esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Response Analysis of Asphalt Pavement under Pavement-Unevenness Excitation
## 4 Numerical Results
### Influence of Pavement-Unevenness Grade  
Pavement unevenness is the cause of mechanical load, and it is divided into several grades according to the degree of pavement unevenness. The mechanical response results of the bottom layer of different pavement grades are shown in Figure 10.  
Figure 10 reflects the variation and distribution of \(\sigma_{zz}\), \(\sigma_{x}\), \(\varepsilon_{xx}\), and \(\varepsilon_{zz}\) at the bottom of the uneven pavement of grades A, B, C, and D. As can be seen from the figure, the peaks of \(\sigma_{zz}\), \(\sigma_{xx}\), \(\varepsilon_{xx}\), and \(\varepsilon_{zz}\) at the bottom of the top layer of grade A pavement are 0.0381 MPa, 0.0115 MPa, 0.0098, and 0.0225, respectively, which increase by 20.3% compared with the level pavement surface. At the same time, these peaks increase sharply as the pavement-unevenness grade decreases, and when the road grade decreases from grade A to grade D, the \(\varepsilon_{xx}\) peaks increase by 12.3%, 51.5%, and 103.7%, respectively. \(\sigma_{zz}\), \(\sigma_{xx}\), \(\varepsilon_{xx}\), and \(\varepsilon_{zz}\) distribution in the range of 25-75% and 1-99% will increase with the decline of the unevenness grade of the road surface, thus increasing its dispersion, and the probability of high stress and high strain at the bottom of the surface layer will increase. In pavement design, the horizontal strain at the bottom of the asphalt layer is usually used as the design index of pavement fatigue life, and the increase of \(\sigma_{zz}\), \(\sigma_{xx}\), \(\varepsilon_{xx}\), and \(\varepsilon_{zz}\) will undoubtedly accelerate the fatigue damage of pavement.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Roughness Grade** & **Lower Limit** &
\begin{tabular}{c} \(G_{q}\) (\(n_{0}\)) (10\({}^{-6}\)m\({}^{3}\)) \\ **Average Value** \\ \end{tabular} & **Upper Limit** \\ \hline A & 8 & 16 & 36 \\ B & 32 & 64 & 128 \\ C & 128 & 256 & 512 \\ D & 512 & 1024 & 2048 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The pavement roughness coefficient \(G_{q}\) (\(n_{0}\)) for different roughness grades.  
Figure 9: Mechanical response at the bottom of the surface layer at different wheels of the vehicle model. (**a**) The bottom of the surface layer \(\varepsilon_{zz}\); (**b**) the bottom of the surface layer \(\varepsilon_{xx}\).
## 3 Results and Discussion
### Effects of Fibers and Fly Asl Content on the Compressive Strength of Concrete  
Figure 5 illustrates the compressive strength development of different concrete mixes determined at 3, 7, 28, and 56 days of curing. Figure 5 clearly distinguishes the effects of 0.3% volume fraction of CCS and PVA fibers on the compressive strength of 100% cement concrete and concrete blended with 20% fly ash content. The compressive strength of the control mix (CM), containing 100% cement, was considered as the reference value.  
Figure 4: Superplasticizer dosage for achieving 90–110 mm slump in different concrete mixes. (Note: C is cement, FA is fly ash).  
Therefore, the compressive strength of all other mixes was assessed with the reference value. For the control mix (CM), the reference value at every age of curing is taken as one; all other values of compressive strength were divided by the mix's compressive strength, CM, and the strength efficiency index (SI). The same concept was discussed by [PERSON] et al. [24] for comparing the efficiency of different concrete mixes. Table 3 shows the SI value determined for various concrete mixtures in this study. The effects of the type of fibers and the fly ash content were considered the research variables. Some research studies showed that increasing the coarse aggregates' size up to a specific limit also increased the compressive strength [35]. [PERSON] et al. [24] used 12 mm and 18 mm long PVA fibers, and they discussed that the long fibers were able to connect and tie together the coarse aggregates in concrete. Results showed that the addition of 20% fly ash caused a slight delay in compressive strength development. The concrete mixes containing fly ash suffered a loss of strength between 15% and 35% at 3 days and 7 days compared to the reference value. A 0.3% volume fraction of CSS fibers caused positive effects on the compressive strength compared to the control mix with fly ash (CF), which had a 6% higher strength at 3 days and was 9% higher at 7 days. At 56 days, fly ash concrete containing CSS fibers and 0.3 SF, showed an 18% higher strength than the reference mix CM and 16% higher than the CF mix.  
Compared to the CCS fibers, PVA fibers in 100% cement concrete showed the same value of compressive strength as in the control mix (CM) at 3 and 7 days. They showed a slight increase in strength at the 28 and 56 days. Whereas tha PVA fibers in fly ash concrete showed a reduction in compressive strength of 6% at 3 days compared to the control mix, (CF) and a 35% reduction in strength of the reference mix (CM). However, at 7 days and later, PVA fibers showed an increase in strength between 4% and 7% compared to the control mix (CF). In general, the addition of a small volume fraction of 0.3% of CCS and PVA fibers did not show adverse effects on strength at 28 and 56 days of curing.
## 6 Influence Analysis of Shield Tunneling Parameters  
### Influence of Heading Face Pressure on Ground Settlement  
In the numerical model of the small radius curve tunnel, in order to explore the influence of the heading face pressure on the surface settlement, the heading face pressure is taken as 180 kPa, 240 kPa, 300 kPa, 360 kPa, 420 kPa, 480 kPa, and 540 kPa for numerical simulation. The curve of surface settlement with the pressure of the heading face is shown in Figure 11.  
It can be seen from Figure 11 that when the heading face pressure of the shield machine is 180 kPa, the surface settlement value is 26.8 mm. As the heading face pressure of the shield machine gradually increases, the surface settlement value gradually decreases. Taking the surface settlement when the heading face pressure is 180 kPa as the benchmark, the surface settlement decreases by 18.52%, 33.33%, 44.43%, 53.70%, 59.26%, and 62.96% for every 60 kPa increase in the heading surface pressure.  
When the heading face pressure is 300-350 kPa, the surface settlement control effect is better. If the heading face pressure is further increased, on the one hand, it will have little effect on improving the surface settlement. On the other hand, the large heading face pressure means that the high energy consumption of the shield machine is not conducive to the cost control of the enterprise.  
Considering the background of the small radius curved tunnel of Changsha Metro Line 6, the pressure of the heading face is recommended to be 300-350 kPa.
## 3 Results and Discussion
### Effect of Mineralogy  
While a detailed investigation into the effect of mineralogy on high radon or thoron exhalation rates is of interest, it falls outside the main scope of this paper. However, to illustrate how mineral composition can influence radon/thoron potential, a high-resolution  
Figure 6: Combined doses calculation models of radon and thoron exhalation from Irish building materials under standard (0.5 h\({}^{-1}\)) and high ventilation (10 m\({}^{3}\)/h) scenarios. High ventilation reduces combined doses by 85-95%, demonstrating its critical role in mitigating risks.  
mineral scan of a Galway granite sample (shown here to have high radon and thoron potential) was evaluated. Prior to analysis, minerals of interest were manually introduced to the microscope. Some minerals commonly associated with radon and thoron emission (Figure 7) include those containing uranium and thorium, such as the following:  
* Uraninite (UO\({}_{2}\)): A primary uranium ore with high radioactivity (gamma radiation source) and radon potential (alpha radiation source).
* Thorianite (ThO\({}_{2}\)): A thorium-rich mineral with high radioactivity (gamma radiation source) that contributes to thoron emissions (alpha radiation source).
* Monazite (Ce,La,Nd,Th)PO\({}_{4}\): Contains significant thorium (gamma radiation source) and can be a source of thoron and, to a lesser extent, radon (alpha radiation sources).
* Zircon (ZrSiO\({}_{4}\)): Often contains trace uranium and, to a lesser extent thorium (gamma radiation sources), contributing to radon and thoron (alpha radiation sources).  
Figure 8 presents a composite of backscattered electron (BSE) images of the Galway granite sample and the corresponding mineral maps. BSE imaging reveals the mineralogical matrix and texture, while the elemental maps highlight the spatial distribution of uranium- and thorium-bearing minerals within the sample. The granite sample scan revealed a considerable number of rutile, zircon, monazite, and hematite minerals. These kinds of detailed imaging and mapping are beneficial for future research aimed at identifying the specific minerals contributing to elevated radon and thoron levels and the degree of their contribution. By analyzing these mineralogical features and the chemical composition of the samples, it is anticipated that a better understanding of how the distribution and concentration of uranium and thorium-bearing minerals influence overall radon and thoron  
Figure 7: Spectra of the most common uranium–thorium bearing minerals used for capturing the minerals which can produce high radon or thoron emanation rates.  
emissions can be achieved. This approach provides mineralogical insights, which enable a refined understanding of radiation risks associated with various building materials. The approach demonstrated here highlights a novel method for material testing and certification for radon and thoron potential studies, ultimately facilitating the development of more effective radiation protection resource management strategies for building materials.
# Status of the SXFEL Facility
## 2 The SXFEL Test Facility
### Main Accelerator  
The main accelerator of the SXFEL-TF is designed as a compact linac with high-gradient C-band RF accelerating structures. Initially, the main linac consists of 3 linac sections (L1 to L3) and two bunch compressors [25]. Later on, the two-stage bunch compression scheme was replaced by a single-stage bunch compressor with BC1 at a beam energy of approximately 200 MeV, based on the experience of FERMI. Currently, the main linac layout is shown in Figure 3, where L1 is the S-band accelerating section and L2 and L3 are C-band accelerating sections. To further boost the electron beam energy up to 1.5 GeV in the future, extra space between L2 and L3 has been reserved, in which a second bunch compressor and more C-band structures can be installed.  
\begin{table}
\begin{tabular}{c c} \hline Stage 2 & \\ \hline FEL output wavelength & 8.8 nm \\ Modulator undulator period & 40 nm \\ Modulator undulator K value & 2.22 \\ Radiator undulator period & 23.5 mm \\ Radiator undulator K value & 1.43 \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Electron energy & 130 MeV \\ Bunch charge & 0.5 nC \\ Projected emittance (rms) & 0.95 mm\(\cdot\)mrad \\ Central slice emittance (rms) & 0.65 mm\(\cdot\)mrad \\ Bunch length (FWHM) & \(\sim\)10 ps \\ Projected energy spread (rms) & 0.14\% \\ \hline \end{tabular}
\end{table}
Table 2: Main electron beam parameters of the SXFEL injector. (Reprint from reference [26]).  
Figure 2: Layout of the SXFEL injector. (Reprint from reference [26]).  
C-band accelerating structures have the advantage of compensating the energy spread at the phase around the crest due to the stronger longitudinal wakefield, and this, as a result, can reduce the beam energy jitter. In the meantime, simulation suggests that the transverse wakefield of the C-band structure will not obviously degrade the beam performance. Designed parameters for the main linac are shown in Table 3.  
In order to monitor the beam positions and optics, beam position monitors (BPMs) and beam profilers are used in the main linac. In the first bunch compressor (BC1), non-destructive bunch length and beam energy detectors are installed to give feedback on the beam energy, peak current, and arrival time with the Low-level RF system.  
The triplets and FODO lattices are arranged for the main linac. Figure 4 shows the Twiss functions calculated by the ELEGANT program [27]. To minimize the horizontal beta function at the last bending magnet and hence mitigate the CSR effect, two doublets are used on each arm of the chicane. After L2, there is some extra space reserved for accommodating C-band accelerating structures for upgrading the linac energy in the future.
# Healthchain: A Privacy Protection System for Medical Data Based on Blockchain
## 4 Cryptographic Primitives
### Asymmetric Encryption  
Asymmetric encryption uses two different keys for encryption and decryption. Compared with symmetric encryption, asymmetric encryption does not have the problem of secure key transmission, and has better security. However, the speed of encryption and decryption is reduced accordingly. Therefore, asymmetric encryption is usually used to encrypt the key generated by a symmetric encryption algorithm. The flow of an asymmetric encryption algorithm is shown in Figure 3. In the scheme proposed in this paper, we use an RSA algorithm [30] for the encryption of the symmetric encryption key. The security of the RSA algorithm is based on the factorization of large integers. It is the most widely used asymmetric encryption algorithm. It was jointly proposed in 1978 by three scholars of MIT: [PERSON], [PERSON], and [PERSON].  
Figure 3: Asymmetric encryption algorithm flow.  
Figure 2: Encryption process of the AES algorithm.  
The encryption and decryption process of an RSA algorithm includes the following steps:  
1. Randomly select two different prime numbers p and q. According to the computational power of the current computer, these two numbers should comprise at least 200 bits before they can be considered safe enough in practice.
2. Calculate the common modulus n: \[n=p^{*}q\] (1)
3. Calculate the Euler function for the common modulus n: \[\varphi(n)=(p-1)(q-1)\] (2)
4. Randomly select an integer e, which must meet two conditions: e and \(\varphi(n)\) are coprime, and \(1<e<\varphi(n)\). Take (e, n) as public key P.
5. Calculate: \[d=e^{-1}\bmod(p-1)(q-1)\] (3) and take (d, n) as the private key S and keep it private.
6. The encryption process from plaintext M to ciphertext C is: \[C=M^{e}\bmod n\] (4)
7. The decryption process is: \[M=C^{d}\bmod n\] (5)  
The algorithm flow is shown in Figure 4.
## 5 Experiments
### Experimental Setup
#### 5.1.2 Baselines  
In order to demonstrate the effectiveness of MultiDS, we select five competitive approaches as baselines to compare with the proposed method.  
* ProtoNet [4]: This algorithm is a strong baseline for meta-learning based methods. ProtoNet proposes to average samples of the same class to obtain class center. It classifies samples based on distances between each class center and samples in feature space;
* HATT [28]: This model improves ProtoNet by a two-step attention mechanism, which is composed by feature-level and instance-level attentions. Feature-level attention focuses on more informative features. Instance-level attention assigns different weights to instances according to their significance;
* DS-FSL [9]: This approach firstly introduces distributional signatures into few-shot learning. It proposes to utilize precomputed distributional signatures as word weights and constantly update them to fit different data;
* MLADA [29]: This method combines adversarial learning with few-shot learning to solve the cross-domain problem in few-shot learning. The core idea is to produce knowledge by a generator to enhance the domain adaptability;  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Dataset** & **Domain** & **Class** & **Example** & **Token/Example** \\ \hline Dataset for meta-training & Clinc150 & 10 & 150 & 22,500 & 150 (\(\pm 0\)) \\ \hline \multirow{3}{*}{Dataset for meta-testing} & Banking77 & 1 & 77 & 13,083 & 170 (\(\pm 31\)) \\  & Huffpost & unk & 41 & 36,900 & 900 (\(\pm 0\)) \\ \cline{1-1}  & Hwu64 & 21 & 64 & 11,036 & 172 (\(\pm 40\)) \\ \cline{1-1}  & Liu57 & unk & 54 & 25,478 & 472 (\(\pm 823\)) \\ \hline \end{tabular}
\end{table}
Table 3: Dataset statistics. unk denotes unknown.  
* DualAN [11]: This method is an improved version of MLADA. It enhances adversarial learning by introducing high-quality and stable data. These data come from two N-way-K-shot tasks from different domains.
# KAN-Transformer Model for UltraShort-Term Wind Power Prediction Based on EWMA Data Processing
## 3 Experimental Analysis
### Data and Simulation Settings  
This experiment uses data from the 200 MW wind farm, as described in Section 2. To predict the future wind power value based on 30 historical data points, a sliding window method is used to extract samples. The dataset is divided as follows: Training set: 24,498 samples, Validation set: 7008 samples, and Test set: 3504 samples. The model is built and trained using Python 3.9.7 programming language and the PyTorch-gpu 2.1.0 deep learning framework. The hyperparameters for the model are set as shown in Table 2.  
The test set contains a total of 3504 samples. To clearly present the prediction results, samples are selected at intervals for plotting. MAE, RMSE, and R\({}^{2}\) are used to evaluate and analyze the prediction results of the 3504 test samples. The experimental analysis consists of two parts. The first part is model optimization verification. First, the Transformer model is enhanced with the KAN module to create the KAN-Transformer model. This part validates whether the KAN layer improves the model's prediction accuracy by capturing nonlinear relationships. Next, the original data are processed using EWMA to create the EWMA-KAN-Transformer model. This part verifies whether applying EWMA denoising to the introduced multivariable features enhances the model's prediction accuracy. The second part is a comparison with common time series prediction models. The proposed model is compared with common time series prediction models in terms of prediction curves and test set evaluation metrics. This part assesses whether the proposed model shows advantages over other models in the field of ultrashort-term wind power prediction.
# Controllable Fano-like Resonance in Terahertz Planar Meta-Rotamers
## 4 Conclusions  
In conclusion, we have demonstrated meta-rotamers with Fano-like resonance. The meta-rotamers based only on the difference in the spatial position of their component meta-atoms, the C-shaped SRR and Y-shaped metal rod, can be interconverted by the rotation of the Y-shaped meta-atom. The resonant frequency and the Q-factor can be controlled by asymmetric characteristics of Fano-like resonance. This is because of the cooperative effects determined by the spatial coupling conditions of nodes and antinodes of the second-order LC resonance by the C-shaped SRR meta-atoms and the dipole resonance by the Y-shaped meta-atoms. The higher Q-factor observed in the antinode-antinode combination is due to the strong electric near-field coupling between two resonant modes located spectrally very close together. In addition, the red-shift of the LC resonance may be caused by the  
Figure 4: (**a**) Resonant frequencies of the second-order LC resonance obtained at three samples of the C-shaped meta-atoms and two states of meta-rotamers. (**b**) Frequency shift of the second-order LC resonance (\(\Delta f_{1}\)) and dipole resonance (\(\Delta f_{2}\)) as a function of angular gap \(\theta_{c}\). (**c**) Q-factor (red squares and circles, left scale) and resonance depth (blue squares and circles, right scale) for the two resonances, \(f_{1}\) (blue and red squares) and \(f_{2}\) (red and blue circles), respectively.  
increase of effective inductance value in the condition of the antinode-antinode coupling. We believe that the findings provide possibilities for exploring metamaterial-based THz devices, such as modulators, sensors, antennas, and switches, requiring high sensitivity and multifunctions of spectral tunability and selectivity.  
Conceptualization and methodology, J.-W.L.; writing--original draft preparation, S.J.; writing--review and editing, J.-W.L. and M.-G.B.; data acquisition and analysis, S.J. and M.-G.B.; supervision, project administration, and funding acquisition, J.-W.L. All authors have read and agreed to the published version of the manuscript.  
National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (Grant Number: NRF- 2019R1 FIA1058851).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.
# A Systematic Survey on 5G and 6G Security Considerations, Challenges, Trends, and Research Areas
## 5 Proposed Future Security Mechanisms
### Artificial Intelligence for 6G
#### 5.3.3 Risks of AI in 6G Security  
While AI can improve security through more agility and responsiveness, rather than by incorporating frequent updates and changes to monitoring capabilities in traditional security methods, it is expected that it can also be leveraged for malicious attacks against 6G. These attacks are often called adversarial model attacks [144] and often constitute injecting false data to change a security model's behavior or evading detection from models all together. Adversarial model attacks, like the attack outlined in [144], attempt to avoid known security models, such as intrusion detection systems. These attacks are also referred to as evasion attacks [145], which augment normal traffic on a network to gain information without being detected or deteriorate network performance as in a poisoning attack [146].  
There are also inherent risks to implementing AI models that are not fully explainable. When models are implemented that are treated as \"black boxes\", there are considerable risks to deployment, since model performance cannot be anticipated for all input parameters. This has led to an increase in works for Explainable AI [147], ensuring that models are fully understood before deployment. This is expected to be a crucial component to the use of AI/ML models in 6G system operations, particular for cyber forensics aspects, troubleshooting and more. As can be seen, while AI and ML are expected to be pivotal to 6G operations, they are also expected to be a pivotal consideration as a source and method of cyber attacks and thus provide tremendous potential for additional research in order to ensure their reliability, trustworthiness, and effectiveness in future cellular network operations.