esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimizing Retention Bunkers in Copper Mines with Numerical Methods and Gradient Descent
## 4 Results
### First Stage of Optimization  
Figure 10 shows linear response surfaces as a function of independent factors for the first stage of the simulation. Table 4 presents the results of the Analysis of Variance (ANOVA) between each independent factor and the obtained responses.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Independent Factor** & \(\mathbf{F}\) [**kN**] & \(\mathbf{v}\) [**m/s**] & \(\mathbf{Q}\) [**Mg/h**] \\ \hline \(L\) [m] & 0.041 & 0.488 & 0.243 \\ \(d\) [m] & 0.887 & 0.605 & 0.695 \\ \hline \hline \end{tabular}
\end{table}
Table 4: ANOVA test results (\(p\)-value) for the first stage of optimization.  
Figure 10: Response surfaces of the experiment for the first stage of the simulation.  
Figure 9: Parameters of the calibrated primary bin operation: (**a**) load on the bin drawer, (**b**) retention bunker filling, (**c**) bin capacity, and (**d**) average particle velocity on the bin drawer.  
Based on the obtained response surfaces and ANOVA results, it is clear that in this stage of the simulation, the length of the bin drawer \(L\) had the greatest impact on each of the dependent factors. A statistically significant impact of the bin length was observed only regarding the pressure force \(F\) on the drawer (\(p\)-value = 0.041). Given that the primary aim of the first stage was to reduce the pressure on the bin's drawer, the results are considered satisfactory as they correctly identified the influencing independent factor.  
The offset of the bin from the axis of the bunker \(d\) had the most significant impact on capacity \(Q\) and particle velocity \(v\), but this impact was considerably less pronounced than that of changing the length of the bin drawer \(L\). No statistically significant effect was obtained for any case.  
Based on the obtained response surfaces, a simplified optimization was carried out for the first stage of the simulation. In light of a 54% reduction in vertical force \(F\) on the drawer and only a slight decrease in capacity \(Q\) by 4%, the best variant was considered to be one where the length of the bin's drawer \(L\) was shortened by 1 m to 2.98 m, and the bin was offset \(d\) 1 m from the axis of the bunker compared to its current position. In this configuration, the vertical force \(F\) loading the drawer amounted to 155.5 kN, the average particle velocity \(v\) was reduced to 0.35 m/s, and the capacity \(Q\) remained practically unchanged, estimated at 484 Mg/h. The proposed design changes, based on the results of the first stage of the simulation, were introduced into the solid model of the bin and served as an entry point for the second stage of simulation.
# Review of Recent Advances in the Application of the Wavelet Transform to Diagnose Cracked Rotors
## 6 Conclusions  
Here, the literature relating to the application of the Wavelet theory to cracked rotor diagnosis has been briefly reviewed. This revision of the literature has covered the different ways in which Wavelet Transform has been applied, its combination with intelligent classification systems, wavelet parameters selection, and the presentation of results. The future prospects for the techniques currently used have been discussed. It is concluded that there are a lot of different techniques that have worked properly for crack diagnosis, but only under certain conditions. Extrapolation of these methods to other conditions, and benchmarking between the different proposed techniques for crack detection are needed.  
**Acknowledgments:** The authors would like to thank the Spanish government for financing through the CDTI project RANKINE21 IDI-20101560.  
**Author Contributions:** The three authors have been working in this field for several years. [PERSON] has wide experience in this area, and has looked a lot of bibliography and was in charge of the introduction section. [PERSON] has wide experience in working with wavelets and was in charge of the wavelets section and its bibliography. [PERSON] wrote the paper and was in charge of the section of results presentation and prospects.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Abbreviations/Nomenclature**  
The following abbreviations are used in this manuscript:  
CWT: Continuous Wavelet transform  
FEA: Finite Elements Approach  
WFEM: Wavelet Finite Element Methods  
GA: Genetic algorithms  
FT: Fourier transform  
HT: Hilbert transform  
WT: Wavelet transform  
AE: Acoustic Emission  
STFT: Short Time Fourier transform  
CWT: Continuous Wavelet transform  
XWT: Cross Wavelet Transform  
ANN: Artificial Neural NetworkDWT: Discrete Wavelet Transform  
MRA: Multiresolution analysis  
WPT: Wavelet Packets transform  
SVM: Support Vector Machines  
NDE: Non Destructive Evaluation  
POD: Probability of detection  
\(t\): Time  
\(N\): Number of samples for a time domain signal  
\(c\): Scale parameter in CWT  
\(b\): Shift parameter in CWT  
\(s\): Scale range evaluated in CWT  
\(\psi\): Wavelet function  
\(w\): Weighting function  
\(x\): Time domain signal  
\(A\): Approximation information  
\(D\): Detail information  
\(k\): Decomposition level  
\(j\): Position of a packet within decomposition level  
\(W(k,j)\): WPT coefficients  
\(f\): Frequency  
\(f_{\psi}\): Central frequency of the wavelet function  
\(f_{s}\): Sampling frequency  
\(f_{r}\): Frequency resolution of a packet in WPT  
\(a\): Crack size  
\(a_{NDE}\): Crack size that reaches certain POD at a given confidence level
## 2 Materials and Methods  
### Description of the Received Data (SatNOGS and GlobalStar)  
For the intersatellite transmission system, a 32-bit Cortex M3 ARM microcontroller was utilized to control the STINGR simplex transmission device, which was equipped with an STX3 modem. This configuration allowed the transmission of packets to a network of LEO satellites using the GlobalStar network. The received data is then forwarded to a web interface where the user interprets the data for further processing.  
The device sends a signal to one or more nearby satellites without knowing exactly which satellite will receive the signal. Subsequently, the GlobalStar satellites download the information to the nearest Gateway. Redundant messages are discarded. Each message consists of 9-byte data packets, meaning that the system can transmit only 9 bytes in a single message. Therefore, if the message is larger, multiple packets need to be sent. There is a possibility of message loss when no satellite is within range. As the system operates in simplex mode, a redundancy method is employed to increase the probability of successful message reception.  
The information received by the satellite and transmitted to the gateways was presented in a raw log report generated in Excel format. The received packets were placed in different cells using a standardized JSON format. Depending on the configuration 2 or more packets make up a message. A valid message has a length of 157 bytes starting with a marker \"<HK>\" and ending with \"</HK>\".
# Enhanced Privacy-Preserving Architecture for Fundus Disease Diagnosis with Federated Learning
## 2 Related Work
In summary, significant research progress and work have been carried out to improve the drawbacks found in FL, to apply FL for the healthcare space, and to leverage its benefit for private model collaboration. The work found in this study aims to build upon these foundations by developing a more accurate and more resource-efficient FL architecture tailored specifically for private, collaborative training in the eye healthcare space. Table 1 below shows a comparison between previous works and demonstrates the originality of our approach. As illustrated in the table, various studies have explored different facets of medical image analysis, disease classification, and FL. However, none has simultaneously tackled the challenge of preserving data privacy while maintaining high model accuracy in an FL framework for fundus disease diagnosis. Our approach is particularly innovative in leveraging a dynamically weighted federated averaging (wFedAvg) method alongside k-client selection training to minimize accuracy loss, while ensuring data security. Lastly, by integrating TL with VGG-19 and optimizing client selection, our study offers a scalable and computationally efficient solution that balances privacy, accuracy, and training efficiency to allow for collaborative ML model training within the eye healthcare space.
# A Cross-Chain Solution to Connect Multiple DNS Blockchains in Consensus Roots System
## 2 Related Work
### Cross-Chain  
Cross-chain technology enables interactions between different blockchain systems, solving interoperability challenges and creating opportunities for development [17]. It has applications in sectors like finance, IoT, and supply chain management. In finance, it enhances asset liquidity and innovation, while, in IoT, it improves system efficiency and security. In supply chain management, it boosts logistics and traceability. This technology allows for smooth digital asset transfer between blockchains, increasing convenience and efficiency. Cross-chain establishes trusted communication and information sharing, solving \"data silos\". Notable methods include notary mechanisms, sidechains, and hash time locks. The traditional notary mechanism uses a trusted third party between blockchains, like Ripple's Interledger Protocol [18], but it also raises centralization concerns. Sidechains increase Bitcoin's throughput, supporting bidirectional pegged sidechains. They often use Simplified Payment Verification (SPV) for transaction confirmation, reducing data transfer burdens [19]. Relay chains combine sidechain and notary mechanisms. Cosmos, for example, uses Tendermint and IBC for cross-chain transactions. It connects various blockchains for trusted data transfer. Hash Time Locked Contracts (HTLCs) enable asset swaps between blockchains using hash locks and time locks [20]. They secure transactions and ensure timely completion. In short, cross-chain technology solves blockchain interoperability challenges, offering various solutions through notary mechanisms, sidechains, relay mechanisms, and HTLC. It can revolutionize finance, IoT, and supply chain management by improving efficiency, security, and interoperability. Recent works have proposed a cross-and-off-blockchain micropayment channel, a trust-model-based consensus committee method, and a modular token transfer framework [21; 22; 23], collectively focusing on reducing on-chain transactions, strengthening security coordination, and improving the interoperability and latency in cross-chain blockchain networks.
# Automatic Wireless Signal Classification: A Neural-Induced Support Vector Machine-Based Approach
## 2 Signal Model and Problem Statement
### Convolutional Neural Networks
#### 3.1.3 Pooling Layer  
The pooling layer is also called the down-sampling layer as it sums up information and maps the dominant response within the specific local region of the respective field. Max pooling is used in this study, which can be represented as:  
\[h_{k}^{m+1}(i)=\max\Bigl{(}h_{m}^{k}\Bigl{(}n^{m+1}(i-1)+1\Bigr{)},h_{m}^{k} \Bigl{(}n^{m+1}(i-1)+2\Bigr{)}\ldots h_{m}^{k}\Bigl{(}n^{m+1}(i-1)+N\Bigr{)} \Bigr{)} \tag{9}\]  
where, \(N\) is the length of pooling windows and \(n^{m+1}\) is called stride and represents the margin between two pooling windows.
# Convolutional-Neural-Network-Based Hexagonal Quantum Error Correction Decoder
###### Abstract  
Topological quantum error-correcting codes are an important tool for realizing fault-tolerant quantum computers. Heavy hexagonal coding is a new class of quantum error-correcting coding that assigns physical and auxiliary qubits to the vertices and edges of a low-degree graph. The layout of heavy hexagonal codes is particularly suitable for superconducting qubit architectures to reduce frequency conflicts and crosstalk. Although various topological code decoders have been proposed, constructing the optimal decoder remains challenging. Machine learning is an effective decoding scheme for topological codes, and in this paper, we propose a machine learning heavy hexagonal decoder based on a convolutional neural network (CNN) to obtain the decoding threshold. We test our method on heavy hexagonal codes with code distance of three, five, and seven, and increase it to five, seven, and nine by optimizing the RestNet network architecture. Our results show that the decoder thresholding accuracies are about 0.57% and 0.65%, respectively, which are about 25% higher than the conventional decoding scheme under the depolarizing noise model. The proposed decoding architecture is also applicable to other topological code families.  
quantum error correction; heavy hexagonal code; convolutional neural network decoder +
Footnote †: journal: Applied
## 4 Results and Discussion
### The Compound Coaxial Helicopter in Forward  
The propeller speed of compound coaxial helicopter in forward flight is 6500 RPM. The forward flight speed of compound coaxial helicopter is 150 km/h. The rotor and wing provide the half lift, respectively, and the thrust is mainly provided by the propeller in forward flight. The downwash flow of the rotor will affect the lift of the wing and flat tail. The lifts of the wing and flat tail are different in the two states. Figure 27 shows the velocity contour of a compound coaxial helicopter in forward flight.  
The rotor overlapping azimuth has a significant effect on the aerodynamic performance of compound coaxial helicopter in forward flight. The rotors produce aerodynamic interference to the wing. The lift of the wing in forward flight is shown in Figure 28.  
As can be seen from Figure 28, compared with state 1, the maximum value of the wing lift coefficients is 2.1% smaller, the minimum value is 1.4% larger, and the amplitude is 61.2% smaller in state 2. After SMA processing, the wing lift coefficients of the two states are identical.  
Figures 29-31 show the upper rotor thrust coefficients, lower rotor thrust coefficients, and total rotor thrust coefficients of the compound coaxial helicopter in forward flight.  
Figure 26: The compound coaxial helicopter rotor \(C_{T}\) in hover.  
Figure 28: The wing \(C_{T}\) in forward flight.  
Figure 27: Velocity contour of a compound coaxial helicopter in forward flight.  
Figure 29: The compound coaxial helicopter upper rotor \(C_{T}\) in forward flight.  
It can be seen from Figure 29, the thrust coefficients of upper rotors are different at the azimuth of 0 \({}^{\circ}\), 90 \({}^{\circ}\), 180 \({}^{\circ}\) and 270 \({}^{\circ}\) in the two states, but the phase angle of the peak is the same in the two states. It can be seen from Figure 30, the thrust coefficients of lower rotors are different at azimuth 0 \({}^{\circ}\), 90 \({}^{\circ}\), 180 \({}^{\circ}\) and 270 \({}^{\circ}\) in two states. However, the peak's phase difference of the lower rotor's thrust coefficients is 90\({}^{\circ}\) in the two states.  
It can be seen from Figure 31 that in the forward flight of the compound coaxial helicopter, compared with that of state 1, the maximum value of the total thrust coefficients is 28.7% smaller, the minimum value of the total thrust coefficients is 92.8% larger, and the amplitude of total thrust coefficients is 83.5% smaller in state 2. After SMA processing, the total thrust coefficients of state 2 are 0.87% larger than that of state 1. It can be seen that the aerodynamic performance of the compound coaxial helicopter is more stable when the compound coaxial helicopter works in state 2 in forward flight.  
Figure 31: The compound coaxial helicopter rotor \(C_{T}\) in forward flight.  
Figure 30: The compound coaxial helicopter lower rotor \(C_{T}\) in forward flight.
# Impact Testing of 3D Re-Entrant Honeycomb Polyamide Structure Using Split Hopkinson Pressure Bar
## 4 Results and Discussion
### Waveform Analysis
The transmission bar receives the transmitted compression wave. The transmitted wave reaches the strain gauges X\({}_{4}\), X\({}_{5}\) and X\({}_{6}\) in turn. As these three strain gauges are spaced 20 cm apart, it was shown in Figure 4 that the transmitted wave spacing was about twice that of the incident wave or the reflected wave. In order to more clearly express the transmitted wave, the transmitted waveforms are taken out for analysis separately, as shown in Figure 5. It can be seen the three transmitted waveforms all have the super-long pulse width which is much longer than the loading pulse width, that is, the so-called extended pulse width. The extended part is shown in the rectangle box D in Figure 5, with a total pulse width of about 1.4 ms. The reasons for the occurrence of the super-long pulse width are as follows: on the one hand, the compression delay phenomenon appears in the rising process of the transmitted pulse, because after the incident wave is transmitted to the specimen interface, it passes through the specimen with relatively small impedance, and its propagation velocity slows down, leading to the increase in the pulse width of transmitted wave when it is transmitted to the transmission bar. On the other hand, when the specimen conducts the transmitted wave, it releases the absorbed elastic potential energy to the transmission bar, which causes the secondary compression of the transmission bar, resulting in the extension of the pulse width and compression \"creep\". After that, the transmitted wave reaches the end of the transmission bar and the sparse wave is reflected back, which passes through the strain gauges X\({}_{6}\), X\({}_{5}\) and X\({}_{4}\) in turn. As shown from the partial enlarged subgraph, namely rectangle box E, when the reflected sparse wave reaches the position of strain gauges for the first time, the compression \"creep\" caused by the transmitted wave is not completed, resulting in the superposition of the head of the sparse wave and the tail of the transmitted wave. It also can be seen that the transmitted wave does not immediately unload to 0, but directly changes from the compression wave to the stretch wave. From the time period between the arrival of transmitted wave and the arrival of the sparse wave, it can be found that the pulse width at X\({}_{4}\) is the largest and the pulse width at X\({}_{6}\) is the smallest.
# Research on Shear Behavior of Sand-Structure Interface Based on Monotonic and Cyclic Tests
## 1 Introduction
In the first part, a series of monotonic shear tests were carried out with HJ-1 ring shear apparatus (Nanjing, China). Through this series of tests, the factors affecting the shear stiffness were discussed, and the dilatancy and shrinkage of the sand were found. According to the empirical formula [29] of initial shear modulus proposed by the predecessors and fitting the test results, the formula for calculating the initial shear stiffness of the interface between sand and structure was proposed. Then, in the second part, the cyclic shear tests were conducted on the basis of considering the influence of the initial shear stress. This work showed the shape of the loading and unloading curves, as well as the development law of normal deformation. This study clarified the relationship between loading stiffness and unloading stiffness, as well as the changing law of loading stiffness with cycle. The behavior of cyclic accumulated residual deformation was analyzed. In addition, comparative tests of cyclic shear on the sand-steel interface under different stress level amplitudes were carried out. This work discussed the variation law of loading and  
Figure 1: Schematic diagram of loading and unloading modulus. unloading stiffness and its residual deformation under different stress level amplitudes. This research is helpful to improve our understanding of sand-structure contact in practice.
# State of Health Prediction for Lithium-Ion Batteries Using Transformer-LSTM Fusion Model
## 5 Conclusions  
This paper proposes a method for predicting battery SOH based on multidimensional features and a transformer-LSTM fusion model, aiming to enhance the accuracy and generalizability of SOH prediction. The key findings reveal that by extracting features from the time domain, frequency domain, and time dimension of the voltage, energy and temperature curves, the trend of battery degradation can be effectively captured. Furthermore, denoising the feature data significantly improves prediction accuracy and reduces the negative impact of noise on model performance. By integrating these optimized features with the transformer-LSTM fusion model for prediction, the experimental results demonstrate that the proposed model excels in both prediction accuracy and generalization ability, thus verifying its efficacy and practicality in SOH prediction.  
Looking ahead, this study will focus further on the implementation and optimization of SOH prediction in actual application scenarios. Compared to laboratory scenarios, the operating conditions of actual batteries are considerably more complex and variable. They are influenced by unstable external factors such as fluctuations in ambient temperature, variations in user operating habits, and the use of diverse charging equipment. These factors pose challenges to the accuracy of SOH prediction. Therefore, to ensure effective implementation of SOH prediction in real-world applications, it is crucial to adapt and optimize the model based on actual operating data of the batteries to accommodate changes in real-life scenarios.  
Figure 13: (**a**) Prediction results for battery with batch number 44 on “2017-05-12”, (**b**) prediction results for battery with batch number 48 on “2018-04-12”.  
**Author Contributions:** Conceptualization, X.C. and T.L.; methodology, X.C.; software, X.C.; validation, X.C. and T.L.; formal analysis, X.C. and T.L.; investigation, X.C. and T.L.; data curation, X.C.; writing--original draft preparation, X.C.; writing--review and editing, X.C. and T.L.; visualization, X.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by Xiamen University.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are openly available at [[https://doi.org/10.1038/s41560-019-0356-8](https://doi.org/10.1038/s41560-019-0356-8)]([https://doi.org/10.1038/s41560-019-0356-8](https://doi.org/10.1038/s41560-019-0356-8)). This data can be found here: [[https://data.matri.io/1/projects/5c48](https://data.matri.io/1/projects/5c48) dd2]([https://data.matri.io/1/projects/5c48](https://data.matri.io/1/projects/5c48) dd2) bc625d700019f3204 (accessed on 1 March 2025).  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Cybersecurity for Blockchain-Based IoT Systems: A Review
## 5 Discussion
### Blockchain for IoT: Potential Applications for Smart Cities
#### 5.2.1 Data Integrity and Security  
One of the primary concerns in IoT systems is the integrity and security of the data collected and transmitted by connected devices. Blockchain can provide a tamper-resistant and auditable ledger for recording and verifying data transactions [45]. By storing data in a decentralized manner across multiple nodes, blockchain ensures that data cannot be easily altered or manipulated. This feature is particularly crucial in smart cities where large volumes of data are generated from various sensors and devices [46]. Blockchain-based data integrity can help prevent data tampering, ensure the authenticity of information, and maintain data privacy.