esa-sceva/satcom-chunk-collection huggingface dataset:
# Artificial Intelligence-Empowered Doppler Weather Profile for Low-Earth-Orbit Satellites
## 4 Conclusions
It is observable that the XGB model demonstrates efficiency in the testing duration, achieving approximately 0.0997 s for elevated signal-to-noise ratios and delivering an accuracy of 87%. Nonetheless, it is noteworthy that this model incurs significant computational costs. In comparison, the IIS-LSTM model showcases a commendable accuracy of 87% while offering improved computational efficiency for lower SNRs at around 0.4651 s. Following closely are the XGB and CNN models, with respective testing times of approximately 0.5990 and 0.6120 s. Additionally, as the number of channel epochs increases, the estimation performance improves. However, it is important to consider that increasing the number of epochs may not always be beneficial, particularly in low-SNR conditions where the estimation error may converge. Specifically, our study demonstrates that the delivery rate of the packet for the spreading factor (SF12) is more than 91% with 11.5 MHz bandwidth for Globalstar and 145.8 MHz for ISS NAUKA, and the carrier frequency of 631 MHz for ISS located at an altitude of 402.3 km and 500 MHz for Globalstar at an altitude of 1414 km. The technique developed has the potential to address high orbital signal fluctuations. Our innovative model enhances the channel for capturing features that are high-dimensional and accurately measuring the Doppler shift. This approach shows promising results and has the potential to significantly enhance the estimation of the Doppler shift in LEO satellites. By leveraging sophisticated hardware architectures and cutting-edge platforms, the establishment of expansive networks can be facilitated through the deployment of efficient transmission technologies such as LoRa. Central to this process are protocols and network management strategies that prioritise reliability, transmission range, and energy efficiency. These methodologies all aim to improve robustness and increase transmission distances, albeit at the expense of certain satellite operational factors. Furthermore, it is imperative to carefully consider the integration of network management solutions and seamless incorporation into existing IoT frameworks to address the complexities and opportunities in designing, developing, implementing, and deploying wireless low-power and long-range sensors and networks comprehensively.
# Reducing the Number of Sensors in the Data Glove for Recognition of Static Hand Gestures
## 3 Materials and Methods
### Database Generation  
A special measurement procedure was applied to collect the measurement data from the data glove. The script implementing this procedure controls the readings of 10 sensors in a continuous mode, enabling quick diagnostics of the sensors' operation. After a subject assumed the final, static position of a gesture shown in Figure 4, the script saves 10 consecutive readings of sensor values to a csv file that was automatically created at the start of the measurement.  
There were 15 subjects aged from 23 to 68 who volunteered in the experiments. Five women and ten men took part in the trials. Three of the subjects were left-handed. All subjects had an unrestricted range of hand movement necessary for the study. They had similar hand size ranging from 18 to 21 cm measured from the wrist to the tip of the middle finger, with no known musculoskeletal disorders. None of the subjects had also been exposed to sign language prior to the study.  
The tests of the data glove were approved by the Institutional Ethics Committee of Technical University of Lodz (No. 1/2021, date of approval: 28 October 2021). Before starting the measurement, subjects were instructed about the course of the measurement (shown in Figure 5) and were familiarized with 16 gestures based on letters of the Polish Sign Language fingerspelling alphabet, which they were about to present. Each participant performed each of 16 static gestures 10 times. After each repetition, the hand was relaxed for a few seconds, which resulted in slight differences between repetitions.  
The measurements of each subject were taken in a natural sitting position, with the elbow resting on a surface and the forearm gently deviating from the vertical. Finger placement in the natural relaxed position was also measured in this position. In total, the database consisted of 25,550 readings of the sensors.  
The nominal resistances of the sensors differed even relative to sensors from the same production batch. The reading distribution chart shown in Figure 6 presents the median readings and outliers. The formation of outliers was caused by an imperfection in the way the sensors were connected to the conductive threads unnoticeable at the time of the study. This minor flaw was corrected, while the outliers remained in the dataset on purpose, reflecting an imperfection in the actual functioning of the device. In all the presented results, we used the data with outliers for training the classifiers.  
Figure 4: Gestures based on static letters of Polish Sign Language alphabet.
# Secure K-Means Clustering Scheme for Confidential Data Based on Paillier Cryptosystem
###### Abstract
Homomorphic encryption, particularly the Paillier cryptosystem, provides a balance between security and computational efficiency, making it a promising approach for privacy-preserving data mining [10; 11; 12] tasks. Our protocol addresses several key challenges in applying homomorphic encryption to clustering algorithms, such as efficiently computing distances, securely updating centroids, and ensuring the overall scalability and security of the process. We design our protocol to be both practical and robust, demonstrating its effectiveness through theoretical analysis and experimental validation. The primary contributions of this work are as follows: 1. We design a secure K-means clustering protocol using the Paillier cryptosystem, ensuring data privacy throughout the clustering process. 2. We address key computational challenges in applying homomorphic encryption to the K-means algorithm, including secure distance calculation, cluster assignment, and centroid update. 3. We provide a comprehensive analysis of the protocol's security and efficiency, demonstrating its practicality for real-world applications. In the following sections, we review related work in privacy-preserving clustering and homomorphic encryption, describe the Paillier cryptosystem and its application to secure K-means clustering, and present our proposed protocol in detail. We also discuss the security and performance of the protocol, validate it through experimental results, and conclude with potential directions for future research.
## 3 Methods
### Computational Layer  
This layer includes four data processing tools: data fusion, a source location estimation tool, a hazard prediction tool, and an environmental noise (false alarm rate) learning tool. The data fusion algorithm combines data from the sensors, advanced machine learning and modelling algorithms. The hazard prediction tool relies on an FOI (Swedish Defence Research Agency, Totalforsvarets forskningsinstitut-FOI) dispersion engine that allows air dispersion calculations to predict concentration levels following a chemical release [37]. The source location estimation tool runs an inverse version of the FOI dispersion engine simultaneously with an optimization algorithm to calculate the source's strength and position [36]. The last component, the environmental learning noise tool, implements algorithms that process incoming raw sensor data and checks if the situation becomes stable (through normality detection). It works as a standalone system.
# A Registration Method for Historical Maps Based on Self-Supervised Feature Matching
###### Abstract  
Comparing historical map images of the same region from different periods is an effective method for studying urban history and planning. Image registration techniques in the field of computer vision can be applied to this task. However, historical map registration faces unique challenges, including insufficient training data, variations in image sizes, and unavailable texture features. To address these challenges, we constructed a dedicated dataset of over 100 scanned historical maps, including both raw and preprocessed segmented images. We then developed an enhanced SuperGlue-based registration framework, optimized for the specific obstacles posed by historical maps, such as low texture and large image size. Additionally, we proposed a self-supervised fine-tuning feature extraction algorithm and a Transformer-based architecture utilizing graph attention mechanisms to refine feature descriptors and enhance feature matching performance. Experimental results indicate that our solution achieves superior performance compared to existing models, with RMSE reduced by up to 20%, ROCC improved by up to 10%, and processing time shortened by at least 15%.  
h
istorical map; image registration; feature description; graph attention; feature matching
# Research on the Influence of Dust Suppressants on the Coupling Behavior of Dust-Mist Particles
## 4 Results and Discussion
### Influence of Suppressant Materials on Atomization Parameters of Spray
#### 4.1.2 Velocity Distribution of Mist Droplet  
This experiment and the work of [PERSON] [16] and [PERSON] [39] reveal that dust suppressants with a lower surface tension exhibit higher initial velocities compared to water. We observed a gradual decrease in velocity for the dust suppressants, aligning with their findings. These similarities indicate that surface tension significantly influences initial mist particle velocity, while the rate of velocity decrease is influenced by the specific properties of each suppressant [16]. Figure 9 shows the change in velocity of the different dust suppressants. It can be observed that the mist particle velocity decreases from 5.0 cm to 10.0 cm along the measuring points. At 5.0 cm, the mist particle velocity of DA, which has the smallest surface tension, reaches a velocity higher than that of water. Compared to DA, the mist particle velocity of OP-10 and SDS decreases more gradually.  
Figure 9 shows that between 10.0 cm and 15.0 cm, the mist velocity of the three suppressants DA, OP-10, and SDS did not show a significant difference and remained in a reducing state. Because the 15.0 cm point is at the edge of the measuring field, the spray effect is significantly weakened, resulting in minimal speed change [40]. Based on the above discussion and comparing the results to surface tension and viscosity in Figure 2, it can be seen that changes in surface tension and viscosity within the core atomization region (5.0 cm to 10.0 cm) can significantly affect particle velocity, which is directly attributed to particle size [16; 41]. Near the nozzle (5.0 cm), viscosity has a more significant effect on mist particle size and velocity tha  
Figure 8: Particle size distribution at different measuring points.  
Figure 9: Velocity distribution of particles.
## 6 Performance Evaluation
### Simulation Results
#### 6.4.4 Transient Node Failure Test (Fourth Simulated Scenario)
From Figure 9c-d, we can observe that ASAART uses slightly lower energy consumption and a reduced number of total MAC layer delivered packets compared with the SHR and SSR protocols. This reduces the network packet collision and contention, and saves energy. In conclusion, the ASAART technique demonstrates better fault tolerant capability in the presence of transient node failure rate compared with the SHR and SSR protocols, particularly when there is too much traffic congestion in the network. Figure 9: _Cont._  
Figure 9: Transient node failure rate test (fourth simulated scenario) with source node = 40: (**a**) Average packet delivery rate (success rate); (**b**) End-to-end delay; (**c**) Energy consumption; (**d**) Transmitted MAC layer packets.
# A Survey of Scenario Generation for Automated Vehicle Testing and Validation
## 2 Terminology
### Scenario Types  
As described in [13], the classification of scenarios into functional, logical, and concrete levels provides a structured framework for scenario generation, progressively increasing the detail and specificity of the scenario parameters. The functional view serves as a high-level description, typically written in natural language by human experts, capturing the conceptual essence of traffic situations such as \"a vehicle overtakes another on a curved highway under rainy conditions.\" The logical view makes these descriptions more specific by providing parameter ranges and constraints for critical elements, enabling the generation of diverse and representative instances within defined bounds. For example, the overtaking scenario could include variables such as the range of vehicle speeds allowed and the road curvature. Finally, the concrete view provides fully parameterized instantiations of logical scenarios, detailing precise values for all elements, to ensure reproducibility and direct execution in simulation environments. For instance, the initial speeds and positions of the vehicles would be specified. This hierarchical approach supports scenario generation across development phases, allowing for both broad exploration of possible situations and rigorous, repeatable testing of autonomous vehicle systems under controlled and varied conditions. Figure 3 illustrates this categorization.
# Influence of the Quality of Consumer Headphones in the Perception of Spatial Audio
## 2 Headphones Measurements and Virtual Headphone Simulation  
It is well known in loudspeaker testing that visual cues play an undesirable role in the results provided by test subjects. Similarly, when testing headphones, tactile cues can also influence results. Consequently, it can be challenging to conduct a double-blind comparative listening test for headphones. It is difficult to hide the possible influencing variables, such as brand, design or price. In addition, the manual substitution of different headphones on the subject's head can be disruptive and introduces useless fatigue on the subject [6]. Moreover, the fitting and tactile sensations are impossible to remove, making them an important bias factor [7].  
In order to avoid these effects, it is appropriate to use a virtual headphone simulation to perform the listening tests [8; 9]. This method employs one reference headphone to simulate the different headphones under test. In this way, listeners can evaluate the simulated versions of the different headphones wearing just the reference headphone, therefore avoiding the manual change of headphones and removing the visual and tactile biases. Some other advantages are obtained with this virtual method: listeners can have immediate access to the different headphones, and the procedure test becomes more flexible, transparent, controlled and repeatable.  
The reliability of this virtual simulation method has been previously studied, finding good correlation between standard listening tests using real headphones and the virtual simulation method. However, in some cases, some discrepancy related to a specific model or sound signal [8] has been found due to the visual and tactile bias present in the standard test [10].  
Due to the great advantages of a virtual test over a standard one, this study used a virtual headphone listening test methodology. This will remove the strong bias that would appear in this study due to the great difference in appearance and fitting characteristics among the consumer headphones and high quality ones used in this test.
###### Abstract  
With the rapid development of deep learning, its powerful capabilities make it possible to perform mechanical fault diagnosis of high-voltage circuit breakers (HVCBs). Among deep learning approaches, the convolutional neural network is widely used. However, while it can extract features effectively, it also has some limitations. Specifically, it depends on a large number of training data and only takes data information into account without considering structural information. These shortcomings lead to unused information and unsatisfactory model results. To address these shortcomings, this paper proposes AKNN-DMGCN, a novel dynamic multi-attention graph convolutional network based on an adaptively constructed graph, which can achieve high accuracy and robust mechanical fault diagnosis of HVCBs. First, a novel adaptive k-nearest neighbor (AKNN) graph construction method is proposed to construct informative graphs. The AKNN method can mine the relationship between the original data samples and utilize the data and label information. Thus, it has high fault tolerance to noise signals and can construct a structure graph with rich and accurate information, which can improve the overall model performance. Then, a dynamic multi-attention graph convolutional network (DMGCN) is applied for mechanical fault diagnosis of HVCBs. DMGCN fully utilizes structural and numerical information representing HVCB signals to perform classification. DMGCN has a dynamic multi-attention mechanism with strong expressive ability, which allows it to achieve high diagnostic accuracy. The experimental results indicate that the accuracy of AKNN-DMGCN reaches 97.22% on a balanced dataset and 95.01% on an imbalanced dataset, which demonstrates that the proposed method is effective for both balanced and imbalanced samples.  
high-voltage circuit breaker; mechanical fault diagnosis; adaptive graph construction; dynamic multi-attention graph convolutional network +
Footnote †: journal: Applied  
Article
## 1 Introduction
Although extensive research has been conducted on the evolution characteristics of spatial fractures based on AE, all fractures are considered as a whole, and there is little research on fracture clusters. Therefore, this article intends to study the fracture clusters characteristics of internal fractures in rocks. Nuclear power is a clean source of energy, and with the depletion of traditional energy sources, it accounts for an increasing proportion of the energy mix. While nuclear power avoids the pollution of traditional energy sources, it brings new challenges, such as the leakage and disposal of high-level radioactive wastes. High-level radioactive waste is characterized by strong radioactivity, long half-life, and high toxicity, making its management and disposal challenging and time-consuming. It has become one of the key factors limiting the development of the global nuclear industry [12; 13]. Currently, deep geological repository in underground rock formations is recognized as a suitable method for the disposal of high-level radioactive waste [14; 15; 16]. The Beishan area in Gansu Province, China, is primarily composed of large-scale granite, which possesses good mechanical properties, stable geological structures, and minimal biological activity. Therefore, it is considered as an ideal rock formation for high-level radioactive waste disposal. After more than 30 years of repeated evaluations and demonstrations, the Beishan area in Gansu Province was eventually designated as one of the pre-selected sites for the high-level radioactive waste repository in China [17]. Many scholars have conducted research on the mechanical characteristics of Beishan granite, and have achieved rich results through analysis of spatiotemporal features, waveform, energy, amplitude and \(b\)-value (\(b\) is a parameters of magnitude and frequency) based on AE [18; 19; 20; 21]. Those researches have mostly focused on the characteristics of acoustic emissions during the failure process of granite, but there is less research on the development process of the internal fracture cluster of Beishan granite. This paper takes the construction of the Beishan high-level radioactive waste disposal repository as the background and conducts indoor triaxial experiments. During the ex periment, AE data were monitored. Based on the spatial distribution characteristics of AE, the internal evolution rule of fracture clusters in the rock was studied, and the AE characteristics of different clusters were further analyzed. An intelligent identification method for the major fracture clusters was established. The research results can provide a theoretical basis for predicting rock mass disasters in deep underground engineering projects, such as high-level radioactive waste disposal repositories.
## 6 Discussion
### Perspectives  
There are three main directions for the near-future development of the proposed system. The first involves using a dynamic cropland mask that would better reflect year-to-year land use changes. The near future will also bring crop type maps [49; 50] that, in turn, will allow crop-specific satellite-based indices from individual fields. Yet, a relatively short time series of crop type maps (since 2015 in case of Sentinel-derived ones) will limit their use in crop yield forecasting that relies on training the models on data archive. The second enhancement of the system would be to use weather forecasts to determine predictors derived at the moment from climate reanalysis. This should improve the accuracy of inseason forecasts that completely disregard the forthcoming change in weather conditions, however are only based on the average course of the weather condition over the season in previous years. A last development would be to extend the list of predictors. One group of these could be constituted of the outputs of crop growth models [43]. However, it must be emphasized that the models require extensive calibration against field data which are often not available. The second group of potential new predictors are related to less exploited satellite-derived data such as sun-induced fluorescence [51].
# A Low-Overhead Countermeasure against Differential Power Analysis for AES Block Cipher
## 3 DPA Attack and Our Proposed Countermeasure
### Our Proposed Countermeasure
#### 3.2.2 Boolean Masking  
_Original concept._ In Boolean masking, the variable sharing splits into \(d+1\) random shares (i.e., \(s_{0},s_{1},s_{2}\ldots s_{d}\)). The resultant Boolean share is computed using the exclusive-OR operation over split random shares (\(s_{0}\oplus\cdots\oplus s_{d-1}\oplus s\)). It shows that every share of \(s_{i}\) is statistically independent of \(s\) and uniformly distributed.  
_Problem implementing Boolean masking._ When implementing circuits considering the concepts related to Boolean masking over application-specific integrated circuits (ASICs) or FPGA devices, the use of different wires with different length results in masking being less appropriate. The reason is that glitches may arise when especially the combinational logic for design implementation/modeling is used. The term \"glitch\" determines the unnecessary signal transitions without functionality.  
_Solution._ To avoid glitches (useless transitions) in our design implementation, we used the Fourier expansion (in general, the Fourier expansion or series is a periodic function,  
Figure 2: Our proposed solution to avoid the zero value problem in multiplicative masking.  
which is composed of harmonically related sinusoids, combined by a weighted summation. For complete mathematical descriptions and formulations, we refer readers to [40]) along with the REBECCA's approximation. The Fourier expansion of a Boolean function derives the correlation sets. Moreover, the Fourier expansion represents the Boolean functions as a polynomial over the real domain \(\{1,\
eg 1\}\), as shown in Equation (8). REBECCA considers glitches by demonstrating the stable and transient relationship of gates. To verify the resultant correlation as an output, it requires a correlation set as an input.  
\[\begin{array}{l}\{\top,\bot\}\rightarrow\{-1,+1\}\\ (x\oplus y)\rightarrow(xy)\\ (x\wedge y)\rightarrow\left(\frac{1}{2}+\frac{1}{2}x+\frac{1}{2}y-\frac{1}{2} xy\right)\end{array} \tag{8}\]