esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Consistency Tests  
Consistency tests for penetration (ASTM D5), softening point (ASTM D36) and viscosity (ASTM D4402) were conducted in accordance with their relative standard to evaluate the effect of ageing on the unmodified and modified binders. The penetration test was carried out with the following procedure: a needle of specified dimensions was allowed to penetrate a sample of bitumen under a known load at a fixed temperature for a known time. Ageing reduces the penetration of the needle through bitumen after the rolling thin film oven test (RTFOT). The reduction in penetration will be more when the PAV procedure is applied. The penetration ageing index is used to characterize the increase in bitumen hardness after ageing. It is calculated according to Equation (1):  
\[\text{Ageing Index for a test}=\frac{\text{Aged result test value}}{\text{ Unaged result test value}} \tag{1}\]  
The softening point test is a good indicator of the sensitivity of the bitumen. The softening point temperature is the temperature where bitumen softens and eventually deforms until it touches the bottom plate, 25 mm below the ring in the test. After ageing, the softening point temperature increases, indicating an increase in the hardness of the bitumen. The ageing index was calculated using Equation (1), with an increase in the ageing index indicating an increase in the softening point after ageing.  
The increase in softening point and the reduction in penetration for bitumen at various ageing stages are used to determine the changes in the consistency parameter as a function of temperature, defined as temperature susceptibility. To observe the temperature susceptibility of the modified binders, the penetration index (PI) should be calculated using Equation (2):  
\[\text{PI}=\frac{1952-500*\log(\text{Pen}_{25})-20\times\text{SP}}{50\times\log (\text{Pen}_{25})-\text{SP}-120} \tag{2}\]  
The PI can be used to give a good approximation of the behavior to be expected. The PI calculated from one penetration and one softening point may vary from the values calculated from two penetration values, and for this reason, confirmation using stiffness or viscosity measurements is desirable [1].  
The Brookfield viscometer was used to measure the resistance of the bitumen to flow. Ageing causes a change in viscosity. The change in viscosity is selected for the evaluation of the ageing effect on modified bitumen. The ageing index is the ratio of the bitumen viscosity after ageing to that before ageing. It is commonly used to normalize the ageing behavior of samples with different viscosities, such as modified bitumen. The viscosity ageing index is calculated according to Equation (1).
# An Improved Wavelet Modulus Algorithm Based on Fusion of Light Intensity and Degree of Polarization
Due to the influence of lighting conditions, weather conditions, and observation locations, the images have noise and low contrast, and this edge information cannot be accurately obtained. The polarization image is a beneficial supplement to the intensity image, which can reflect the material type, surface roughness, improve the contrast between the target and the background, and highlight the edge and detail information [14,15]. Therefore, the fusion of the light intensity edge and polarization edge is an effective method to detect more edge details. In this paper, an improved wavelet modulus maxima algorithm for the fusion of light intensity and degree of polarization is proposed. Firstly, the principle and flow of the algorithm are given. Then, an improved wavelet modulus maxima algorithm is introduced, which can accurately obtain the edge of light intensity image and polarization image. Then, the method of edge refinement and fusion to obtain the light intensity and edge polarization degree is introduced, which can increase the effective edge and eliminate false edges. Finally, we verify the effectiveness of the proposed algorithm through simulation experiments. In this paper, the improved wavelet modulus maxima algorithm for the fusion of light intensity and degree of polarization can solve the problems of poor edge connectivity and adaptive ability of traditional wavelet modulus maxima algorithm, and can overcome the deficiency of less information and low accuracy of edge detection using light intensity image only. The research in this paper provides a reference for edge detection of images with serious noise interference and low contrast.
# Multi-UAV Conflict Resolution with Graph Convolutional Reinforcement Learning
## 4 Experimental Setup
### Training Environment
#### 4.3.3 Reward Function  
Once the agents take an action according to their policy, they will receive a reward from the environment \(r_{i,t}\) for the current time step, which indicates the quality of the action. Thus, a carefully constructed reward function is crucial in achieving desirable performance [25].  
In our case, the reward consists of three terms. First, the _number of conflicts_ term punishes agents according to the number of conflicts. The more conflicts the agent is in, the more it will have the incentive to solve the conflicts. Furthermore, the _deviation term_ penalizes the agents for solutions that drift the agent from its original track. In this work, if an agent has deviated more than \(90^{\circ}\) from the original route, it is penalized heavily. In cases where it has not, it is penalized as a fraction of the current deviation to the maximal deviation.This fraction is proportional to the maximal deviation. Such a term indirectly also incentivizes the agents to solve the conflicts as soon as possible, as the quicker the conflicts are solved, the less of a negative reward the agent will get. Lastly, through the _severity term_, the agents are encouraged to solve the most severe conflicts first. This term considers more severe conflicts, i.e., smaller distance at CPA, as more important to solve first. Formally, the reward function is as follows:  
\[\begin{split} r_{i,t}=w_{1}\sum_{\vec{\varepsilon}(i)}-1+w_{2} \begin{cases}-\frac{|\mu-\mu^{\prime}|}{90}&\text{if }|\mu-\mu^{\prime}|<90\\ -10&\text{otherwise}\end{cases}\\ +(-w_{3}(1-exp(1-\frac{1}{(\frac{d_{cpu}}{d_{thresh}})^{1/2}})))\end{split} \tag{8}\]  
where \(w_{1},w_{2},w_{3}\) are positive weights that indicate the importance of each term, \(\mathcal{E}(i)\) indicates all the agents that have an edge with \(i\), \(\mu\) and \(\mu^{\prime}\) are the original and current heading and \(d_{cpu}\) and \(d_{thresh}\) are the distance at CPA self-separation distance. The formulation of the _severity term_ is used to exponentially penalize conflicts with higher severities. It ranges from 0 to 1. In this paper, \(w_{1},w_{2},w_{3}\) are kept equal, however in future work these can be extended to be learnable parameters. The total reward for a given time step \(t\) is:  
\[R_{t}=\sum_{i}^{N}r_{i,t} \tag{9}\]  
where \(N\) is the total number of agents.
# Reversible Data Hiding in Encrypted Images Based on the Mixed Multi-Bit Layer Embedding Strategy
## 3 Proposed Scheme  
While ensuring that both the cover image content and secret data can be securely transmitted and restored, to improve the embedding capacity and decryption quality of the cover image, this paper proposes a separable RDH-EI scheme.  
The scheme includes image pre-processing, image encryption, data embedding, and data extraction and recovery. Figure 1 shows the basic framework of the proposed scheme. The sender segments the cover image into non-overlapping blocks by two-round segmentation and classifies the blocks into available hidden blocks (AHBs) and unavailable hidden blocks (UHBs) based on the correlation of pixels. Then, it generates a location map and creates the hidden room by encoding the pixel information in the AHBs in turn. Next, the cover is encrypted with an encryption key _Ke._ Finally, all data, including secret data and auxiliary data, are embedded by two embedding strategies (AMLE and the FMLE) based on the hiding key _Kd_. After transmission, the receiver can extract the required data and recover the cover image separately, depending on the type of key possessed.
## 2 Applicability of Blockchain in Sports
### Sports Event Management  
Managing large-scale sports events is a complex task that demands high levels of efficiency and fraud prevention. Blockchain enhances transparency by enabling digital issuance and the validation of tickets using non-fungible tokens (NFTs) and smart contracts [8]. For example, a blockchain-powered ticketing system eliminates counterfeit tickets by tracking ownership and resale transactions securely [17]. NBA teams like the Sacramento Kings and UEFA already experimented.  
The 2018 Asian Games marked one of the earliest trials of blockchain technology in live sports, integrating RFID mechanisms to prevent ticket fraud and streamline resale markets. While technologically modest by today's standards, it demonstrated the feasibility of blockchain ticketing well before the widespread adoption of NFTs and smart contracts [26]. More recently, blockchain-based ticketing and fan engagement were prominently featured during the 2022 FIFA World Cup, where fan tokens enabled by platforms like Socios.com provided interactive experiences for millions of supporters. Similarly, the 2023 NBA All-Star Game piloted decentralized ticket distribution and NFT-based access, reflecting a maturing ecosystem of blockchain in global sports events. Beyond fraud prevention, blockchain reduces ticket speculation and dynamic pricing challenges, benefiting both organizers and attendees. FutbolCoin facilitates contracts and transactions between sports clubs, players, and agents using smart contracts. UEFA, the European football governing body, implemented blockchain ticketing in 2018 and 2021 to prevent fraud and scalping during major tournaments. The system distributed 1 million tickets via a blockchain-based mobile app for the 2018 Europa League Final and mobile-only ticketing at the EURO 2020 tournament. The impact was reduced ticket fraud and scalping, and improved fan experience. The study supports the findings that blockchain enhances ticketing security and transparency, and validates the importance of Smart Tickets, a key variable in the study [27].
# Nonlinear Dynamics Analysis of Disc Brake Frictional Vibration
## 2 Dynamical Modeling  
A typical disc brake consists of the caliper, brake pad, brake disc and piston, etc. Its structure and principle are shown in Figure 1. The brake calipers are fixed to the axle, and the friction pads are fixed to the support plate and are driven by pistons to apply braking force to the brake discs.  
According to the installation and operation conditions of the disc brake shown in Figure 1, a six-degree-of-freedom dynamics model of the disc brake system was established, as shown in Figure 2. The x-axis is parallel to the brake disc plane and the y-axis is perpendicular to the brake disc plane. Where the fixed brake pad, brake disc, and movable brake pad masses are \(m_{1}\), \(m_{2}\) and \(m_{3}\), respectively, and \(m_{1}=m_{3}\); the upper and lower brake pads have support stiffness \(k_{1}\), \(k_{3}\), damping \(c_{1}\), \(c_{3}\), and \(k_{1}=k_{3}\), \(c_{1}=c_{3}\), respectively; the normal support stiffness and damping between the fixed and movable brake pads and the brake disc are \(k_{12}\), \(c_{12}\), and \(k_{23}\), \(c_{23}\), respectively; the horizontal displacements are \(x_{1}\), \(x_{2}\), and \(x_{3}\) respectively; the normal displacements are \(y_{1}\), \(y_{2}\), and \(y_{3}\) respectively; \(k_{2x}\), \(k_{2y}\), and \(c_{2x}\), \(c_{2y}\) are the stiffness and damping between the brake disc and the support respectively; \(P\) is the pressure applied to the brake pad by the wheel cylinder piston; \(v\) is the initial velocity when the car starts braking. Based on the state of motion, the nonlinear vibration Equation (1) for six degrees of freedom is established.  
Figure 1: Schematic diagram of disc brake principle.  
Figure 2: Dynamic model of disc brake system.  
The stiffness matrix is as follows:  
\[[K]=\begin{bmatrix}k_{1}&&&&\\ &k_{12}&&\\ &&k_{2x}&&\\ &&&k_{2y}&&\\ &&&&k_{23}&\\ &&&&&&&k_{3}\end{bmatrix}_{6\times 6} \tag{7}\]
# Semantic-Aware Low-Light Image Enhancement by Learning from Multiple Color Spaces
## 4 Model Details
### Semantic Feature Enhancement (SE) Module  
Low-light image enhancement networks focus on illumination adjustment and detail restoration, while semantic segmentation networks specialize in scene understanding and  
Figure 1: SKF Framework Architecture Diagram.  
object recognition. Due to the heterogeneity between these two types of features, semantic information fusion must account for their differences.  
This study proposes the Semantic Feature Enhancement (SE) module, which fuses semantic and image features. It uses a pre-trained HRNet to extract multi-scale semantic features, which are then combined with image features via a cross-modal interaction mechanism. This involves computing similarity between reference and target features and converting semantic info into attention weights that enhance the network's performance. Its detailed structure is shown in Figure 2.  
As shown in Figure 1, the SKF framework includes three SE modules, extracting semantic features \(F_{s}^{b}\) and image features \(F_{i}^{b}\) at different resolutions \((\left(H/2^{4-b},W/2^{4-b}\right),\quad b=0,1,2)\). Pixel-wise interactions are performed within the SE module to generate the fused feature map \(F_{o}^{b}\).
## 3 Results and Discussion
### Volatile Composition of Single-Dose Espresso Coffee-Based Blends

Table 1: _Cont._  
\begin{table}
\begin{tabular}{c c c c} \hline
**Compound** & **CAS Number** & **Reported \({}^{\bullet}\)** & **Aroma Descriptor \({}^{\text{b}}\)** & **References \({}^{\text{c}}\)** \\ \hline
2-(Menovymethyl)furan & 13679-46-4 & [33] & Burnt, herbal, potato-like & [2, 37] \\
2,3,4-Trinethylfuran & 10599-57-2 & [33] & Sweet, bread-like, caramel-like, \\  & & & cinnamon-almond-like, bitter \\  & & & \\
2-(2-Propeny)furan & 75135-41-0 & [33] & - \\
5-Methyl-2(3H)-furanone & 591-12-8 & [37, 59] & Sweet, herbaceous, tobacco-like, coffee, earthy, raw \\
2-Furanmethanol & 98-00-0 & [33] & Slightly caramel-like, warm, oily, burnt, bitter \\
2-5-Diethyltetrahydrodofuran & 41239-48-9 & Sweet, herbaceous, caramel-like & [36, 37, 41] \\
2-Butylfuran & 4466-24-4 & [33] & - \\ Furfuryl formate & 13493-97-5 & [33] & Floreal \\
2-Acetylfuran & 1192-62-7 & [33] & Balsamic-sweet, tobacco-like, floral, balsamic-cinnamic, \\  & & & spicy, roasty \\ \(\gamma\)-Butylrolactone & 96-48-0 & [33] & Sweet, slightly buttery \\
2,3,4-5-Tetramethylfuran & 10599-58-3 & [33] & - \\
3-(1-Dimethylethyl)-2-5-dihydrofuran & 34318-48-4 & - \\
1-(2-Fury)-2-propanone & 6975-60-6 & [33] & Sweet, fruity-caramel-like, spicy, radish \\
2-Methyl-5-propylfuran & 5505-95-95-95 & [33] & Candy, fruity-sweet \\ Dihydro -5-methyl-2(3H)-furanone & 108-29-2 & [33] & Sweet, hay-like, tobacco-like, herbaceous \\
5-Methylfurral & 620-02-0 & [33] & Sweet, spicy, caramel \\
2-Acetyl-5-methylfuran & 1193-79-9 & [33] & - \\ Methyl12-faroate & 611-13-2 & [33] & Berry-like, fruity, winney, mushrooms-like, fungus-like, \\  & & tobacco-like & [34, 36] \\
2-Pentifuran & 3776-69-3 & [33] & Fruity, green bean, metallic, vegetable \\ Benzofuran & 271
## 1 Introduction
In contrast to previous works, the proposed approach does not have route discovering phases to allow the communication among the nodes. Moreover, it considers arbitrary trajectories of the mobile nodes instead of pre-know information about them. Intensive simulation study of the proposed protocol is performed, and its overhead is evaluated against two reference approaches, namely: (a) an optimal one, which takes advantage of the global system state knowledge to minimize the needed transmissions; and (b) a flooding one, which provides maximal possible overhead due to the naive retransmission of the alarm by all the sensors. The remainder of the paper is organized as follows: Section 2 discusses related works in the area. Section 3 presents the target application and the studied scenario. Section 4 describes the proposed bio-inspired networking strategy to perform the cooperation among mobile and static sensor nodes, while Section 5 presents and discusses experimental results. Finally, Section 6 concludes the paper with final remarks and directions for future work.
# Enhancing Air Quality Prediction with an Adaptive PSO-Optimized CNN-Bi-LSTM Model
## 4 Experiment Analysis
### Visualization and Analysis of Results  
Visualizing the model's prediction results against real-world data, as performed in Figures 13 and 14 by comparing randomly selected periods from datasets 1036A and 1037A, demonstrates the proposed model's performance. The figure showcases instances where the APSO-CNN-Bi-LSTM model excelled in accurately predicting sudden temporal variations in air quality, showing a closer alignment with actual readings than alternative models. This precision in capturing abrupt changes is fundamental in air quality prediction since sudden spikes or drops in pollutant levels can have immediate implications for public health advisories and environmental management decisions. The minor discrepancy between predicted and actual values underscores the model's ability to effectively learn and replicate the complex dynamics of air quality fluctuations, even during periods of instability. By accurately tracking these fluctuations, the APSO-CNN-Bi-LSTM model offers decision-makers a reliable tool to anticipate and respond to air quality events promptly. This heightened accuracy can support proactive measures to mitigate exposure risks, such as issuing timely alerts, adjusting industrial operations, or implementing temporary emission controls, thereby contributing to more effective environmental governance and public health protection. Overall, the graphical representations in Figures 13 and 14 provide compelling empirical evidence of the model's exceptional predictive capabilities, closely mirroring actual air quality measurements. These results underscore the model's effectiveness and superiority over other approaches in addressing the complex challenges associated with predicting air quality dynamics. The visual alignment between predicted and actual values highlights the model's robustness and reliability in real-world applications. The prediction time step for this part is set to 1 h.  
Figure 14: Comparison of prediction results (1037A).  
Figure 13: Comparison of prediction results (1036A).
# Electroglottograph-Based Speech Emotion Recognition via Cross-Modal Distillation
## 2 Materials and Methods
### Methods
#### 2.1.2 Student Model  
The student model was based on only EGG signals and was composed of three stages as well. The fundamental frequency (\(F_{0}\)) is a commonly-used excitation feature to characterize vocal cord vibration. The fundamental frequency intensity and duration help in the analysis of prosody factors according to [PERSON] et al. [41]. Thus, we chose the fundamental frequency (\(F_{0}\)) as the input of our student speech emotion recognition model. The extraction of \(F_{0}\) from EGG signals was based on [42], as shown in Figure 3.  
Firstly, we extracted the voiced segments from the raw \(EGG\) signals according to the short-time logarithmic energy to avoid the effect of unvoiced segments on the estimation of the \(F_{0}\). Then, the fundamental frequency was estimated by the auto-correlation method, which is based on the periodical change in the amplitude of EGG signals, formulated as Equation (1).  
\[F_{0}=\frac{f_{s}}{\frac{f_{s}}{f_{max}}\leq k\leq\frac{f_{s}}{f_{min}}} \tag{1}\]  
where \(x_{EGG}(m)\) and \(x_{EGG}(m+k)\) are two different sample points of the input EGG signal. \(f_{s}\) is the sampling rate. \(f_{max}\) and \(f_{min}\) are the maximum and minimum of the \(F_{0}\), respectively.  
As the method of \(F_{0}\) extraction will cause erroneous values [43], we adopted the same smoothing method as [44] with bidirectional searching as proposed by [PERSON] et al [45] after the extraction of \(F_{0}\).  
When considering the emotion feature encoder, as the fundamental frequency is a 1D acoustic feature with strong temporal correlation, we selected Bi-LSTM (bidirectionally long short-term memory) as the feature encoder for its superior performance in sequence processing and previous speech emotion recognition tasks. After the comparison of different depths of the layer, we finally adopted a 4-layer Bi-LSTM with a fully connected layer as the student speech emotion recognition model, illustrated in Figure 4. Each bidirectionally LSTM contains 512 bidirectional LSTM cells (256 forward nodes and 256 backward nodes) and finally generates a deep emotion feature vector with a length of 512. The dimension of the student feature was restricted the same as the teacher's feature output to learn the distribution of the teacher's features via cross-modal emotion distillation.  
Figure 3: The structure of the EGG feature exaction module.
## 4 Results and Discussion
This approach shows that the proposed method is capable of achieving highly accurate results while using only one imaging modality, which is FLAIR MR images. The results from this study support a potential future practice of using less image modalities for brain tumor grading. In addition, it implies that the image acquisition time can be reduced since one imaging modality is enough to do the diagnostic classification. In [38], the authors also used only FLAIR MR images for detecting and segmenting brain tumors. The results show that with the use of only one imaging modality, FLAIR MR, they were able to obtain accurate performance with a classification precision of 87.86% and a sensitivity of 89.48%. Another concern is related to the use of only axial MR images. We opted for the axial plane MR images as they have less noise and higher resolution in comparison to sagittal and coronal plane slices. In contrast, using blurry thin coronal and sagittal images can decrease the performance of the predictive system. Finally, it can be seen from studies in the literature such as work by [PERSON] et al. that using axial plane images for tumor detection and classification is a common practice since it yields better overall results [39]. The 12-layer ConvNet model presented in this paper is compromised of convolutional, sub-sampling, dense and fully-connected output layers. The overall accuracy achieved is 91.16% in this study. This outcome exceeds findings from reports in the literature where the maximum accuracy for this classification problem achieved was between 85-90% [28]. In contrast, the results by [PERSON] et al. discuss binary classification with discrimination of metastases from gliomas and the discrimination of high-grade from low-grade gliomas [28]. This is differentiated from the current report, which presents results towards multiple classes of classification in order to discriminate between healthy brains, brains with low-grade glioma and brains with high-grade glioma. Additionally, the work reported here utilized only FLAIR weighted MR scans, and we did not assist the algorithm with any other MR image types such as T1- or T2-weighted images. The studies by [PERSON] et al. used both T1- and FLAIR-weighted MR images [28]. Overall, the results reported in this study are innovative and can be considered as the state of the art for classifying brain scans using ConvNet for the neural network architecture.