esa-sceva/satcom-chunk-collection huggingface dataset:
# Microencapsulation of Polyphenols and Their Application in Food Technology
## 3 Polyphenols: Properties and Significance in Food
### Application of Polyphenols in Food Technology
#### 3.3.2 Antioxidant Activity  
The antioxidant properties of polyphenols are pivotal in preventing oxidative degradation, a primary factor contributing to the loss of food quality during storage. By scavenging free radicals and inhibiting oxidation reactions, polyphenols help preserve the sensory attributes, nutritional value, and safety of various food products [101]. Lipid oxidation is a significant cause of rancidity in edible oils and fatty foods. Polyphenols, such as hydroxytyrosol from olive leaves and extracts from rosmary, effectively inhibit oxidative rancidity in products like sunflower oil and olive oil, thereby maintaining flavour stability and extending shelf life. These natural antioxidants serve as an alternative to synthetic preservatives, aligning with consumer preferences for clean-label products. In meat products, polyphenols play a dual role by preventing lipid oxidation and preserving colour and flavour. Oxidation in meat leads to off-flavours, discolouration, and nutrient loss; polyphenols counteract these effects, ensuring product stability over time. Similarly, in dairy products, polyphenols prevent the oxidative spoilage of milk fats, maintaining sensory quality and reducing waste. The efficacy of polyphenols as antioxidants can be enhanced through synergistic combinations with other natural compounds, such as ascorbic acid or tocopherols [102]. Additionally, encapsulation technologies protect polyphenols from degradation during processing and storage, allowing for their controlled release and sustained activity in food matrices. The integration of polyphenols into food systems is anticipated to expand with advances in extraction and formulation techniques. Research is focusing on tailoring polyphenol blends to specific food applications, optimising their functionality while meeting regulatory and consumer demands for natural, sustainable additives [103].
# Review of Image-Based 3D Reconstruction of Building for Automated Construction Progress Monitoring
## 6 Discussion
### Future Development Trends  
#### 6.4.1 Combination with VR and AR  
In recent years, virtual technology has not only achieved success in the game industry, but also promoted the development of other fields. The virtual technology is a computer simulation system that can use a computer to generate a simulation environment to immerse users in the environment. In the field of AEC, VR technology can provide realistic location and condition of structure element for remote construction monitoring [40; 41], while AR technology can superimpose virtual BIM models on the real world to achieve a sensory experience beyond reality. For example, the electromechanical equipment models that need to be installed in the future can be projected into the screen to guide the on-site construction and check whether the construction progress is consistent with the BIM design at any time; information on construction procedures, issues, and attributes can be projected to the front by recognizing the scene and gestures of the wearer; the pipeline can be projected to the ground and walls for precise excavation in the renovation project.  
In the 3D reconstruction scene, VR and AR also have application value. They can be used to align as-planned models with as-built models and observe which areas have not been reconstructed on the actual construction site. For example, [PERSON] et al. [40] proposed framework for integration of BIM and interactive game-like immersive VR interfaces, which empowered project managers and stockholders with an advanced decision-makingtool. [PERSON] et al. combined daily images and 3D/4D models to create D4 AR models [3, 33, 35, 72, 73]. In addition, they aligned the as-built point clouds with the BIM model for automated progress deviation measurement [35]. Similarly, in a virtual environment, other forms of data can be integrated with BIM, such as images, laser point clouds, RFID, etc.  
For progress monitoring, the combination of real-time 3D reconstruction and VR can make managers in the office as if they were in the construction site, which provides a new way for remote management of progress, quality, and safety, especially for the inspection of dangerous areas.
## 3 Results and Discussion
### Volatile Compound Analysis  
The volatile aroma compounds of seven green huajiao were separated and identified using HS-SPME and GC-MS, and the results are shown in Figure 5, Supplementary Figure S1 and Supplementary Table S2. A total of 69 volatile aroma compounds were detected in the seven green huajiao samples, including 55 common fractions (Figure 5A). Figure 5B shows significant differences in the content and composition of the volatile compounds in the seven green huajiao samples (_p_ < 0.05), and the distributions of total volatile contents ranged from 74,006.16 mg/kg (G2) to 94,995.47 mg/kg (G7). G3 and G5 from the same city (Panzhihua, Sichuan Province) had higher volatile contents of 82,648.33 mg/kg and 88,871.57 mg/kg, respectively. The volatile substances of green huajiao were categorized into six groups, including 11 alcohols, 25 terpen, 10 aldehydes and ketones, 7 esters, 7 hydrocarbons and 9 other substances. Alcohols and terpenes are the most important volatile compounds, and the percentage of these two compounds in huajiao samples is over 90%. Alcohols have the highest percentage content of 68.50% to 78.79%, followed by terpenes, ranging from 12.97% to 24.33%. The contents of aldehydes and ketones, esters and hydrocarbons were 1.15-2.52%, 1.41-4.15% and 0.48-0.91%, respectively. A previous report agreed with this result and showed that the content of alcohols and terpenes was higher in the volatile compounds of green huajiao, while the content of esters was higher in red huajiao [33]. Compared with red huajiao, the total volatile matter content of green huajiao from Sichuan and Chongqing regions was high, the proportion of alcohols was significantly high, and the proportion of esters was significantly low [17; 18].  
Figure 5C shows a heatmap of the content of 69 volatile compounds in the green huajiao samples. The volatile compounds with highest contents in the huajiao samples were linalool, germacrene D, trans-caryophyllene, linalyl acetate, sabinene, limonene and others. Other studies also reported that limonene, linalool and sabinene are the major volatile compounds in different varieties of huajiao and play an important role in the aroma characteristics [1; 5]. In addition, a previous study reported that \(\alpha\)-pinene and sabinene are the major volatile compounds in green huajiao [34]. It is assumed that these differences are due to the different cultivation areas, geographical and climatic conditions, varieties, processing and extraction technologies, etc.
# Prediction and Optimization of Open-Pit Mine Blasting Based on Intelligent Algorithms
## 5 Results and Discussion
### Modeling for Prediction
#### 5.1.1 GA-LSSVM Model  
To enhance the prediction accuracy of the GA-LSSVM model, multiple iterations were conducted to determine the maximum number of iterations (Imax) and the population size (npop). RMSE was employed as both the fitness function and the evaluation metric for the predictive model. There exists a negative correlation between prediction performance and RMSE; smaller RMSE values indicate better predictive efficacy. Multiple iterations were performed, as illustrated in Figure 5. After 100 iterations, the minimum RMSE value corresponded to a population size of 60, and stability was achieved after approximately  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(L_{m}\) (CNY) & \(\rho_{r}\) (t/m\({}^{3}\)) & \(E_{m}\) (CNY) & \(N_{D}\) (Per) & \(D_{m}\) (CNY) & \(R_{1}\) (CNY) & \(R_{2}\) (CNY) \\ \hline
31.78 & 3.3 & 10.5 & 1.13 & 35 & 0.042 & 0.165 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The constant parameters of the blasting cost model.  
Figure 4: The optimization process of MOPSO.  
70 iterations. Therefore, the final choice for \(\mathrm{n_{pop}}\) was determined to be 60. Taking into account iteration redundancy, the value of \(\mathrm{I_{max}}\) was set to 90.  
When the blast fragmentation (\(\mathrm{P_{50}}\)) is taken as the output indicator, the optimal values for \(\gamma\) and \(\sigma^{2}\) are 468.69 and 287.23, respectively. The regression fitting effect of the final GA-LSSVM model on the sample data is shown in Figure 6. We can see that the predicted values of the model are very close to the test values, with an RMSE value of 1.805. This indicates that the GA-LSSVM model has a good fitting effect on \(\mathrm{P_{50}}\).
# Exploiting Autoencoder-Based Anomaly Detection to Enhance Cybersecurity in Power Grids
## 3 AE-GRU Anomaly Detection Framework
### Hybrid AE-GRU  
In this section, we introduce the hybrid AE-GRU model, which combines the advantages of AE and GRU architectures to improve the cybersecurity measures in smart grids. The AE-GRU model is designed to encode time-series data effectively, aiming to mitigate the potential cyber threats. The AE-GRU model integrates two stacked GRUs within both the encoder and decoder components (Figure 3). This configuration enables the model to capture the intricate temporal patterns and anomalies present in smart grid data. By leveraging the sequential nature of GRUs, the model can efficiently extract features from the  
Figure 2: General representation of a GRU.  
input data and reconstruct normal traffic patterns. The primary objective of the AE-GRU model is to minimize the reconstruction error, typically measured using metrics like Binary Cross-Entropy (BCE) [31]. This metric evaluates the dissimilarity between the original input data and the reconstructed output. By reducing the reconstruction error, the AE-GRU model enhances its ability to accurately capture and represent the underlying structure of the smart grid data.  
The integration of the AE-GRU model represents a proactive measure to enhance the cybersecurity in smart grids. By effectively encoding the time-series data, the model can be used in conjunction with semi-supervised anomaly detection methods to identify potential cyber threats, such as malicious attacks or anomalies in system behavior.
# Theoretical and Numerical Research on High-Speed Small Refrigeration Twin-Screw Compressor
## 4 Numerical Model
### Independence Verification  
The stationary domain, which includes the suction and discharge ports, is meshed using ANSYS Meshing 2024R1 with tetrahedral-dominated elements, featuring boundary layer refinement and interface densification for improved accuracy. For the rotating domain, TwinMesh produces high-quality structured grids through a systematic process: first, a 2D cross-sectional mesh is created via node mapping between the screw rotor profile and casing contour, followed by the development of the 3D mesh through axial interpolation while maintaining rotational periodicity. This meshing approach guarantees optimal grid resolution for both stationary and rotating components. Figure 6 illustrates the detailed fluid-domain grid of the screw compressor. The computational study was conducted using ANSYS CFX 2024R1 to simulate the screw compressor model.  
A grid independence study was conducted using the 3000 rpm rotor domain mesh. The investigation systematically varied the radial, circumferential, and axial node counts while monitoring changes in the intake mass flow rate and gas power output. The comparative results for different grid configurations are presented in Table 4. The analysis revealed that radial and axial node distributions exhibited a significant influence on the solution accuracy within the screw rotor domain. Based on the convergence criteria, Configuration 7 was selected as the optimal mesh configuration for the 3000 rpm rotor simulation. For the other rated rotor configurations, the mesh parameters were determined through geometric scaling based on the ratio of the cylinder diameter and rotor length relative to the 3000 rpm reference configuration.  
The computational grids are generated using TwinMesh 2021, where the temporal resolution is intrinsically linked to the angular step size of the cross-sectional mesh. For the current simulation, an angular resolution of \(1^{\circ}\) is implemented, corresponding to the generation of a complete structured mesh and subsequent iterative computation at each degree of rotor rotation until the convergence criteria are satisfied. This \(1^{\circ}\) angular step  
Figure 5: Control volume of the fluid-domain screw compressor.  
Figure 6: Grids of the fluid domain for the screw compressor.  
size is consistently maintained across all rotor speed configurations to ensure temporal resolution uniformity.
## 4 Simulation Results
### Simulation Settings
#### 4.2.3 The Communication Cost Analysis  
In this section, we compare the proposed DRSL algorithm with anchor-free approach on communication cost. Communication cost is the transmission data size which is needed for the localization procedure. Since nodes in DUANs always have limited power, meanwhile, communication is one of the most important part of power consumption during the localization procedure. Communication cost is an essential performance metric for the localization algorithm.  
The sending message in this algorithm usually has two parts: a packet-header and a packet body. We design the packet structure as in Figure 14  
Assume that there are \(M\) nodes deployed in the network. The time duration of the self-localization procedure is \(T\). The test period is \(T_{s}\). The estimation period is \(T_{0}\)  
\(1D_{s}\) is the node's ID that sends message, \(log_{2}M\) bits;  
_Time_ is the time stamp, 2 bytes;  
_Type_ is the message's type, 2 bits  
there are 3 types of messages which represent the packet body's length.  
(1) \"Hello\" packet: \"Hello\" packet contains the sending anchor's coordinate. Thus, the length of \"Hello\" packet is 4 bytes.  
(2) \"Range\" packet: \"Range\" packet only triggers the range measurement process. Thus, the length of \"Range\" packet is 0 bit.  
(3) \"Error\" packet: \"Error\" packet contains the receiving node's ID and the least square error. Thus, the length of \"Error\" packet is \(log_{2}M+10\) bits.  
(4) \"Reference\" packet: \"Reference\" packet contains the reference node's ID and coordinate. Thus, the length of \"Reference\" packet is \(\frac{1}{8}log_{2}M+4\) bytes.  
Figure 14: The packet structure.  
Figure 13: The localization error analysis of different algorithms.  
The communication cost \(C\) is as shown in Equation (15)  
\[C=\frac{TT_{0}}{T_{s}}\left[(3M-4)log_{2}M+(2^{10}+14)M+(2^{9}-1)6\right]\quad bits \tag{15}\]  
Increase the number of nodes in the network from 10 to 50. The time duration of localization \(T\) is set to be \(1000t_{0}\) and the estimation period \(T_{0}\) is set to be \(100t_{0}\) and the test period \(T_{s}\) is set to be \(200t_{0}\). The comparison between the DRSL algorithm and the MASL anchor-free approach on communication cost is shown in Figure 15.  
As shown in Figure 15, the communication cost increases with the number of nodes in the network. This is reasonable since more nodes mean more messages are needed to transmit during the localization procedure. Furthermore, we can see that the communication cost of DRSL algorithm is lower than that of MASL algorithm with no anchor. This is because the proposed DRSL algorithm does not need to do a complete localization process during each time period and does not always have a high sampling frequency which reduce the communication cost of the localization procedure obviously.
# Data-Driven Fault Detection and Diagnosis: Challenges and Opportunities in Real-World Scenarios
## 2 Research Questions and Contributions
The validation of novelty detection approaches on five distinct datasets from different industrial contexts, with a particular focus on the tasks performed on streaming data leading to the reduction in the quantity of data and the automatic labeling of collected datasets.
# Nutritional and Functional Properties of Gluten-Free Flours
###### Abstract  
This study characterized and compared 13 gluten-free (GF) flours (rice, brown rice, maize, oat, millet, teff, amaranth, buckwheat, quinoa, chickpea, gram, tiger nut, and plantain) for their nutritional and functional properties. For all GF flours investigated, starch was the major component, except for gram, chickpea, and tiger nut flours with lower starch content (<45%), but higher fiber content (8.8-35.4%). The higher amount of calcium, magnesium, zinc, potassium, phosphorus, similar values for iron and lower content of sodium in gram, makes this flour a good alternative to chickpea or other GF flour to develop healthier food products. Amaranth flour had a high protein digestibility, while tiger nut and millet flours were less digestible. Gram, chickpea, quinoa, buckwheat, and oat flours fulfilled amino acids recommendation for daily adult intake showing no limiting amino acid. Total polyphenic content and antioxidant capacity showed higher values for buckwheat, followed by quinoa and maize flours. Gram, chickpea, maize, and quinoa flours are good candidates to improve health conditions due to lower saturated fatty acid content. The findings of this study provide useful insights into GF flours and may contribute to the development of novel gluten-free products like bread, cookies, or pasta.  
gluten-free flour; gram; plantain; chickpea; tiger nut; pseudo-cereal; oat; millet; teff; rice +
Footnote †: journal: Applied Sciences
## 3 Methodology  
### Sample  
This study utilizes a dataset derived from the Orbis database by Moody's (New York, NY, USA), focusing on the textile industry, specifically within the Standard Industrial Classification (SIC) code 22 (Textile Mills). The choice to concentrate on SIC code 22, which represents businesses primarily engaged in the textile mill sector, was driven by the desire to closely examine the core activities most relevant to our research on cost stickiness. Given the textile industry's diversity, this focus aimed to ensure a manageable scope for in-depth analysis within a more homogenized group of firms, despite the potential for variability in cost dynamics and business models across the broader industry variety.  
Firms classified under SIC code 22 are engaged in various stages of textile production, from raw material processing to the creation of finished fabrics. Given the specialized nature of their operations, these companies often exhibit complex supply chain structures. However, compared to the entire textile and garment industry range, companies within SIC code 22 might be considered to have relatively shorter supply chains. This is because their operations are more concentrated on specific stages of textile production, rather than spanning from raw material sourcing to the retailing of finished garments.  
The propensity for shorter supply chains in these firms suggests a direct link to cost stickiness, primarily due to the necessity of maintaining operational capacity (such as machinery and skilled labor) despite fluctuations in demand. However, it is essential to recognize that even within the scope of SIC code 22, the degree of supply chain complexity can vary significantly among firms, influenced by factors such as the diversity of their product lines, their sourcing strategies, and their customer bases. Therefore, while we might initially categorize these firms as having shorter supply chains, the specific characteristics and strategic choices of each company could lead to variations in how supply chain structures impact cost behavior.  
The dataset encompasses firms located in the Nordic countries (Denmark, Finland, Iceland, Norway, Sweden) and Spain, covering the period from 2017 to 2022. The final sample consists of 809 observations, achieved through the application of winsorization, a statistical method aimed at moderating the effects of extreme observations. By limiting extreme values to better match the rest of the dataset, winsorization aids in reducing the distortion caused by outliers in the analysis. Specifically, we used the \"winsor\" command in Stata 14 to adjust values at both ends of our dataset to the nearest values within a predetermined percentile range. For this study, we set the threshold at the 1% level, meaning we replaced the lowest and highest 1% of data points with the closest available values at the 1 st and 99 th percentiles, respectively. This technique is crucial for enhancing the stability of variance across our data, thereby improving the accuracy and reliability of our statistical estimates, without significantly modifying the overall distribution of the data.
# Progress in THz Rectifier Technology: Research and Perspectives
### THz Diode Technology: I-V Characteristics and Figures of Merit (FOM) for MIM Diode  
The parameters that indicate the performance of an MIM diode are referred to as figures of merit (FOM) (asymmetry, nonlinearity, and responsivity). These parameters, combined with current density, turn-on voltage (TOV), and zero-bias resistance (ZBR), are influenced by several factors such as the combination of metal and insulator, the thickness of the insulation, and the barrier height between the M-I interface.
## 2 Materials and Methods
### UHFUS Acquisition Protocol and Image Postprocessing  
Intraoral UHFUS scans were performed with a standardized acquisition protocol by two examiners using Vevo MD (VisualSonics, Toronto, ON, Canada) equipped with a 70 MHz probe. Training and calibration between the examiners were performed on patients who were not part of the study to standardize UHFUS scan performance, image acquisition,and image interpretation prior to study beginning. Image analysis was conducted on DICOM format images processed with Horos software (Version 3.3.6, Annapolis, MD, USA). Data collection started when Cohen's kappa value between examiners was >0.90.  
A disposable sterile probe cover was employed for the management of cross-infections in order to avoid the direct contact between the oral mucosa and the probe. Sterile ultrasound gel was employed as a couplant material to improve ultrasound beam transmission.  
The standardized UHFUS acquisition was conducted by placing the probe perpendicular to the mucosal surface with minimal pressure on the tumor.  
B-mode axial and longitudinal acquisitions were performed using a standardized preset. Scan depth and focus position were adjusted to optimize the scan, while other parameters (gain, time gain compensation, dynamic range, mechanical index, and thermal index) were kept constant throughout the UHFUS examination.  
The parameters of TT, DOI, and echogenicity were measured on B-mode axial images. Histology was set as benchmark for TT and DOI evaluation.  
TT (expressed in mm) was measured as the distance between the lesion's surface and the deepest margin in the tissue. DOI (expressed in mm) was measured as the distance between the normal mucosal surface and the deepest margin of the lesion in the tissue.  
Echogenicity was evaluated through grey scale analysis of a selected region of interest (ROI). The average grey level distribution value, minimum value, maximum value, and standard deviation were retrieved and compared.  
Color Doppler was employed for the assessment of vascularization, using a setting scale of \(\pm\)1.9 cm/s to detect blood vessels with a low flow rate. A qualitative analysis of the degree of vascularization was performed by assigning each lesion a score from 1 to 3. Score 1 corresponded to vascularization present in <30% of the lesion, with perilesional pattern; score 2 indicated vascularization between 30% and 60% of the lesion, with predominantly intralesional pattern; finally, score 3 indicated intense perilesional and intralesional vascularization in >60% of the lesion, with the presence of aliasing phenomena.