertain region description first. Then, we combine the constructed uncertain granules with a given voting algorithm to complete the formation of multi-class classification rules. The detailed processes are described in the following steps.  
Step 1: Define a score function as \(S\)(*). This is a discrete function whose domain consists of all class labels, such as {Class\({}_{1}\), Class\({}_{2}\),..., Class\({}_{c}\), UR} where UR represents uncertain regions. Initial values of these functions are set to 0;  
Step 2: Select two classes of data, e.g., Class\({}_{i}\) and Class\({}_{j}\), \(i\
eq j\). A granular descriptor \(\Omega_{ij}\) is constructed to describe the uncertain region between two neighbor classes. For example, the processes in Section 3.1 are implemented for one-dimensional data, and those in Section 3.2 are implemented for high-dimensional data;  
Step 3: New classification rules considering \(\Omega_{ij}\) are formed based on (16) or (22). Then, for a testing data point \(\mathbf{x}_{k}\), its class label \(y_{k}\) can be determined;  
Step 4: Update the score functions as seen below:  
\[\left\{\begin{array}{l}S(Class_{i})=S(Class_{i})+1,\text{ if }y_{k}=Class_{i};\\ S(UR)=S(UR)+1,\text{ if }x_{k}\in\Omega_{ij};\\ S(Class_{j})=S(Class_{j})+1,\text{ if }y_{k}=Class_{j}.\end{array}\right. \tag{23}\]  
Step 5: Repeat Steps 2-4 until all pairs \((i,j)\) are studied. Meanwhile, the final values of \(S(\text{*})\) are calculated. Consequently, for the testing data point \(\mathbf{x}_{k}\), its final class label will be determined by the maximum \(S(\text{*})\).
# Experimental Study on Physical and Rheological Properties of Trinidad Lake Asphalt Modified Binder
## 2 Materials and Methods
### Rheological Properties Tests  
Sinusoidal oscillation loading was utilized to undertake the dynamic shear rheological test (DSR). The DSR measures a sample's complex shear modulus (G\({}^{\bullet}\)) and phase angle (\(\delta\)). The first parameter can be considered as the sample's total resistance to deformation when shear loaded, while the latter is the lag between the applied shear stress and the resulting shear strain. Typically, the higher the G\({}^{\bullet}\) value, the stiffer the binder is (able to resist deformation), and the lower the \(\delta\) value, the greater the elastic portion of G\({}^{\bullet}\) is (able to recover its original shape after being deformed by a load).  
However, based on the SHRP specification, Bahia [29] suggests the use of these two parameters (G\({}^{\bullet}\), \(\delta\)) at a fixed temperature and frequency of testing. Since the viscosity of the binder changes with temperature and the elastic properties of the binder change with the frequency of loading, the above parameters have been suggested to be ineffective in predicting the rutting performance of binders, especially in the case of modified binders [30; 31; 32; 33]. Rutting parameter G\({}^{\bullet}\)/sin\(\delta\) does not fully evaluate the delayed elasticity--this parameter measures a short loading time, which is representative of traffic loading. For some binders, this process is long, which leads to underestimating the resistance to permanent deformation. A parameter that includes both features is the zero shear viscosity (ZSV) [34]. The concept of ZSV was introduced by [PERSON] [35] to evaluate the contribution of binders to the rutting resistance of bitumen pavements. ZSV is a suitable indicator to evaluate the partial contribution of the binder to the rutting resistance of bitumen pavement layers. Thus, in this study, the ZSV of TLA-modified binders was calculated using different models and test methods.  
The available literature describes many test methods for the experimental determination of ZSV of binders based on the use of DSR under different experimental conditions, both oscillatory and creep mode [36; 37; 38]. In this study, ZSV was determined with two different test modes: frequency sweep test and creep test. Following the preparation of the modified binders, both rheological tests were conducted using DSR for original (unaged) samples. For both rheological tests, the Grubbs test was carried out to detect outliers in a univariate data set assumed to come from a normally distributed population. To calculate the ZSVs of the modified binders, the Cross model was fitted to measure the complex viscosity (\(\eta^{\bullet}\)).
## 3 Proposed System Methodology
### Multi-Person Tracking
#### 3.3.3 Multi-Person Tracking  
The goal of person tracking is to establish correspondence between individuals across frames. Thus, to establish correspondence between persons in frame \(t\) and frame \(t+1\), we calculated the position and velocity of every detected human silhouette in all frames. In our model, we assumed that people can enter or leave the scene, thus, for temporally fixing of all humans across frames, the position of each human silhouette was located and locked by assigning a unique integer ID, which was fixed to that particular silhouette in all frames. The states of all the predicted persons in frame \(F_{t}\) were stored in a structure and matched with the states of frame \(F_{t\ +1}\), while the detected fixed human silhouettes were tracked using the Jaccard similarity index.  
\[S_{t}=\sum_{i=1}^{n}I_{Li} \tag{13}\]  
While using data association and cross-correlation as a cost function, detected and predicted persons were associated in consecutive frames, as represented in Figure 10. The root steps involved in multi-person tracking are illustrated in Figure 11.  
Figure 11: Key steps involved in multi-person tracking.  
Figure 10: Sample frames of multiple human silhouette-fixing and tracking at different time intervals.
# The Current Development of Structural Health Monitoring for Bridges: A Review
## 5 Damage Identification Methods
### Modal Parameter-Based Methods
#### 5.1.3 Mode Parameter Combined Methods  
To overcome the difficulty of SDI based solely on mode shape or frequency, some scholars have studied methods of combining multiple modal parameters. From a statistical perspective, [PERSON] et al. [130] proposed two subspace-based methods that utilize natural frequencies and mode shapes to consider uncertainty factors such as noise, limited data, and non-stationary excitations. The proposed methods were validated to achieve Level 3 SDI in the damage test of the S101 Bridge in Austria. [PERSON] et al. [131] proposed a method combining natural frequency variation and CMS for damage detection. They used two vectors, the measured frequency change value and the curvature mode of the intact structure, to plot the damage position and damage coefficient function. The practicability of the proposed method was verified by a numerical simulation and a laboratory model of a cantilever beam. From the perspective of the neural network, [PERSON] et al. [132] took mode shape and mode curvature differences, respectively, as the input of CNN training samples to study their damage location accuracy. The results showed that the damage location accuracy was higher when the mode shape was used as the input. [PERSON] et al. [133] conducted theoretical and experimental modal tests for crack identification in cantilever beams by using CMS and natural frequency. Firstly, the governing equation of the transverse motion of the beam was established, and the frequency and mode shape of the damaged beam were calculated. Then, the first four CMS were drawn using these modal parameters. Similarly, the crack location and damage coefficient were obtained from the intersection of the curves. This method had received ideal recognition results in the test.  
In most cases, the modal parameters of the structure are the basis for SDI. To maximize the effect of SDI, modal parameters must be extracted with greater precision and utilized in a more complete manner. The aforementioned SDI methods based on modal parameters are summarized in Table 3.
## 5 Meteorological Prediction Test
### Models Used in the Test  
The models employed in the comparative experiment are the Linear Regression (LIN) [96], Gaussian Process (GAU) [97], and Multilayer Perceptron (MLP) [98]. The settings used in these models were: Linear Regression: attributeSelection: M5 Method, batchsize:100, ridge: 1.0 \(\times\) 10\({}^{-8}\).  
Multilayer Perceptron: batchsize = 100, hiddenlayer = a, learningRate = 0.3, momentum = 0.2, seed = 0, trainingTime = 500, validationSetSize = 0, validationThreshold = 20.  
Gaussian Process: batchsize = 100, Kernel: polykernel, noise = 1.0, numDecimalPlaces = 2, seed = 1 are the originals maintained by the Weka software [99]. To perform the time series tests, the resources provided by the Weka Experimenter module were used, with 70% for training samples and 30% to obtain the model results. The fuzzy neural network used in this study obtained its parameters to be used in the experiments by choosing the best values in a 10 k-fold cross-validation procedure where the number of membership functions was chosen in the interval [2; 3; 4; 5], the number of bootstraps in [4; 8; 16; 32; 64], and the consensus threshold in [0.4, 0.5, 0.6, 0.7]. After preliminary procedures, the model was used in the final tests with two triangular membership functions, 16 bootstrap replications, and a consensus threshold of 0.5.
## 2 Material and Methods  
### Study Design  
A retrospective, single-center study was conducted, enrolling 24 male COVID-19 patients admitted to the ICU of Hospital de Sao Jose, Unidade Local de Saude de Sao Jose, between November 2020 and July 2021. For all patients, demographic and clinical data, routine laboratory parameters results, and whole blood samples were collected. Patients were divided into three groups of eight individuals each based on disease severity: Group A included less severe patients who were discharged from the ICU without requiring IMV; Group B with patients who required IMV and were also discharged from the ICU; and Group C with those who needed IMV and died in the ICU. In COVID-19, severe disease is typically classified as requiring hospitalization and supplemental oxygen, while critical disease involves the need for noninvasive or mechanical ventilation [10]. Hence, patients from Group A were considered to have a less severe condition than those from Group B. In Groups B and C, all required IMV, being solely differentiated based on their outcomes, specifically, whether they were discharged from the ICU or deceased. The three groups' demographic, clinical, and laboratory data were compared concerning the outcomes \"need for IVM\" (A vs. B) and \"mortality in the ICU\" (B vs. C and A + B vs. C) in order to develop predictive models (Figure 1).  
Ethical approval was granted by the hospital's Ethics Committee (project number 1043/2021, 20 May 2020), and informed consent was obtained from all patients or their family members prior to enrolment. COVID-19 diagnosis was confirmed with real-time polymerase chain reaction tests for SARS-CoV-2. Patients' clinical data were obtained from the hospital's electronic medical record system and properly anonymized. Serum samples were collected within the first 14 days of ICU admission. Due to limitations in sample availability and the very low number of female patient samples, only male patients were included to ensure the robustness of the models by minimizing confounding factors. Consequently, it is important to acknowledge that the inclusion of only male patients and the small sample size of 24 individuals are limitations of the study that should be considered upfront, as they can affect the generalizability of the findings.
# Assessing Energy Efficiency in Desktop-Size FFF 3D Printers
## 1 Introduction
The growth of the 3D printing industry itself further underscores the need for energy-efficient practices in additive manufacturing. Rapid market expansion, driven by increased availability of printing devices and novel applications for 3D printing, has raised the market value from USD 3.07 billion in 2014 to USD 8.68 billion in 2017 [2], with projections suggesting a nearly USD 52 billion valuation by 2028 [2]. As the technology becomes more widespread and is increasingly utilized in household settings, individual 3D printer users are expected to place greater emphasis on energy consumption in the additive manufacturing process. The energy utilized in the FDM additive manufacturing process can be categorized into two primary types: primary energy, often referred to as direct printing energy, which is used in forming the filament into its designated shape [12], and secondary energy, or indirect printing energy, which supports operations such as bed heating, print head cooling, and powering the XYZ axis drives. As 3D printing technology grows in popularity, a comprehensive analysis of technical parameters can substantially reduce its energy demands. Research on energy efficiency reveals that insulating the hot end can reduce energy consumption by between 30.63% and 33.8%, while utilizing an enclosed print chamber provides an average power reduction of 18%. Additionally, optimizing infill patterns--such as using \"lightning infill\"--can decrease material usage by 51%, which directly benefits both energy efficiency and material savings [13]. Further studies indicate that layer thickness plays a critical role in total energy consumption; although thicker layers can increase process speed, they may also compromise dimensional stability and print quality. Print speed, closely correlated with build time, is another major factor in energy demand, as higher speeds can lead to increased energy consumption. This finding underscores the need for a careful balance between print quality and efficiency [14; 15]. Optimizing energy use in FFF 3D printing can significantly reduce its carbon footprint, addressing both the rising costs of energy and the growing regulatory pressures on CO2 emissions. Implementing energy-efficient practices, such as adjusting infill density and print speed, lowers the energy required per print, thereby reducing CO2 emissions generated by electricity production. Furthermore, using efficient materials and configurations minimizes waste and enhances the sustainability of the FFF process [5; 16]. These optimizations are particularly critical as environmental policies worldwide increasingly mandate reductions in carbon emissions. Regulations often require industries to report and limit carbon output, motivating manufacturers to adopt more sustainable production methods. In this context, optimizing 3D printing processes offers both environmental and economic advantages: companies not only save on energy costs but also meet compliance with regulatory standards, supporting a transition toward greener manufacturing technologies [17; 18].
# Towards Flexible Distribution Systems: Future Adaptive Management Schemes
## 4 Market Schemes for Adaptive Control and Management Methods Collaborative TSO-DSO Use
### Local Trading and Markets in Distribution Networks
This could be realized, for example, by (i) dynamic real-time tariff using an increased number of MV and LV sensor measurements, cloud-/edge server solutions, big data analytics, etc. ; (ii) distribution tariff structures that support the whole system cost minimization; and (iii) the possibility to take into account location (voltage level) as part of the distribution tariff. In [105], the authors made an assessment of the different mechanisms (time of use, ToU tariffs, dynamic tariffs, finer geographical granularity, bilateral agreements, and local flexibility markets) for unlocking the flexibility potential of demand. The used criterions included (i) effectiveness on congestion management, (ii) ease of implementation, (iii) market compatibility considering TSO, DSO, and feeder viewpoints, as well as (iv) impact on system balancing in general. Based on the assessment, local flexibility markets effectiveness, for example, on (i) and(iii) is high for TSO, DSO, and feeder, (ii) is medium, and(iv) is neutral. The Danish Energy Authorities have also started the market design for local flexibility markets focusing on ensuring power system security via market-based solutions, enhancing the role of flexibility from prosumers and other new actors, and the DSO's role development in the market [107].