esa-sceva/satcom-chunk-collection huggingface dataset:
# Advancing Brain Tumor Segmentation with Spectral-Spatial Graph Neural Networks
## 2 Methods and Material
### Evaluation Metrics  
In the ongoing development of tumor segmentation models, the implementation of an adept and thorough evaluation metric is of essence. The metrics illustrated below are designed to give an intensive examination of the performance of models.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Hyperparameter** & **Spatial GNN** & **Spectral GNN** & **Spectral–Spatial GNN** \\ \hline Number of Epochs & 200 & 200 & 200 \\ Number of Node Features & 20 & 20 & 20 \\ Output Classes & 4 & 4 & 4 \\ Learning Rate & 0.005 & 0.005 & 0.005 \\ Learning Rate Decay & 0.95 & 0.95 & 0.95 \\ Weight Decay & \(5\times 10^{-4}\) & \(5\times 10^{-4}\) & \(5\times 10^{-4}\) \\ Layer Sizes & [256; 256; 256; 256] & [256; 256; 256; 256] & [256; 256; 256; 256] \\ Dropout & 0.5 & 0.5 & 0.5 \\ Chebyshev filter size & - & 2 & 2 \\ Activation Function & ReLU & ReLU & ReLU \\ Optimizer & Adam & Adam & Adam \\ Aggregator & LSTM & - & LSTM \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters and hyperparameters for Spatial, Spectral, and Spectral–Spatial GNNs.  
Dice coefficient: A primary metric employed is the dice coefficient, which is formulated as  
\[\text{Dice}=\frac{2\times|X\cap Y|}{|X|+|Y|}=\frac{2\times\sum|A\times B|}{\sum A ^{2}+\sum B^{2}} \tag{4}\]  
This metric, drawing from [PERSON] et al. [4]'s work on volumetric medical image segmentation, measures the overlap between model predictions \(Y\) ) and actual segmentations \(X\), assessing model accuracy. \(X\) represents the true regions of interest, such as tumors in MRI scans, while \(Y\) is the algorithm's predicted segmentation. \(A\) and \(B\) are binary masks for \(X\) and \(Y\), marking regions of interest as 1, simplifying the comparison of actual and predicted segmentation  
Precision, sensitivity, and specificity: Precision, another critical metric, calculates the ratio of true positives to the combined sum of true positives and false positives. Sensitivity (or recall) measures the model's ability to correctly identify positive samples, while specificity assesses its skill in identifying negative samples. These metrics, when combined, provide a comprehensive view of the model's performance, ensuring a meticulous assessment of the balance between detecting tumor regions and avoiding false positives.  
\[\text{Precision}=\frac{\text{True Positives}}{\text{True Positives}+\text{False Positives}} \tag{5}\]  
\[\text{Sensitivity}=\frac{\text{True Positives}}{\text{True Positives}+\text{False Negatives}} \tag{6}\]  
\[\text{Specificity}=\frac{\text{True Negatives}}{\text{True Negatives}+\text{ False Positives}} \tag{7}\]
# Rail Diagnostics Based on Ultrasonic Guided Waves: An Overview
## 5 Implementation of On-Board Systems
### Passive Approach  
#### 5.2.1 Hardware Configuration  
In this case, as illustrated in Refs. [49; 66], the sensing head is made of two receivers, denoted \(A\) and \(B\), separated by a known distance; both are sensitive only to waves propagating unidirectionally (from left to right in Figure 13). The sensing head is mounted on a beam rigidly connected to the front-axle of the test car; similarly as in the active approach, contactless probing is employed. The sensors are tilted with respect to the rail surface (according to [PERSON]'s law) to best capture the leaky surface waves propagating in the railhead. The orientation of receivers ensures directional sensing of the waves excited by the wheels located on only one side of the arrays (front end), with virtually no sensitivity to waves propagating in the opposite direction (i.e., to reflections or waves excited from the wheels located to the other side of the arrays' back end).  
Data are continuously recorded for the entire duration of the test [49]. A tachometer logic pulse marking the spatial position of the test car and GPS positioning are recorded, in addition to high-speed camera videos of the tested rail to verify the presence of visible discontinuities when the prototype detects an anomaly [48].
## 4 Experiments and Results
### High Precision Label Making  
The separability check of the pseudo-TCT image requires a high-precision label of buildings and roads, and traditional labeling is usually performed manually. Manual labeling can easily miss small buildings and roads, or buildings and roads are labeled with background information, which can lead to inaccurate accuracy evaluation results [41; 42; 43].  
To solve the problem whereby it is difficult to produce a large high-precision label by traditional methods, a combination of pseudo-TCT images, such as the 0123 combination, was selected and clustered into 10 classes using K-Means. The classes containing buildings and roads in the K-Means classification were converted to vector files and then further edited in ArcGIS to obtain the label. The label was manually edited to eliminate the effect of the 0123 combination. The K-Means clustering results and vector labels for region 2, region 3 and region 4 (containing more than 99% of buildings and roads) are shown in Figure 4.  
Figure 4: _Cont._  
Figure 3: Original images, TCT images, and 0123 combination images of region 2, region 3, and region 4. (**a**) original image of region 2; (**b**) original image of region 3; (**c**) original image of region 4; (**d**) TCT image of region 2; (**e**) TCT image of region 3; (**f**) TCT image of region 4; (**g**) 0123 combination image of region 2; (**h**) 0123 combination image of region 3; (**i**) 0123 combination image of region 4.
## 2 Results and Discussion
### Antioxidant Activity  
To better evaluate the antioxidant activity of eight isolated peptides (TJP1 to TJP8) from protein hydrolysate of hairtail (_T. japonicus_) muscle, four kinds of radical (DPPH\(\bullet\), HO\(\bullet\), O\({}_{2}^{-}\bullet\), and ABTS\({}^{+}\bullet\)) scavenging assays, reducing power, and lipid peroxidation inhibition assay were tested, and the results were presented in Table 2 and Figures 8-10.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Retention time (min)** & **Amino acid sequence** & **Theoretical mass/observed mass (Da)** \\ \hline TJP1 & 8.919 & Gln-Asn-Asp-Glu-Arg & 660.3/660.6 \\ TJP2 & 9.189 & Lys-Ser & 233.0/233.3 \\ TJP3 & 12.112 & Lys-Ala & 217.1/217.3 \\ TJP4 & 13.829 & Ala-Lys-Gly & 274.1/274.3 \\ TJP5 & 14.209 & Thr-Lys-Ala & 318.0/318.4 \\ TJP6 & 17.237 & Val-Lys & 245.1/245.3 \\ TJP7 & 19.772 & Met-Lys & 277.0/277.4 \\ TJP8 & 20.436 & Ile-Tyr-Gly & 351.0/351.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Retention time, amino acid sequences, and molecular mass of eight isolated peptides (TJP1 to TJP8) from protein hydrolysate of hairtail (_T. japonicus_) muscle.  
Figure 7: Mass spectra of TJP1 (A), TJP2 (B), TJP3 (C), TJP4 (D), TJP5 (E), TJP6 (F), TJP7 (G), and TJP8 (H) from protein hydrolysate of hairtail (_T. japonicus_) muscle.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**EC\({}_{\text{50}}\) (mg/mL)**} \\ \cline{2-5}  & **DPPH\(\bullet\)** & **HO\(\bullet\)** & **O\({}_{2}^{-}\)\(\bullet\)** & **ABTS\({}^{+}\)\(\bullet\)** \\ \hline TJP1 & 4.95 & 6.865 & 2.753 & 1.925 \\ TJP2 & 7.68 & 5.634 & 4.296 & 2.496 \\ TJP3 & 0.902 & 1.740 & 2.082 & 1.652 \\ TJP4 & 0.626 & 2.378 & 2.538 & 0.831 \\ TJP5 & 1.425 & 5.261 & 4.911 & 3.527 \\ TJP6 & 1.262 & 3.845 & \textgreater{}10.000 & 2.835 \\ TJP7 & 3.150 & 4.993 & 4.427 & 8.752 \\ TJP8 & 0.663 & 2.498 & 1.835 & 0.586 \\ Glutathione (GSH) & 0.251 & 0.758 & 0.456 & 0.078 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Radical scavenging activity of eight isolated peptides (TJP1 to TJP8) from protein hydrolysate of hairtail (\(T\). _japonicas_) muscle.  
Figure 8: DPPH\(\bullet\) (**A**); HO\(\bullet\) (**B**); O\({}_{2}^{-}\)\(\bullet\) (**C**); and ABTS\({}^{+}\)\(\bullet\) (**D**) scavenging activities of eight isolated peptides (TJP1 to TJP8) from protein hydrolysate of hairtail (\(T\). _japonicas_) muscle. All data are presented as the mean \(\pm\) SD of triplicate results.
# Exploring Designer Trust in Artificial Intelligence-Generated Content: TAM/TPB Model Study
## 2 Literature Review
### Basic Hypotheses
#### 2.3.3. Designers' Perceptions and Trust Based on TAM/TPB Theory  
AIGC technology enhances industrial structure and work efficiency, demonstrating significant market potential. When users face risks associated with new technology, their subjective norms, perceived usefulness, and perceived ease of use will influence their perception and valuation of AIGC, thereby affecting designers' trust and intention to use AIGC. According to the definitions in TAM and TPB theories, perceived usefulness refers to the extent to which designers believe AIGC can improve their work efficiency, reflecting the technology's impact on productivity. Perceived ease of use denotes how easy designers find it to use AIGC, indicating the ease with which they can learn and utilize the technology. Subjective norms refer to the consideration of opinions from other users or social groups before using AIGC. Thus, designers' perceptions and trust are interrelated; perceived usefulness, ease of use, and subjective norms all positively influence trust [44]. Based on previous research findings, the following three hypotheses are proposed:  
**H3.**: _Perceived usefulness has a positive impact on designers' trust._
**H4.**: _Perceived ease of use has a positive impact on designers' trust._
**H5.**: _Subjective norms have a positive impact on designers' trust._
## 3 Proposed Approach
### The CNN Model and Its Variants
#### 3.1.1 Convolution Layer  
In the convolution layer, an input (usually a tensor) is modified by a kernel or filter that is learned. A convolution layer has three attributes:  
* Convolution kernels, which are defined by width and height;
* The number of input channels and output channels (hyperparameters);
* The depth of the convolution filter (the input channels), which must be equal to the number of channels (depth) of the input map.  
Figure 3: MFCC and Spectogram representations of a soundtrack considered in our work.  
The example below shows the convolution of a \(5\times 5\times 1\) image with a depth of 1 using a \(2\times 2\times 1\) kernel of depth 1 in order to obtain \(4\times 4\times 1\) convolved features:  
\[\underbrace{\begin{bmatrix}1&1&0&1&1\\ 1&0&1&1&0\\ 0&0&1&1&0\\ 1&1&0&1&1\\ 0&0&0&1&1\end{bmatrix}}_{\text{Image}}+\underbrace{\begin{bmatrix}0&1\\ 1&1\\ 1&0&1&1\\ 1&0&2&3\end{bmatrix}}_{\text{Core},2\times 2}=\underbrace{\begin{bmatrix}2&1&3&2\\ 0&2&3&1\\ 2&2&2&2\\ 1&0&2&3\end{bmatrix}}_{\text{Convolved image}}\]  
In this example, the kernel shifts nine times, from top left to bottom right, each time performing a matrix multiplication operation between \(K\) and the part of the image to which it is applied. In the case of images with multiple channels, e.g., RGB, the kernel has the same depth as that of the input image. Matrix multiplication is performed between the kernel and image stacks, and all results are summed using bias to output a one-channel convolved image.  
The purpose of the convolution operation is to extract high-level features. Conventionally, the first convolution layer is responsible for capturing low-level features such as edges, color, gradient orientation, etc. With additional layers, the architecture accommodates higher-level functionality. The convolution result is of two types: either the convolved feature is reduced in dimensionality relative to the input, which is the general case, or the dimensionality is increased or remains the same.
## 6 Evaluation
### Performance Evaluation  
#### 6.2.1 Data Transfer Rate  
The primary objective of this work is to reduce the impact of the proposed framework on the overall speed of data transfers. To address this objective, we evaluated and compared the total data transfer time and throughput of the proposed CRBF-based integrity framework with that of the baseline (without integrity), TPBF, and GridFTP data transfer frameworks. To evaluate the framework fairly, we used four I/O threads for the CRBF and TPBF frameworks and four parallel TCP streams for GridFTP.  
The data transfer time for all workloads (see Section 6.1.2) is presented in Figure 8a, indicating that the proposed framework has a minimal impact on the total data transfer time compared to the baseline, TPBF, and GridFTP. Although computing the Bloom filter in the proposed data integrity framework is computationally expensive, we minimize this overhead using hash optimization [44; 45] and pipelining techniques. The pipeline is designed to perform read, write, hashing, Bloom-filter-generating, and data transfer operations as efficiently as possible. Each function overlaps with the activities of another block to maximize efficiency. Additionally, Figure 8b illustrates the data transfer rate comparison for D3 workloads with varying numbers of I/O threads, revealing that the GridFTP delivers higher data transfer rates than the proposed framework when using a  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **No. of Files** & **File Size Range** \\ \hline D1 (Big workload) & 100 & 1 GB\(\sim\)2 GB \\ D2 (Small workload) & 100,000 & 1 KB\(\sim\)1 MB \\ D3 (Mixed workload) & 20,050 * & 1 KB\(\sim\)2 GB \\ \hline \hline \end{tabular}  
* D3 workload contains files of different sizes.  
\end{table}
Table 4: Workload specification.  
Figure 7: File size distribution.  
single I/O thread. However, as the number of I/O threads increases, the TPBF and the suggested framework outperform the GridFTP regarding the data transfer rate. Furthermore, Figure 8b demonstrates that the proposed CRBF-based data integrity framework imposes a minimal overhead, about 5%, on the overall data transfer rate compared to the baseline. In addition, this figure presents negligible overhead compared with the TPBF data integrity method. Consequently, we conclude that the proposed CRBF-based data integrity framework has a negligible influence on data transfer speed.
# Electromagnetically Induced Transparency in All-Dielectric U-Shaped Silicon Metamaterials
## 2 Simulation Methods  
The numerical simulations were executed in a commercial calculation software (FDTD Solutions, Version 8.19.1584, Lumerical Solution, Inc., Vancouver, BC, Canada). The simulation domain containing one unit cell of the U-shaped Si metamaterial array was illuminated by a plane wave source with spectral range from 800 to 1200 nm. Periodic boundary condition was used in both \(x\) and \(y\) directions and perfectly matched layers were employed in the \(z\) direction. The Si metamaterial array was placed on a quartz substrate. The experimentally measured Si dielectric constant (Palik) was used in the simulation [31] and the refractive index of the surrounding medium was assumed to be \(n=1.0\). A three-dimensional uniform meshing with a 5-nm mesh size was used. The transmittance and reflectance spectra of the Si metamaterial as well as spatial maps of the electric field at the interesting wavelengths were obtained from power monitors and frequency domain field profile monitors.
## 4 Validation and Analysis
### Precision Analysis of Coefficient  
Equations (44)-(47) the accuracy of the numerical value \(C_{x}\), \(C_{y}\), \(C_{x1}\), and \(C_{y1}\) increases with the increase of the subdivision numbers. Figure 5 gives the relation between the four numerical solutions and the subdivision amount \(m\) and \(n\). With five significant figures as the goal, when \(m\geq\) 35,000 and \(n\geq\) 35,000, all of the four coefficients can achieve this significance level; when \(m\geq\) 5000 and \(n\geq\) 5000, all the four coefficients can reach four significant figures.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline Pole pitch \(\tau_{n}\) & 17.7 & mm \\ Winding width \(w_{c}\) & 11.8 & mm \\ Permanent magnet thickness \(h_{m}\) & 20 & mm \\ Air gap \(h\) & 1 & mm \\ Winding thickness \(h_{c}\) & 10 & mm \\ Outer coil straight segment length \(l_{w}\) & 76.7 & mm \\ Inner coil straight segment length \(l_{n}\) & 41.3 & mm \\ Magnetic flux density vertical component \(B_{x}\) & 0.81 & T \\ Number of turns \(N_{c}\) & 218 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Electromagnetic model parameters.
## 3 Method and Data
### Coupling Coordination Degree Model  
Although econometric models reveal the effects of regional green technological innovation (RGTI) on carbon reduction in the construction industry (CRCI) to a certain extent,and mediation models further clarify the moderating role of new quality productivity (NQP), these approaches fail to thoroughly analyze the co-evolutionary mechanisms between RGTI and CRCI, particularly under the dynamic evolution characteristics of NQP. To address this limitation, a coupling coordination model was introduced for systematic analysis. Considering that the interaction between green technological innovation and carbon reduction in the construction industry is significantly influenced by the systemic role of NQP, a three-dimensional co-evolutionary analysis paradigm was constructed from the perspective of NQP.  
The coupling coordination model, an effective tool for quantifying the degree of synergistic development among system elements, functions by measuring the interactive relationships among subsystems to evaluate the functional integration and optimization of the overall system [38]. This model has been widely applied in the field of regional economics [39]. The specific calculation method of the RGTI-NQP-CRCI three-dimensional coupling coordination model is detailed in Equations (9)-(11).  
\[C=3\sqrt[3]{U_{1}U_{2}U_{3}}/(U_{1}+U_{2}+U_{3}) \tag{9}\]  
\[T=\alpha_{1}\times U_{1}+\alpha_{2}\times U_{2}+\alpha_{3}\times U_{3} \tag{10}\]  
\[D=\sqrt[3]{C\times T} \tag{11}\]  
In the formula, \(C\) represents the coupling degree of the ternary system, and \(T\) denotes the comprehensive evaluation value of the ternary system. U\({}_{i}\) denotes the index of the \(i\)-th subsystem, while \(\alpha_{i}\) represents the weight coefficient of the \(i\)-th subsystem, satisfying \(\alpha_{1}+\alpha_{2}+\alpha_{3}=1\). Considering that RGIT, NQP, and CRCI are equally significant in evaluating the overall system in this study, the weights are set as \(\alpha_{1}=\alpha_{2}=\alpha_{3}=1/3\), based on previous research. \(D\) represents the coupling coordination degree of the ternary system.  
To ensure the scientific validity and interpretability of the evaluation results, this study establishes coupling coordination degree determination criteria based on previous research [40], which are detailed in Table 1. In this context, the coordination type reflects the current overall stage of RGTI, NQP, and CRCI, while the coordination level further refines the coordination type and relates to the range of D values. This allows for a comprehensive assessment of the collaborative development levels of the three systems and their dynamic change trends.
## 4 5G and V2X Deployment Projects
### 5G-Blueprint  
The 5G cellular V2X aspect of the 5G-Blueprint project focuses on designing and validating technical architecture, business models, and governance models for cross-border teleoperated transport based on 5G connectivity. The project aims to explore the economic, governance, tactical, and operational aspects of implementing 5G-enabled transport and logistics solutions. The goal is to increase efficiency, reduce manpower shortages, and improve safety through teleoperation, data sharing, and AI utilization. The project involves several use cases, including automated barge control, automated docking, CACC-based platooning, and remote takeover. These use cases demonstrate the potential of 5G connectivity for various aspects of transport and logistics, such as improving port entry efficiency, optimizing docking operations, and enabling the remote control of vehicles and vessels.  
To achieve these objectives, the project sets technological, business, and governance goals. Technological objectives include designing and implementing a 5G network with a low-latency, reliable connectivity, and high throughput, as well as developing and deploying a teleoperated system and ensuring its safety. Business objectives involve market analysis, identifying commercial possibilities, promoting teleoperated transport, and facilitating the adoption of 5G-based solutions. Governance objectives focus on identifying and addressing regulatory issues related to cross-border teleoperated transport based on 5G connectivity. The project also addresses specific challenges and considerations related to 5G technology. It explores the combination of URLLC and eMBB to support teleoperation with a high bandwidth, a low latency, and reliability. Additionally, it validates the use of 5G in challenging environments, such as port terminals and critical infrastructure, and examines concepts like network awareness and multiple slices on the same user equipment. The project leverages 5G's capabilities, including network slicing and multi-access edge computing (MEC), to optimize connectivity and performance for different applications and scenarios.  
The outcome of the project will be a blueprint for the operational deployment of teleoperated transport solutions in the logistics sector and beyond. It aims to pave the way for the widespread adoption of 5G-enabled teleoperation and enhance cross-border transport efficiency and safety [93].
## 4 Experimental Results
### Metric Localization Results
This was because the number of matched local feature pairs was directly proportional to the lighting conditions, as shown in Figure 14. When the lighting was sufficient, the number of matches was significantly higher compared with the weak lighting conditions, and a greater number of matched local feature pairs contributed positively to the localization accuracy. However, our method maintained a difference in the accuracy of less than 0.1 m under varying lighting conditions for the same-source data. This indicated that the lighting had a very limited impact on our method's performance in the metric localization because our method could maintain a high accuracy in the nearest map node retrieval, even under different lighting conditions. Therefore, a relatively abundant set of matched local feature pairs could still be ensured when the lighting was weak.