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
#### 3.3.6 Structural Improvement and Texture Modification  
Polyphenols influence the structural properties and texture of food products by interacting with proteins, fats, and polysaccharides. These interactions can result in significant improvements in the stability, texture, and overall quality of a wide range of food products [121]. One of the key functions of polyphenols is their ability to stabilise emulsions, preventing phase separation and enhancing the homogeneity of emulsified products. For instance, polyphenols from grape seeds and green tea have been shown to improve the stability of oil-in-water emulsions in dressings, mayonnaise, and sauces. This stabilising effect ensures that the emulsion remains uniform, contributing to both the sensory appeal and shelf life of the product [122]. Polyphenols also contribute to the structural integrity of gel-based foods, such as jellies, jams, and desserts. When combined with polysaccharides like pectin or agar, polyphenols can enhance gel formation, resulting in more stable and resilient gels. This effect is particularly beneficial in products that require a firm and consistent texture [123; 124]. For example, polyphenols from tea and pomegranate extracts have been found to increase the firmness of gels, ensuring they retain their shape and texture during storage. Polyphenols interact with proteins to form more stable structures, which is particularly important in protein-based gels such as those derived from egg whites. These interactions help improve the texture and consistency of gelled products, contributing to their firmness and smoothness. Studies have shown that polyphenols can enhance the gel strength of egg white-based products, which is beneficial in applications such as desserts and baked goods. In meat products, polyphenols are known to improve water retention and enhance firmness, leading to improved texture and juiciness. For instance, the incorporation of polyphenol-rich extracts from fruits such as pomegranate or grape seeds has been shown to increase the water-holding capacity of meat, thereby improving its tenderness and mouthfeel [125]. Additionally, polyphenols help reduce the loss of moisture during cooking, which is crucial for maintaining the juiciness and overall quality of meat products. With advances in food technology, the potential applications of polyphenols for structural and texture modification continue to expand. Research into polyphenol-protein, polyphenol-carbohydrate, and polyphenol-fat interactions is ongoing, with the aim of developing new formulations that can further enhance the texture and sensory properties of food products. Moreover, innovations such as encapsulation technologies are being explored to protect polyphenols during processing, ensuring their effectiveness in improving food structure and texture [126].
## 3 The Extended/Generalized Finite Element Methods (XFEM/GFEM)
### XFEM/GFEM Enrichment Functions for LEFM
#### 3.2.2 Tip Enrichment  
The second type of enrichment functions is a set of asymptotic functions, also referred to as tip enrichment functions, that allow representing the discontinuity at the crack tip or front:  
\[F_{j}\left(r,\theta\right)=\left\{\sqrt{r}\sin\frac{\theta}{2},\sqrt{r}\cos \frac{\theta}{2},\sqrt{r}\sin\frac{\theta}{2}\sin\theta,\sqrt{r}\cos\frac{ \theta}{2}\sin\theta\right\} \tag{17}\]  
where \(r,\theta\) are spatial coordinates of a polar system with its origin at the crack tip/front. These functions were introduced by Belytschko and Black [97] and form a basis that can exactly represent the analytical solution of the Westergaard problem.  
Initially [44], the use of asymptotic enrichment was limited to elements containing the crack tip/front, however in the works of Stazi et al. [104] and Laborde et al. [105] it was shown that this would lead to suboptimal convergence rates. In order to obtain the same rate of convergence as for smooth problems, the use of asymptotic enrichment in a domain of fixed size around the crack front is necessary [105; 106]. This alternative enrichment scheme was termed \"geometrical enrichment\" while the initial scheme is referred to as \"topological enrichment\". Usually, the domain where asymptotic enrichment is used is defined as the set of points whose distance from the crack tip/front is smaller that a predefined length \(r_{e}\), called the enrichment radius.  
An alternative to the enrichment functions of Equation (17) consists of using the displacement expression of the Westergaard solution directly as an enrichment function. This approach was introduced by Duarte et al. [107] and subsequently adopted in several works in the XFEM [108; 109; 110] and GFEM [111; 112] literature. This kind of enrichment results in different enrichment functions in each spatial dimension, thus in some works [113; 114] it was termed \"vector enrichment\" as opposed to \"scalar enrichment\" where the same enrichment functions are used in all spatial dimensions. As a disadvantage of this approach it is mentioned that it could complicate the implementation, especially in existing codes. On the other hand it leads to a decreased number of degrees of freedom compared to scalar enrichment and it can allow the direct estimation of stress intensity factors. Typically, to increase the accuracy of this estimation, higher order terms of the asymptotic expansion are also used as enrichment.
## 2 Regulation of CTPS Activity by GTP
### Regulatory Effects of NADH
Most interestingly, the loop L4 residues Arg 105 and Leu 109 [82], as well as the loop L13 residue Arg 429 [81], do not make any key contacts with other residues in the apo-_Ec_CTPS structure, consistent with the numerous studies that suggest that GTP binding induces substantial conformational changes in _Ec_CTPS [34; 64; 71; 75; 59] and _L_ICTPS [36]. Such changes may serve to appropriately reposition the triad residues His 515 and Glu 517 for catalysis and open the NH\({}_{3}\) tunnel [86]. [PERSON] and co-workers also noted that a 3-A wide entrance to a solvent-filled \"vestibule\" that connects the GAT active site and the GAT/syntase interface is present at the base of the GTP-binding cleft [32]. That GTP binds and occludes this entryway for exogenous NH\({}_{3}\) was supported by the inhibition of NH\({}_{3}\)-dependent CTP formation by elevated concentrations of GTP [71; 72; 75]. Furthermore, GTP could serve as a \"bung\" to prevent the loss of nascent NH\({}_{3}\) and ensure the fidelity of NH\({}_{3}\) transfer to the synthase domain. Analysis of the X-ray structure of _T_CTPS [45] suggested that the binding of ATP and UTP induce a conformational change that brings the GAT and synthase domains closer together, permitting formation of the NH\({}_{3}\) tunnel. Indeed, the various residues that have been implicated in GTP-dependent activation of the glutaminase activity (especially L109A in _Ec_CTPS) are brought closer together in the structure. This observation suggests that an even more prominent structural transition could be associated with GTP binding. Interestingly, [PERSON] and co-workers did not observe the presence of an NH\({}_{3}\) tunnel in the structures of _Ti_CTPS or the _T_CTPS:3 SO\({}_{4}\)\({}^{2-}\) and _T_CTPS-Gln complexes, leading them to conclude that _Ti_CTPS must undergo an extensive conformational change upon binding ATP and UTP [45]. Similarly, a patent NH\({}_{3}\) tunnel was not observed in the structure of _M_tCTPS [15]. Based on the site-directed mutagenesis and kinetics studies outlined above and sequence conservation, [PERSON] and co-workers [45] identified two consensus sequences associated with GTP binding and activation: residues 111-130 (residues 102-121 in _Ec_CTPS, partly shaded light green in Figure 2) in the synthase domain and residues 438-444 (residues 436-442 in _Ec_CTPS, shaded purple in Figure 2) in the GAT domain. These two consensus sequences are located 15-25 A apart in the _T_tCTPS structure, and changes in conformation upon the binding of ATP and UTP were suggested to move these sequences closer together to form the GTP-binding site (arrows, Figure 3A).
# On Simulating the Propagation and Countermeasures of Hate Speech in Social Networks
## 6 Simulation Results
### Countermeasures Simulation Results
#### 6.2.2 Deferring Hateful Content  
As Section 5.2 details, the simulations aimed at studying the effect of deferring hateful content are conducted by varying the deferring probability \(p_{defr}\) and considering that a value of 0.7 deems realistic if we take into account the state-of-the-art accuracy in recognition of hate speech [51]. The obtained results show that, compared to the education, this countermeasure is less successful in decreasing the fraction of hateful persons as can be seen on the top of Figure 9, where a value of \(p_{defr}\) as high as 0.9 still results in a fraction of hateful users that varies from the ca. 4% for the largest network size down to a fraction of ca. 2% for the smallest one. Surprisingly, we can even observe some kind of reluctance and increase for \(p_{defr}<0.5\). As the middle of Figure 9 depicts, something similar happens  
Figure 8: Effects of the education as countermeasure for different network sizes (0, 500, 1000, 2000, 5000) and depending on the \(\alpha\) parameter of the Gamma distributions as shown in Figure 5: (**Top**) fraction of hateful users; (**Middle**) fraction of hateful posts; (**Bottom**) ratio of network densities of hateful to normal users.  
with the fraction of hateful posts for the largest network size (i.e., 5000), although the main tendency for all the network sizes and probabilities is to decrease values.  
As for the mean hate score, it follows a similar pattern, where highest mean hate score is ca. 0.425 for a \(p_{defer}=0.3\) in the 5000 network, and it goes down to ca. 0.39 for \(p_{defer}=0.9\) in all the simulated networks. However, this countermeasure has an obvious effect in decreasing the mean path length of hateful content from an initial range of ca. [1.75, 2.6] for a \(p_{defer}=0\) (i.e., without deferring) down to a range of ca. [0.25, 1.1] for a \(p_{defer}=0.9\) in all network sizes. More outstanding is its property in protection against swaps to a hateful society as shown on the bottom of Figure 8, as it goes below 0.025 for all network sizes when \(p_{defer}=0.9\).  
Overall, we can observe that deferring results are similar to the ones from education, but they have the advantage of having a short-term effect. We argue this is very relevant because deferring content is aligned with the freedom of speech value, which is not the case of other short-term countermeasures such as automatic filtering.
# Radar Voxel Fusion for 3D Object Detection
## 4 Results
### Early Fusion vs. Late Fusion  
The effectiveness of the neural network early fusion approach is further evaluated against a late fusion scheme for the respective sensors. For the lidar, RGB, and radar input configurations are fused with an UKF and an Euclidean-distance-based matching algorithm to generate the final detection output. This late fusion output is compared against the early fusion RVF-Net and lidar detection results, which are individually tracked with the UKF to enable comparability. The late fusion tracks objects over consecutive time steps and requires temporal coherence for the processed samples, which is only given for the samples within a scene but not over the whole data set. Table 5 shows the resulting AP score for 10 randomly sampled scenes to which the late fusion is applied. The sampling is done to lower the computational and implementation effort, and no manual scene selection in favor or against the fusion method was performed. The evaluation shows that the late fusion detection leads to a worse result than the early fusion. Notably, the tracked lidar detection outperforms the late fusion approach as well. As the radar-only detection accuracy is relatively poor and its measurement noise does not comply with the zero-mean assumption of the Kalman filter, a fusion of this data to the lidar data leads to worse results. In contrast to the early fusion where the radar features increased the detection score, the late fusion scheme processes the two input sources independently and the detection results cannot profit from the complementary features of the different sensors. In this paper, the UKF tracking serves as a fusion method to obtain detection metrics for the late fusion approach. It is important to note that for an application in autonomous driving, object detections need to be tracked independent of the data source, for example with a Kalman Filter, to create a continuous detection output. The evaluation of further tracking metrics will be performed in a future paper.
## 1 Introduction
In [12; 19], shorting pins are used to design flexible antennas for off-body or on-body communication. However, these antennas work in the 5 GHz band [12; 13; 17; 18; 19]. If such antennas are scaled to the 2.4 GHz band, the physical thickness of the antennas increases. The scaled designs may be too bulky for wearable applications. As for the 2.4 GHz band low-profile wearable designs [16; 20; 21; 22], they all have a limited CP bandwidth. This is due to the fact that with a physically similar low-profile thickness, their electrical thickness in the 2.4 GHz band is smaller than that one in the 5 GHz band, and thus, leads to a high Q, in other words, a narrow bandwidth in the 2.4 GHz band. Comparing to these reported wearable designs with a low profile, the proposed antenna achieves the highest CP bandwidth of 14.3%, around 3-5 times that of the counterparts. This is accomplished by adopting parasitic elements, as in [14], and a novel slit-loading technique. Notably, except for the parasitic loadings, Ref. [14] also utilizes a widely used bandwidth boosting method, i.e., air gap loading [14; 23], and two blind disk-loaded pins to realize a single-feed design. However, the approach in [14] is unfavorable for wearables because of the thick air gap, and the blind disk and pins make the antenna fabrication complicated and costly (which are not easy to be addressed for wearables). Moreover, it is worth emphasizing that unlike traditional parasitic loadings [14; 29], the bandwidth enhancement in this work is also attributed to the slit-loading approach, which introduces higher-order modes and also unblocks the requirement of inductive matching compensation in traditional designs. Overall, both the parasitic elements and the slit-loading lead to a decent CP bandwidth with a low-profile single-layer substrate. This work is organized as follows. Section 2 illustrates the antenna configuration. Section 3 introduces the CP bandwidth-enhancing procedure from the perspective of CMA. Further, under the guidance of the obtained CMA results, CST microwave studio 2020 is employed to validate the antenna design with full-wave simulations. Then the antenna is fabricated, and the measurement results are discussed in Section 4. Finally, conclusions are given in Section 5.
# Long-Term EEG Component Analysis Method Based on Lasso Regression
### Related Works of Emotional EEG Analysis
(2018) used the AR model and empirical mode decomposition (EMD) to extract EEG features for emotion recognition induced by music videos. The average recognition rate of four kinds of emotions was 86.28%. In addition, [PERSON] et al. (2019) extended the brain functional network method to analyze dynamic EEG changes during music appreciation. The dynamic brain functional network for different frequency bands of EEG signals was constructed by mutual information, observed over time, and used in emotion recognition. Based on the SVM classifier, the recognition rate of the four categories of emotion music classification --happy, distressed, bored, and calm--is 53.3%. In addition to the above processing methods, various intelligent processing methods have vigorously promoted emotional EEG signal analysis. In 2007, [PERSON] et al. extracted two chaotic features (Fuzzy C-Means, Fuzzy K-Means) of EEG signals and applied them to emotion classification ([PERSON] et al., 2017). In 2010, [PERSON] from the National Taiwan University used a differential asymmetry of electrodes to classify five types of emotions induced by music, and achieved a recognition rate of 82% ([PERSON] et al., 2016). In 2012, the Greek scholar [PERSON] proposed Higher Order Crossings features to classify six types of emotions (happiness, surprise, anger, fear, disgust, and sadness) and obtained a recognition rate of 83% ([PERSON] et al., 2016). In 2017, [PERSON] et al. applied the deep neural network (DNN) and convolutional neural network (CNN) to recognize emotions. The recognition rate of two types of emotions reached 73.36%, based on multimodal physiological signals. The recognition rate of three kinds of emotions was 57.58% ([PERSON] et al., 2018). It is not difficult to see that most existing analysis methods are based on statistical analysis and machine learning techniques. There are many deficiencies in the research of  
Figure 1: Schematic diagram of ERP analysis method. The curve in the figure is ERP induced by emotional sounds. Seven EEG components contribute the most. long-term brain cognitive mechanisms and emotion recognition. The main reasons are as follows:  
1. There is no long-term analysis method of EEG components, or quantitative calculation method for EEG features;
2. In most of the existing methods, temporal information is ignored, and the characteristics of temporal variation of emotions are not considered.
# STOD: Towards Scalable Task-Oriented Dialogue System on MultiWOZ-API
## 4 Methodology
### Multi-Domain Scalable End-to-End Task-Oriented Dialogue System  
Based on the above dialogue modeling framework, we construct a multi-domain end-to-end TOD system (MSTOD). MSTOD models a task-oriented dialogue as follows: the model first predicts whether it requires a database query based on the last turn \(U_{t}\) and the  
Figure 2: The overview of our proposed STOD. The black arrow shows the query generation process, and the red arrow shows the response generation process. The circled numbers indicate the order of generation.  
dialogue context \(\{U_{1},Q_{1},D_{1},R_{1},\cdots,Q_{t-1},D_{t-1}\}\). If not, the model will directly generate a response \(R_{t}\). Otherwise, the model will generate an API query \(Q_{t}\), then interact with the database, and finally, the DB result \(D_{t}\) will be returned. In this processing, if an API query is generated, both the API query and the returned DB results will be added into the context, then continue to generate until the next natural language response \(R_{t}\).
# A Survey on MLLMs in Education: Application and Future Directions
## 2 Preliminaries of MLLMs  
### Overview of Large Language Models (LLMs)  
Large language models (LLMs) have revolutionized the field of artificial intelligence, particularly in natural language processing (NLP) tasks. These models are designed to understand, generate, and manipulate human language with a high degree of fluency and coherence. Notable examples include OpenAI's GPT-4o and Anthropic's Claude3.5, which are trained on vast amounts of textual data and can perform a wide array of language-related tasks such as translation, summarization, question answering, and content creation [8; 9; 10]. LLMs leverage the transformer architecture [11], utilizing self-attention mechanisms to capture complex dependencies in language, enabling them to generate human-like text that is contextually relevant.  
The transformer architecture has been pivotal in the success of LLMs. It allows models to process input data in parallel rather than sequentially, significantly improving computational efficiency and performance on large datasets. Self-attention mechanisms within transformers enable the models to weigh the importance of different parts of the input data, capturing long-range dependencies and nuances in language [6]. This capability has led to breakthroughs in tasks that were previously challenging for AI, such as coherent essay writing and intricate dialogue generation.  
Despite their impressive capabilities, traditional LLMs are limited to processing textual data [12]. This constraint restricts their applicability in contexts where information is conveyed through multiple modalities, such as images, audio, and video. Human communication and learning are inherently multimodal, often involving the integration of visual cues, auditory signals, and textual information. The inability of LLMs to process non-textual data limits their effectiveness in applications that require a holistic understanding of diverse information sources.
# Adaptive Leakage Protection for Low-Voltage Distribution Systems Based on SSA-BP Neural Network
## 3 Normal Leakage Current Prediction Model  
### Principle of Adaptive Action Threshold Adjustment  
Figure 3 shows the schematic diagram of the leakage current prediction model considering the environmental humidity, temperature, and load current. By adding temperature, humidity, and current sensors to the existing leakage protector, the feature collection of the leakage current influencing factors of the system is realized. The leakage current after time T (predictive time length) is predicted using the actual leakage current of the system and its influencing factors as inputs for the prediction model. In order to rectify and optimize the system coefficient, the actual measured leakage after time T is compared with the predicted leakage. The optimal modeling is achieved after learning and correcting a certain number of samples.  
The flow chart of adaptive protection action threshold adjustment based on leakage prediction is shown as Figure 4. After preprocessing the collected characteristic data, they are sent to the leakage prediction model to train the unknown system to obtain the predicted leakage current. The measured leakage current is used as the expected value to correct the system parameters. After a period of multi-sample data learning and training, the model parameters are gradually improved, and the predicted leakage current gradually approaches the real leakage current.  
Figure 3: Principle of leakage current prediction model.
## 3 Results and Discussion  
### Plasma System Characterizations  
Figure 1a illustrates the plasma jet system used for preparing PDA-Au. The current-voltage waveforms recorded from the plasma jet are shown in Figure 1b. The applied voltage was sinusoidal with a period of 16.8 us, corresponding to a frequency of 59.5 kHz and a maximum applied voltage of approximately 6 kV (equivalent to a root-mean-square voltage of about 4.2 kV). The maximum discharge current was measured at around 85 mA. Figure 1c demonstrates the OES spectrum of the plasma effluent that extended beyond the quartz nozzle. The spectrum shows intensive emission lines in the 700-1000 nm range, which are attributed to the transition of Ar 2p-1s. The emission band observed at 309.2 nm is associated with hydroxyl radicals (\(\bullet\)OH), which are formed through the plasma-driven dissociation of water vapor present in the surrounding air. These \(\bullet\)OH radicals can subsequently recombine to produce hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)), which may diffuse into the liquid phase. Additionally, the bands at 300-400 nm are attributed to the emissions of the nitrogen second positive system. These bands arise from electronic transitions between the excited C\({}_{3}\)II\({}_{\text{u}}\) state and the B\({}_{3}\)I\({}_{\text{g}}\) state of N\({}_{2}\) molecules in ambient air, which are excited through interactions with high-energy plasma constituents.