esa-sceva/satcom-chunk-collection huggingface dataset:
# Gradient Agreement Hinders the Memorization of Noisy Labels
## 3 Proposed Method
### Class Distribution Agreement Sampling  
We present class distribution agreement sampling in this section. In order to avoid gradient disagreement caused by different input data, the samples input to \(\theta_{A}\) and \(\theta_{B}\) should share same class distribution in every iteration. However, if the samples are identical, the gradient directions for all the parameters of \(\theta_{A}\) and \(\theta_{B}\) will be exactly the same, resulting in failure to distinguish gradient disagreement caused by noisy labels. Thus, we propose a class distribution agreement sampling strategy to ensure roughly the same class distribution for the samples input to \(\theta_{A}\) and \(\theta_{B}\) in every batch while avoiding them being identical.  
Let batches input to \(\theta_{A}\) and \(\theta_{B}\) in the \(n\)th iteration as \(\mathcal{X}_{n}^{a}=\{(x_{i}^{a},y_{i}^{a})\}_{i=1}^{bs}\) and \(\mathcal{X}_{n}^{b}=\{(x_{i}^{b},y_{i}^{b})\}_{i=1}^{bs}\) with \(bs\) being the batch size. As Figure 3 presents, the sampling process is repeated every four iterative steps. In the first iterative step, \(\mathcal{X}_{n}^{a}\) is randomly sampled from the whole training set \(S\) with \(K\) annotation classes. Naturally, the annotated class distribution \(\mathcal{D}_{n}^{a}\) of \(\mathcal{X}_{n}^{a}\) can be obtained:  
\[C_{j}=\{(y_{i}^{a}==j)\}_{i=1}^{bs};\mathcal{D}_{n}^{a}=\{C_{j}\}_{j=1}^{K} \tag{1}\]  
where \(C_{j}\) represents the number of samples belonging to class \(j\) in \(\mathcal{X}_{n}^{a}\). Then \(\mathcal{X}_{n}^{b}\) is formed by randomly sampling \(C_{j}\) instances for every class \(j\) in \(\mathcal{D}_{n}^{a}\) among training samples except those in \(\mathcal{X}_{n}^{a}\). In the second iteration, we feed \(\mathcal{X}_{n}^{b}\) into \(\theta_{A}\) and \(\mathcal{X}_{n}^{a}\) into \(\theta_{B}\) to make sure the training samples input to \(\theta_{A}\) and \(\theta_{B}\) are the same in one epoch. In the next two steps, to balance the training process of \(\theta_{A}\) and \(\theta_{B}\), \(\mathcal{X}_{n+2}^{b}\) is randomly sampled from \(S\) while \(\mathcal{X}_{n+2}^{a}\) follows the class distribution of \(\mathcal{X}_{n+2}^{b}\).
# A New Enhancement Edge Detection of Finger-Vein Identification for Computer System
## 3 Proposed Finger-Vein Identification System
### Image Enhancement and AlexNet  
Current finger-vein biometrics are easily affected by external light sources, so image contrast levels differ. CLAHE [16] denoises and enhances image contrast, but the noise filtering process removes some vein character information, which decreases image quality. Therefore, this study uses POHE [17] to contrast and enhance the image. POHE uses Gaussian parameters to construct the functional model for a Gaussian kernel and regional histogram equalization (HE) to enhance the image. An image that is processed using POHE  
Figure 4: From morphology: (**a**) With Canny edge detection; (**b**) With the closing operator.  
Figure 3: The multi-layer edge detection method: (**a**) Bilateral image after Sobel operator processing; (**b**) Using (**a**) gives the pixel add weighting result; (**c**) Using (**b**) with the Canny operator.  
This study uses the AlexNet CNN because it categorizes very effectively. Unlike earlier neural networks (NN), AlexNet uses the activation function ReLu to accelerate convergence and dropout, in order to prevent overfitting. This NN has 60 million parameters and 65 million neurons and the AlexNet's network architecture contains 12 layers: 1 input layer, 5 convolutional layers, 3 pool layers and 3 fully connected layers, as shown in Figure 7.
# Magnitude Modeling of Personalized HRTF Based on Ear Images and Anthropometric Measurements
## 1 Introduction
The model provides the Log Spectral Distance (LSD) of 4.47 dB, which are lower by 0.85 dB than the DNN-based method using anthropometric data without pinna measurements. However, this model uses different machine learning models for different azimuths and elevation, and a total of 1250 models (25 azimuths and 50 elevations) have been established, which is inconvenient to use. In recent years, some studies have proposed to use HRTF as a function defined on a spherical surface to reproduce binaural signals using spherical harmonics. [PERSON] et al. [20] demonstrated that accurate positioning performance can be restored to a greater extent by using spherical harmonic representation as low as 4 th order. [PERSON] et al. [21] proposed a global HRTF personalization method, which using spherical harmonic transform as a compact representation of the HRTF magnitude spectrum, showing significant improvements upon finite element acoustic calculations. [PERSON] and [PERSON] [22] proposed to truncate the HRTF log-magnitude spectrum after Fourier series expansion, and the resulting smooth HRTF still remains perceptually relevant. On this basis, [PERSON] et al. [23] explored a method for smoothing HRTFs by utilizing a truncated spherical harmonic expansion, and the results showed that the significant smoothing of HRTF in frequency brought by the low-order spherical harmonic representation does not affect the perceived position of the sound. Based upon this previous work, we propose two hypotheses: (1) After truncating SH, the expanded and reconstructed HRTF are perceptually indistinguishable from the original. (2) The higher the accuracy of the ear recognition model, the more personalized and accurate the extracted ear features. In this paper, we propose an acoustic model consisting of two sub-networks. The first network extracts ear features using the VGG-Ear model, where VGG-Ear involves transfer learning from the VGG19 network proposed by the Visual Geometry Group at Oxford University. The second network combines parameters such as ear features, head and torso measurements, and frequency points to predict the spherical harmonic (SH) coefficients. Finally, we use the inverse spherical harmonic transform (SHT) to obtain the personalized HRTF. At the same time, the performance of the proposed method is compared with method with precise pinna measurements. The spectral distance (SD) is then used to evaluate the error between the estimated HRTF and the actual HRTF. The structure of this paper is as follows. In Section 2, the database and related parameters used in the experiments are presented. Section 3 introduces the framework and individual component modules of personalized HRTF. Section 4 analyzes the experimental results and evaluates the performance of the proposed method. Section 5 summarizes the whole paper and looks forward to future work.
# Two-Layer Security Algorithms to Prevent Attacks on Data in Cyberspace
## 5 Comparative Analysis  
Various researchers have implemented the encrypted text obtained from the Vigenere cipher on different algorithms to protect the data. Some researchers have doubled data encryption with the Vigenere cipher. Some researchers have used a single key on a hybrid algorithm. Some researchers have developed hybrid algorithms by combining different algorithms with the Vigenere cipher. A ciphertext can be generated with the help of all these algorithms, but the data cannot be saved entirely.  
In paper [1], the researcher developed an algorithm by modifying the Vigenere cipher algorithm, identified the length of the key with the help of the Kasiski test, and discussed that if the length of the key is according to the text, then it is challenging to identify the key. After that, Vigenere cipher results were implemented on the Polybius algorithm and encryption was obtained. If a security mechanism is developed for the key and text instead of increasing the key length of the Vigenere cipher, then the attack rate can be reduced, which is the drawback of this paper.  
In paper [23], a hybrid algorithm was developed by combining the techniques of the Ceaser cipher and Vigenere cipher algorithm, in which a key was generated. Then, the plaintext and key were implemented on the Caesar cipher to obtain the encrypted text. After that, the encrypted text obtained from the Caesar cipher and the same key were implemented on the Vigenere cipher, and the ciphertext was obtained. If instead of using the same key twice in both algorithms, a unique key is developed for each plaintext, then the data security of the plaintext along with the static key can be increased, which is the drawback of this paper.  
In paper [20], the ciphertext was obtained using the double encryption mechanism in which the Vigenere cipher algorithm was implemented on the plaintext, and an alphabetic encrypted text was obtained. After that, a numerical ciphertext was achieved by implementing the Polybius mechanism on the alphabetic encrypted text. If a single algorithm is made efficient instead of obtaining a separate ciphertext from the same key in the Vigenere cipher and Polybius mechanism, then the data security can be increased, which is the drawback of this paper.  
In paper [24], a hybrid algorithm was developed by combining the Vigenere cipher and the Polybius cipher, the novelty of which was to transmit the data to the receiver in encrypted form with the help of the webpage. Instead of integrating existing techniques into this algorithm, the algorithm's accuracy can be increased if state-of-the-art techniques are developed, which is the drawback of this paper
## 2 Literature Review and Theoretical Foundation
### Antecedents to Information Disclosure
#### 2.2.1 Question Sensitivity  
Question sensitivity refers to the sensitivity of the information being requested. Multiple definitions exist to describe information sensitivity [57]. For this study, question sensitivity is defined as \"material that is delicate and could be personal, political, economic, social, or cultural in nature. It can range from matters connected to national security, to personal emotions and feeling, to taboo topics which would not be shared with an outsider\" [58] (p. 67). Question sensitivity is known to change through time and vary across cultures [58]. In general, it has been shown that people are more averse to disclosing more sensitive information [52; 53; 59].  
Question sensitivity is relevant in user-chatbot interactions, as chatbots usually ask multiple questions to gain information from users, naturally ranging from more general to more sensitive in nature [60].
## 3 Results
### Determination of Parameters of the SMIKE Model Used for the Dynamic Analyses
#### 3.3.1 Experimental Evaluation of Wave Propagation Velocity
Obviously, the value obtained between supports P1 and P3 lies in between the previously mentioned velocities. For comparative purposes, the phase angle of the CSD function of the signals registered for pairs of measurement points, such as P1-P2, P1-P3, and P2-P3, were also used to determine the wave  
Based on the phase angle function, the time delay of the wave was determined for the support pairs (see Equation (19)) and the selected frequencies. As previously mentioned, the mean wave velocities between the particular piers (averaged over five series) and the mean apparent wave velocities between support pairs (averaged over different frequencies) are presented (Table 5). The discrepancies between the wave velocities obtained with both methods, presented in Table 6, do not exceed 6%. Based on the analyses of experimentally determined wave velocities, it was decided to use the mean apparent wave velocity of 240 m/s (obtained for the support pair P1-P3) as a parameter of the proposed spatiotemporal model of mining-induced kinematic excitation.
## 5 Use Case: Dynamic Radio/MEC Resource Allocation and Slicing Management in High Mobility Scenarios
### Setting up the Case Study Scenario
#### 5.1.2 Implementation Scenario  
The scenario designed to implement this use case envisions a smart city with diverse traffic-generating environments, such as those found in the city of Barcelona, as illustrated in Figure 12.  
In this scenario, the four near-RT RIC nodes are placed at different points in the city to provide the system with a wide range of traffic conditions. The configuration of the slice request generation environment is adapted to the distribution of each traffic profile. The selected values are presented in Table 6.  
Figure 12: Example of a realistic use case implementation scenario.  
For the dense urban mobility profile, a node is placed in the Example district, which is an urban area characterized by high population density and intense user activity, including video streaming, online gaming, and real-time conferencing. The high arrival rate reflects the continuous and relentless demand for resources in such environments. Additionally, the life cycle is relatively short, as urban traffic often involves brief and rapid interactions, such as short video streams or voice calls.  
For the moderate suburban mobility profile, a node is located in the Sarria-Sant Gervasi district, which is characterized by moderate user density and more stable traffic patterns compared to urban environments. The arrival rate reflects the predictable and less dynamic nature of traffic in these areas. Requests in this profile typically have a longer duration, such as streaming or browsing, representing sustained interactions with moderate resource requirements.  
The mobility profile for highways is applied by positioning a node near the Ronda de Dalt. Highways are characterized by low but critical traffic generated by moving vehicles, including autonomous driving updates, infotainment systems, and safety alerts. The low arrival rate reflects the reduced number of interactions typically expected in such scenarios. Requests are generally of long duration due to resource-intensive applications, such as navigation systems or continuous vehicle monitoring. This ensures that the system is tested to manage prolonged demands with high reliability.  
For the eventual mobility profile, a node is placed in the district of Les Corts near the Camp Nou stadium, where events such as sports matches generate highly localized and intense bursts of traffic. The very high arrival rate captures the sudden surges in demand that occur during these events. Requests are short-lived, reflecting the rapid turnover in resource allocation, as users frequently switch activities such as posting on social media or streaming content.
# Android Malware Detection Based on Hypergraph Neural Networks
## 3 Hypergraph Neural Networks  
### Preliminaries  
A hypergraph is a more general topology than a graph that is capable of modeling multiple relationships between discrete data. A hyperedge in a hypergraph can contain multiple vertices, and thus, hypergraphs are capable of modeling higher-order relationships among vertices. In terms of the definition of hyperedges and edges, graphs are a special case of hypergraphs. There is a large amount of discrete data with complex relationships in the real world, but modeling binary relationships between discrete data via graphs can result in a loss of information, whereas hypergraphs are a more direct and natural way of modeling [23]. Figure 1 shows the topology difference between graphs and hypergraphs.  
Let \(\mathcal{H}=\langle\mathcal{V},\mathcal{E}\rangle\) be a hypergraph, where \(\mathcal{V}=\{\mathsf{v}_{1}\mathsf{v}_{2},\cdots\mathsf{v}_{n}\}\) denotes the vertex set containing \(|\mathcal{V}|=\mathsf{n}\) vertices and \(\mathcal{E}=\left\{\mathsf{e}=\left(\mathsf{v}_{\mathsf{k}_{1}},\cdots, \mathsf{v}_{\mathsf{k}_{j}}\right)\middle|\mathsf{e}\subset\mathcal{V}\right\}\) denotes the set of hyperedges. In a hypergraph, the vertex-hyperedge adjacency matrix is represented by \(\mathrm{H}\in\mathbb{R}^{|\mathcal{V}|\times|\mathcal{E}|}\). When a vertex \(\mathsf{v}\) belongs to a hyperedge \(\mathsf{e}\), \(\mathrm{H}(\mathsf{v},\mathsf{e})=1\), otherwise \(\mathrm{H}(\mathsf{v},\mathsf{e})=0\). A priori weights of the hyperedges can be represented by a diagonal matrix \(\mathrm{W}\in\mathbb{R}^{|\mathcal{E}|\times|\mathcal{E}|}\) where \(\mathrm{W}(\mathsf{i},\mathsf{i})=\mathrm{w}(\mathsf{e}_{\mathsf{i}})\). The degree of vertices is computed by \(\mathrm{d}_{\mathcal{V}}(\mathsf{v})=\sum_{\mathsf{e}}\mathrm{H}(\mathsf{v}, \mathsf{e})\mathrm{w}(\mathsf{e})\), and the degree of hyperedges is computed as \(\mathrm{d}_{\mathcal{E}}(\mathsf{e})=\sum_{\mathsf{v}}\mathrm{H}(\mathsf{v}, \mathsf{e})\). The matrix representation of the degree of vertices and hyperedges is denoted by \(\mathrm{D}_{\mathcal{V}}\) and \(\mathrm{D}_{\mathcal{E}}\), respectively. They are both diagonal matrices. The Laplacian matrix of \(\mathcal{H}\) is denoted by \(\mathrm{L}=\mathrm{I}-\mathrm{H}\mathrm{D}_{\mathcal{E}}^{-1}\mathrm{H}^{ \mathsf{T}}\). These concepts are clarified here in order to better explain hypergraph neural networks later in the paper.
## 3 Review of Trajectory Planning  
First, the trajectory-planning problem consists of finding a relationship between two elements belonging to the domains of space and time. Thus, the trajectory is usually expressed as a parametric function of time, providing the corresponding desired position at each moment. For exoskeletons, trajectory planning can either be carried out in task space or in joint space, depending on the control method. It can also be a point-to-point or predefined path. Inverse and forward kinematics are very important for moving from one space to another. Inverse kinematics consists of finding the Cartesian position of the end effector given the angular position of a joint. As illustrated in Figure 1, trajectory planning provides the desired trajectories of the end effector in task space or the desired joint angles in joint space. We highlight the high stakes of inverse kinematics in the trajectory-planning problem. When planning in task space, the Cartesian trajectory is first transformed into a sequence of joint displacements through inverse kinematics (IK). Then, to complete the planning process, the motion profiles for each joint are generated by interpolation while considering a specific set of constraints (in accordance with the design requirements) [20] or by approximation [21]. The common method of interpolation is by using third- or fifth-degree polynomials [9; 12; 22; 23]. In concrete terms, the desired motion is defined by only assuming initial and final points or by also considering a set of intermediate via-points that must be properly interpolated. These polynomials make it possible to describe the trajectory between any two positions. For example, [PERSON] et al. [12], and [PERSON] et al. [24] employed fifth-order polynomials for the trajectory planning of an upper-limb rehabilitation exoskeleton. There are many potential advantages to using B-spline as the interpolation function. Similarly, when trajectories are planned in joint space, the joint positions obtained manually or by inverse kinematics are interpolated. Planning in joint space involves a higher computational cost because the transformation from Cartesian to joint-space coordinates takes place in real-time since control happens at the joint level. However, planning in joint space is faster. The difficulty of trajectory planning in task space is related to the resolution of the inverse kinematics problem. Depending on the control method and the space in which constraints are given, planning in each space is applicable. Several methods are available for planning the desired movement of upper-limb exoskeletons, and they can be classified into two main approaches.  
Figure 3: Papers retrieved from January 2009 to August 2020 for the two most common trajectory-planning methods.
## 3 Results and Discussion
### Physical Characterization
#### 3.2.2 Thermal Conductivity Results  
The thermal conductivity is measured [M], as shown in Table 2, with an overall average value of 0.040 \(\pm\) 0.001 W/mK. Figure 8 shows the thermal conductivity measurements of the insufflated samples; these results indicate that the prototypes exhibit favorable thermal performance, suggesting that the material is suitable for building insulation and supporting the potential for developing new insulation materials based on agricultural waste. This result is comparable with other rice husk materials, with values of 0.37 to 0.042 W/mK [11]; and with other lignocellulosic materials, like wheat straw, hemp, eucalyptus bark fiber, algae, and mushroom insulation material, which are in the range of 0.32 to 0.40 W/mK [35; 47; 48; 49]. The thermal conductivity values obtained for rice husk are comparable to those conventional insulation materials currently available on the market, like mineral wool (0.033-0.038 W/m\(\cdot\)K) and expanded polystyrene (0.035-0.040 W/mK) [50; 51], underscoring its potential as a competitive alternative for thermal insulation.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Thermal Conductivity [W/mK]**} & \multirow{2}{*}{**AVG**} & \multirow{2}{*}{**SD**} \\ \cline{1-1}
**Measurement (M)** & & & & & & \\ \hline Sample 1 & 0.040 & 0.035 & 0.037 & 0.041 & 0.038 & 0.0028 \\ Sample 2 & 0.041 & 0.041 & 0.042 & 0.040 & 0.041 & 0.0008 \\ Sample 3 & 0.040 & 0.041 & 0.040 & 0.040 & 0.040 & 0.0005 \\ Sample 4 & 0.041 & 0.040 & 0.041
# Autonomous Active Tag Using Energy Harvesting Strategies
## 2 Materials and Methods
### Processing Module
#### 2.1.3 Memory  
The EEPROM is the M95M02, which has a capacity of 2 Mbit and works with a frequency of 5 MHz. A low power consumption EEPROM has been chosen with enough capacity to give versatility to the autonomous active tag.  
The microcontroller manages the memory modes using an SPI bus communication and it has read/write access. By default, this submodule is in standby mode, which is the lowest power consumption mode that the memory has. When the microcontroller needs to perform a read/write operation, it switches the memory mode to active and returns to standby once finished.
###### Abstract  
The traditional architectural design of facilities requires that maintenance workers refer between plumbing layout drawings and the actual facilities in complex, hidden, sealed in-wall, or low illumination environments. The purpose of a Building information modeling-based Augmented Reality Maintenance System (BARMS) in this study was to provide a smartphone-based platform and a new application scenario for cooling tower and pipe shutdown protocol in a real-world old campus building. An intelligent instruction framework was built considering subject, path, and actions. Challenges and solutions were created to monitor the subject and maintenance protocol while moving from inside to outside, between bright and dark environments, and when crossing building enclosures at roof level. Animated instruction using AR was interactive and followed the knowledge and management protocols of associated instruction aids. The results demonstrated a straightforward mapping of in-wall pipes and their connected valves, with practical auxiliary components of walking direction and path guidance. The suggested maintenance routes also ensured a worker's safety. Statistical analysis showed a positive user response.  
Keywords:marketless augmented reality; intelligent instruction; smartphone app; architectural facility maintenance; building information modeling +
Footnote †: journal: Applied Sciences