esa-sceva/satcom-chunk-collection huggingface dataset:
# Tea Infusions as a Source of Phenolic Compounds in the Human Diet
###### Abstract
The degree of processing has a significant effect on the nutritional value of infusions, and many studies indicate that the least processed teas are the best source of components that have a positive effect on human health, which is particularly noticeable for phenolic compounds [28; 29; 30; 31]. A previous study by [PERSON] et al. [5] conducted on 33 tea samples with different degrees of leaf processing showed that the polyphenols content was almost twice as high in infusions of unfermmented green tea compared to fully fermented black tea. Studies by other authors [3; 9; 10; 16; 20; 24; 29; 32] have also confirmed the high content of phenolic compounds in green tea. Oxalate levels appear to be most affected by the anatomical part of the tea plant from which they originate [33; 34]. Most of these compounds occur in old and hard, low-positioned leaves (used to produce poorer-quality teas), whereas few are found in the small leaves on the top of the plant with the addition of top buds and new leaf primordia (used to produce higher-quality teas) [13; 15; 17]. [PERSON] and [PERSON] [35] reported that in various types of tea leaves of different quality offered on the market, the oxalate content can range from 300 to 2000 mg/100 g fresh leaves, whereas much smaller differences in the levels of these compounds are noted when analyzing the effect of the factors related to the infusion method [33; 36]. Consumers prepare tea infusions in different ways by extracting tea leaves with water at different temperatures and leaving it in contact with the tea leaves for different lengths of time; some consumers brew the same leaves several times. Each of these infusion methods has a significant effect on the degree of extraction of the compounds found in the tea leaves, which results in varying nutritional values of the infusions [26; 37]. [PERSON] et al. [32] and [PERSON] et al. [1] indicate the significance of the temperature of the water used to make tea infusions, whereas [PERSON] et al. [24] are of the opinion that water hardness is also of importance. [PERSON] and [PERSON] [38] noted the effect of the number of leaf extractions with water, and [PERSON] et al. [4] and [PERSON] [39] believe that the most important factor affecting the nutritional value of tea infusions is the duration of contact between the leaves and water. In view of the widespread consumption of tea, its significant role in people's daily diet, and the occurrence of numerous factors that affect the nutritional value of its infusions, the aim of this study was to identify the effects of tea type and time of tea leaf extraction with water on phenolic compound levels. An additional aim of the study was to determine the relationships between the contents of these compounds and the levels of oxalates and selected minerals (Ca, Na, Cu, and Mn) determined in the tea infusions.
## 3 Experimental Results and Analysis
### Ablation Experiment
#### 3.3.2 Distillation Loss  
Based on using the cross-entropy loss to verify the effectiveness of the model after introducing the distillation loss, the distillation loss is integrated into (a) ViT and (d) ViT(IEA&SPA), respectively, to obtain (e) ViT(SA&L_DIS) and (f) ViT(IEA&SPA&L_DIS), the experimental results of jointly using the cross-entropy loss function and distillation loss function as the total loss function are shown in Table 2. The results show that by adding distillation loss, the accuracy of ViT improved by 0.84%, ViT(IEA&SPA) improved by 0.78%, and ViT(IEA&SPA&L_DIS) improved by 0.28% compared to ViT(SA&L_DIS). This shows that ViT(IEA&SPA&L_DIS) has the best classification accuracy after the introduction of distillation loss.  
In order to further verify the effectiveness of distillation loss after its introduction, this paper used three classical CNN backbone networks, VGG-19 [30], ResNet-101 [31], and ResNet-50 [32; 33], as Teacher models in the knowledge distillation strategy, based on ViT(IEA&SPA&L_DIS), and instructed ViT(IEA&SPA&L_DIS) for training; the training results are shown in Table 2. The results show that different Teacher models have different effects on BDiT model accuracy after introducing distillation loss on the basis of ViT (IEA&SPA). Among them, when ResNet-50 is selected as the Teacher model, the classification accuracy reaches 97.86%, which is 0.34% higher than before adding distillation loss. Compared with BDiT (IEA&SPA&L_DIS1) and BDIT (compared with IEA&SPA&L_DIS2), the accuracy increased by 0.55% and 0.98%, respectively. Among them, when ResNet-50 is selected as the Teacher model, the classification accuracy reaches 97.86%, which is 0.34% higher than before adding distillation loss. It is comparable to BDiT (IEA&SPA&L_DIS1) and BDiT (IEA&SPA&L_DIS2), the accuracy increased by 0.55% and 0.98%, respectively. In summary, BDiT (IEA&SPA&L_DIS3), which uses ResNet-50 as the Teacher model, is selected as the final training model of this article, that is, the BDiT model.
# Flexible UHF RFID Tag for Blood Tubes Monitoring
## 4 Fabrication and Experimental Characterization of the Proposed Tag Antenna
### Tag Power Sensitivity  
Figure 12 represents the tag power sensitivity, which defines the minimum power that should be received by the tag to be able to power on the IC chip. At the resonant frequency of 867 MHz, the minimum transmitted power needed to activate the tag is 14 dBm when the tube is full of blood. Moreover, there is a large difference when the tube is empty, requiring a minimum power of 27 dBm to activate the tag. This large amount of power required to activate the IC chip is due to the fact that most of the power present at the antenna terminals is not delivered to the chip, but it is in fact reflected back, as shown by the generalized reflection coefficient of Figure 5.
## 3 System Model and Problem Formulation
### Problem Formulation  
As mentioned earlier, the objective of this paper is to achieve coverage optimization and enhancement by scheduling the working direction and position of nodes. Therefore, in the process of node deployment optimization, we should consider how to reduce redundant coverage and maximize coverage. At the same time, we should also pay attention to minimizing the moving distance to ensure the survival time of the network. Next, we analyze the objective functions and constraints of these two problems in detail.
# Prospects of Structural Similarity Index for Medical Image Analysis
## 4 SSIM in Medical Imaging
### Ultrasonography
In addition, compared to other metrics, SSIM performances are competitively appropriate according to their dedicated assignment. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Study** & **Year** & **Modality** & \begin{tabular}{c} **SSIM** \\ **Implementation** \\ \end{tabular}  & **Compared Matrix** & **Results** \\ \hline [PERSON] et al. [106] & 2007 & US & IQA & CoC, EPI, and QI & SSIM is suitable for this task \\ \hline [PERSON] et al. [107] & 2007 & US and X-ray & IQA & \begin{tabular}{c} MSE, PSNR, CC, and \\ PRD \\ \end{tabular}  & SSIM is suitable for this task \\ \hline [PERSON] et al. [108] & 2008 & US & IQA & CM, CBN, and PSNR & SSIM is suitable for this task \\ \hline [PERSON] et al. [60] & 2016 & US & IQA & \begin{tabular}{c} SNR, PSNR, CoC, and IQI \\ \end{tabular}  & SSIM is suitable for this task \\ \hline [PERSON] et al. [109] & 2016 & US & IQA & PSNR & SSIM is suitable for this task \\ \hline [PERSON] et al. [110] & 2016 & US & IQA & SNR, MSE, and SV & SSIM is suitable for this task \\ \hline [PERSON] et al. [61]\({}^{\star}\) & 2016 & US & \begin{tabular}{c} Tongue contour extractor \\ \end{tabular}  & \begin{tabular}{c} No similarity constraint and \\ similarity constraint \\ \end{tabular}  & \(MSD=2.96\pm 0.95\) to \(3.65\pm 1.02\) \\ \hline [PERSON] et al. [111] & 2016 & US & \begin{tabular}{c} Tongue contour extractor \\ \end{tabular}  & NPSNR & CW-SSIM has the best performance \\ \hline [PERSON] and [PERSON] [59] & 2017 & US & IQA & PSNR and EPI & SSIM is suitable for this task \\ \hline [PERSON] et al. [112] & 2018 & US & IQA & PSNR & SSIM is suitable for this task \\ \hline [PERSON] et al. [113] & 2018 & US & IQA & \begin{tabular}{c} PSNR, MSE, and \\ MAE \\ \end{tabular}  & SSIM is suitable for this task \\ \hline [PERSON] [58] & 2018 & US & IQA & MSE, SNR, and PNSR & SSIM is suitable for this task \\ \hline [PERSON] et al. [114] & 2019 & US & IQA & PSNR & SSIM is suitable for this task \\ \hline [PERSON] et al. [115] & 2019 & US & IQA & SNR & SSIM is suitable for this task \\ \hline [PERSON] et al. [118] & 2020 & US & IQA & PSNR & SSIM is suitable for this task \\ \hline [PERSON] and [PERSON] [116] & 2020 & US & IQA & \ \begin{tabular}{c} PSNR, ENL, and \\ CNR \\ \end{tabular}  & SSIM is suitable for this task \\ \hline [PERSON] et al. [66] & 2020 & US & IQA & EPI and UQI & SSIM is suitable for this task \\ \hline [PERSON] et al. [53] & 2020 & US & Loss function & - &
\begin{tabular}{c} \(Dice\,of\,reiter\,=\,0.8682\
## Appendix A Explanations of Top Intrusion Features in XAI-IDS
### Detailed Explainability of Top Intrusion Features in NSL-KDD  
**Explaining Top Features from a Security Point of View:**  
1. [label=(0)]
2. **DST_HOST_SAME_SRV_RATE:** This feature is relevant because, during a network attack such as a DoS attack or port scanning, the attacker might repeatedly connect to the same service on a particular host. A high DST_HOST_SAME_SRV_RATE value may indicate such repetitive access to the same service, which could be a sign of suspicious activity.
3. **DST_HOST_SRV_COUNT:** This feature is important because it can highlight unusually high numbers of connections to a particular service, which could be indicative of a Brute-Force attack or a DoS attack. A high DST_HOST_SRV_COUNT may suggest that an attacker is repeatedly connecting to a service, attempting to gain unauthorized access or overload the service.
4. **DST_HOST_SAME_SRC_PORT_RATE:** This feature is relevant because, during certain network attacks, such as port scanning or DoS attacks, an attacker might make multiple connections from the same source port. A high DST_HOST_SAME_SRC_PORT_RATE could indicate such behavior, suggesting potential malicious activity.
5. **LOGED_IN:** This feature is important because if an unexpected or unauthorized host is logged into a system, it can be an indication of a security breach. The feature could capture scenarios where an attacker has successfully gained unauthorized access to a system.  
**(5)** **DST_HOST_SERROR_RATE:** This feature is significant, as it could indicate the presence of a network attack. SYN errors may occur when there is an attempted connection to a closed port, which could be indicative of port scanning or a failed unauthorized access attempt. A high DST_HOST_SERROR_RATE may thus indicate malicious network behavior.
# Moving Deep Learning to the Edge
## 4 Deep Learning Inference on Edge Devices
### Hardware-Oriented Deep Neural Network Optimizations
The main disadvantage of pruning is that it introduces sparsity into the matrix of weights. This generates unbalanced parallelism in the computation of output maps and irregular accesses to on-chip memory. A few approaches have followed to reduce the effects of sparsity [135; 136]. In these works, pruning was guided by the datapath of the target computing processor so that it could take advantage of the available computing parallelism. Pruning introduces zeros in the matrix of weights, and some activation functions zero the activations. Considering this, some authors proposed a zero-skipping technique [137; 138] to avoid multiplications by zero. The zero-skipping technique requires a large on-chip memory to explore the available parallelism of the hardware accelerators. Therefore, some coarse-grained skipping techniques were considered to permit zero-skipping in devices with scarce on-chip memory [139]. Some authors [140] overcame the on-chip memory limit by considering that the matrix was stored in a dense format, requiring that all weights, including zeros, be loaded. In [141], an architecture was proposed that could skip zeros in both weights and activations. However, the solution had reduced performance efficiency. A commonly used technique to balance the heavy transfer of weights in fully connected layers is map batching, where several feature maps of the last convolutional layer are saved before running the fully connected layers [142; 143; 144]. This technique permits reusing the kernels in dense layers, amortizing the transfer time of parameters from external memory. Since convolutional neural networks are based on convolutions, some authors have used the Winograd filtering [145] technique to calculate convolutions [146]. Winograd filtering is a known technique to reduce the number of multiplications of a convolution. The technique was efficiently implemented on FPGA [147; 148; 149; 150]. The main data quantization and data reduction techniques are summarized in Table 4.  
Data reduction techniques are normally applied together with data quantization. Together, they generate very efficient solutions with a small accuracy reduction when compared to solutions without optimizations. Which techniques can or should be applied depends on the target application. Accuracy reduction may be a problem for applications where accuracy is critical. In these cases, optimization techniques must be carefully applied or even limited to those that do not modify the accuracy.
###### Abstract  
Reconfigurable Intelligent Surfaces (RISs) are a class of metamaterials that have gained significant attention in recent years due to their potential to revolutionize wireless communication, sensing, and imaging technologies. RISs consist of a planar array of closely spaced, subwavelength-sized elements that can manipulate electromagnetic waves in a controllable manner. By reconfiguring the geometry, material properties, or phases of the individual elements on the RIS, the surface can be customized to meet specific application requirements. RISs can improve wireless communication by creating virtual channels, reducing interference, and improving overall quality. They can also enhance the efficiency of energy harvesting systems and improve sensing and imaging technologies by manipulating the propagation and scattering of electromagnetic waves. Additionally, RISs could be used to increase privacy and security by selectively blocking or allowing specific frequencies of electromagnetic waves. In this editorial, we provide a brief history of the development of RISs and discuss the design and fabrication of RIS structures. We also discuss RIS technology's potential applications and benefits, including improved wireless communication, enhanced energy efficiency, advanced sensing and imaging, and increased privacy and security. Finally, we highlight some current research challenges and future directions for RIS technology. Overall, RISs hold great promise for advancing a wide range of technologies and applications, and we expect to see many exciting developments in this area.  
metamaterials; reconfigurable intelligent surfaces; 5G; 6G; machine learning; artificial intelligence +
Footnote †: journal: Applied  
1
Footnote 1: email: _Applied_
###### Abstract  
Marine environmental monitoring provides crucial information and support for the exploitation, utilization, and protection of marine resources. With the rapid development of information technology, the development of three-dimensional underwater acoustic sensor networks (3D UASNs) provides a novel strategy to acquire marine environment information conveniently, efficiently and accurately. However, the specific propagation effects of acoustic communication channel lead to decreased successful information delivery probability with increased distance. Therefore, we investigate two probabilistic neighborhood-based data collection algorithms for 3D UASNs which are based on a probabilistic acoustic communication model instead of the traditional deterministic acoustic communication model. An autonomous underwater vehicle (AUV) is employed to traverse along the designed path to collect data from neighborhoods. For 3D UASNs without prior deployment knowledge, partitioning the network into grids can allow the AUV to visit the central location of each grid for data collection. For 3D UASNs in which the deployment knowledge is known in advance, the AUV only needs to visit several selected locations by constructing a minimum probabilistic neighborhood covering set to reduce data latency. Otherwise, by increasing the transmission rounds, our proposed algorithms can provide a tradeoff between data collection latency and information gain. These algorithms are compared with basic Nearest-neighbor Heuristic algorithm via simulations. Simulation analyses show that our proposed algorithms can efficiently reduce the average data collection completion time, corresponding to a decrease of data latency.  
Underwater Acoustic Sensor Networks; data collection; probabilistic neighborhood +
Footnote †: journal: Sensors
# Innovative Approaches to Traffic Anomaly Detection and Classification Using AI
## 2 Algorithms and Methods
### Machine Learning  
Machine learning has played a foundational role in traffic anomaly detection by enabling systems to learn patterns from data and identify deviations. These methods, particularly unsupervised learning techniques, are effective in handling the variability and unpredictability of traffic scenarios.  
Trajectory analysis is a common approach in anomaly detection, where the movement patterns of objects are analyzed to identify irregularities. The work in [13] presented a method for detecting anomalous events in video sequences using trajectory analysis. The approach employs single-class support vector machines (SVMs) to group similar trajectories and identify deviations as anomalies. By smoothing trajectories and converting them into fixed-dimensional feature vectors, the model applies a Gaussian kernel for similarity analysis. The authors address the challenge of determining the parameter \(v\), which controls the proportion of outliers, with a novel hypervolume reduction technique to identify true outliers. Tests on synthetic and real-world datasets, including urban traffic surveillance videos, demonstrated robust anomaly detection and generalization to unseen patterns, as shown in Figure 3.  
Clustering techniques help analyze trajectory patterns for trajectory-based anomaly detection. In [14], a study compared six trajectory distance measures and seven clustering methods across six datasets. As illustrated in Figure 4, the obtained results showed that the clustering method has minimal impact on quality, provided that complete, unsampled trajectories are used. Time-normalized distance measures like dynamic time warping (DTW) excel in datasets with varying dynamics, while dimensionality-reduction techniques like PCA perform better on long, overlapping trajectories. In cases where trajectory dynamics is  
Figure 2: Hierarchical graph presenting the methods of this review.  
In [16], a projection-based method for anomaly detection and localization, focusing on thematic models was introduced. The approach combines spatio-temporal descriptors (HOG-HOF) with object location and size information to build a visual vocabulary. The algorithm operates in three stages: vocabulary creation, anomaly quantification, and localization. Compared to traditional probabilistic models, the proposed method improves both detection accuracy and spatio-temporal localization. By performing tests on three real-world surveillance datasets (Figure 5), the authors validated the effectiveness of the proposed methods.
## 3 Results and Discussion  
### Polarizing Property  
The electrical resistivity of all dumbbell-shaped specimens after 28 days curing was measured using a four-probe method for the first 1800 s in order to observe the effect of polarization.  
Figure 3a illustrates a typical variation of the measured resistivity with durations for the PE-SHCC with 1.0% steel fiber and 1.0% MWCNTs without the steel bar (PE-SHCC-SF10-0), a PE-SHCC-SF10 specimen reinforced with a 4 mm diameter round bar (PE-SHCC-SF10-4) and a PE-SHCC-SF10 specimen with a 7 mm diameter round bar (PE-SHCC-SF10-7), respectively. It can be observed from Figure 3a that the resistivity of PE-SHCC-SF10 mixtures without and with steel bar stabilized and gradually diminished. Figure 3b presents the effect of steel bar amount and steel fiber contents on the average resistivity of PE-SHCC dumbbell-shaped specimens before tensile loading.  
As shown in Figure 3a,b, the steel bar embedded at the center of the specimen decreases the electrical resistivity of PE-SHCC dumbbell-shaped specimens. As the reinforcing ratio, defined as the ratio between the cross-sectional area of the reinforcing steel bar to the cross-sectional area of the specimen, increased, the electrical resistivity of specimens decreased because of the transmission of the electrical current through steel bar. The consistent result in rectangular beam with rebar in the tensile zone were reported by [PERSON] and [PERSON] [21]. Figure 3b indicates that the addition of steel fibers decreases the electrical resistivity of the tensile specimen with and without steel bar. The steel fiber content effect on the resistivity of the PE-SHCC tensile specimen is clearer in the specimen without the  
Figure 3: Variation of the measured resistivity with time for PE-SHCC dumbbell-shaped specimens: (**a**) the effect of the steel bar on the electrical resistivity variation of the PE-SHCC-SF10 mixture with duration; (**b**) the effect of the steel bar and steel fibers on the average electrical resistivity of PE-SHCC mixture during the first 1800 seconds.  
steel bar. The average electrical resistivity of the PE-SHCC specimens without the steel bar were 10.26 \(\times\) 10\({}^{6}\)\(\Omega\cdot\)cm, 7.19 \(\times\) 10\({}^{6}\)\(\Omega\cdot\)cm and 3.50 \(\times\) 10\({}^{6}\)\(\Omega\cdot\)cm, respectively, for the PE-SHCC mixtures with 0, 0.5 and 1.0% steel fibers. Consistent results were reported by [PERSON] et al. [29] and [PERSON] et al. [30]. As pointed out by their studies, it is because steel fibers in the PE-SHCC formed the network for conductivity and as the steel fiber content increases, a larger network is constructed that improves the electrical conductivity of cement-based composites such as PE-SHCC. However, for PE-SHCC tensile specimens with a 7 mm diameter round bar, the polarization variation is not clear according to steel fiber content as shown in Figure 3b. It is apparent that the resistivity of PE-SHCC with steel bar depends mainly on the amount of steel bar and is relatively less dependent on the PE-SHCC matrix.
# Lithological Discrimination of Khyber Range Using Remote Sensing and Machine Learning Algorithms
## 3 Materials and Methods
### Linear Mapping  
A lineament is a region that undergoes deformation and fracturing, indicating a zone with increased secondary porosity that facilitates the flow of mineralized fluids. The PCI Geomatica Automatic Lineament Extraction algorithm (PCI-LINE) was utilized to map lineaments utilizing Landsat-8 data. Edge detection, thresholding, and curve extraction are the three steps that the PCI-LINE module typically uses to extract lineaments. ArcMap 10.5 was used to handle the extracted line segments, which included exporting the line segment shape file, modifying the line properties, and transforming complex lines into simple lines [86].