esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Trust Concepts
### Objectives  
The objectives of trust evaluation in 6G networks are diverse and pivotal for ensuring the network's success and widespread acceptance. The primary objective of reliability assessment is to ensure uninterrupted connectivity, efficient management of large data traffic volumes, and continuous service availability, even in challenging scenarios [17]. Achieving this objective is critical for identifying vulnerabilities and establishing resilient network structures to enhance reliability [22].  
Additionally, the security objective revolves around evaluating the effectiveness of security protocols such as encryption, authentication, and intrusion detection in mitigating cyber threats and unauthorized access [22]. Through security evaluation, stakeholders can identify and address vulnerabilities, implement robust security measures, and safeguard confidential information transmitted across the network [23].  
Privacy objectives aim to assess privacy policies, data management procedures, and user consent mechanisms to protect user privacy and ensure compliance with regulations [24]. By evaluating trust in privacy protocols, stakeholders can effectively manage personal data securely and transparently, bolstering trust in the network's privacy measures.  
Furthermore, ensuring ethical technology usage aims to evaluate the fairness, transparency, and accountability of AI algorithms and data analytics to mitigate biases and unethical conduct [25]. Trust evaluation fosters ethical technology deployment within the network, promoting responsible use of technologies.  
Moreover, evaluating the trustworthiness of stakeholders involves assessing the adherence of network operators, service providers, and device manufacturers to established security guidelines and regulatory compliance [26]. This evaluation cultivates confidence and trust in the broader 6G ecosystem.  
The overarching goal is to foster user confidence and promote adoption to establish a trustworthy network environment that meets user expectations [12]. Trust evaluation, encompassing reliability, security, privacy, ethical considerations, and stakeholder trustworthiness, plays a pivotal role in instilling user confidence, driving adoption, and unlocking the full potential of 6G networks [27].  
In summary, the objectives of trust assessment in 6G networks include ensuring reliability, mitigating threats, preserving privacy, addressing ethical concerns, evaluating stakeholder trustworthiness, and instilling user confidence. Achieving these objectives is essential for developing a trustworthy network architecture that fosters user trust, encourages adoption, and facilitates successful implementation across diverse domains.
# Research and Development of a Large-Scale Axial-Flux Generator for Hydrokinetic Power System
## 3 Results
### Feasibility Study  
#### 3.5.1 Axial Generator Efficiency  
Axial-flux permanent magnet (AFPM) generators have many unique features. As permanent magnets, they are usually more efficient, as field excitation losses are eliminated, reducing rotor losses significantly. Their generator efficiency is thus greatly improved, and a higher power density is achieved. Axial-flux construction has less core material, so it  
Figure 8: Comparison between preliminary and improved design by power loss and efficiency variation over mechanical torque changes.  
has a high torque-to-weight ratio. Also, AFPM machines have thin magnets, so they are smaller than their radial-flux counterparts. The noise and vibration they produce are less than those of conventional machines.  
Their air gaps are planar and easily adjustable. Also, the direction of the main air gap can be varied, so the derivation of various discrete topologies is possible. These benefits give AFPM generators advantages over conventional machines in various applications.  
Efficiency is the major point of generator choice and design. It reflects not only in thermal behavior (power losses are heat-dissipated) but also in the overall price performance of a product. Generally, all available studies show \(\approx\)97% efficiency for this type of generator.  
Efficiency also depends on the applied load (mechanical power input), as, with the increase in the current density in the windings, the power output increases, though slower than the increase in the losses. This shows the need for an examination of designs at various loads' mechanical power input.  
Initially, the preliminary defined design was examined in detail, and after several iterations, an improved design is proposed. The major reasons for obtaining improved efficiency are listed below in Table 1.
## 4 Discussion  
The paper shows the feasibility of replacing expert subjective expertise with knowledge obtained from data. Firstly, the weight information of capabilities is determined by analyzing correlations between capabilities and the intercepted missile numbers, based on operation simulation data. Then, as regards the risk criterion, the paper tries to determine the risk by mining information from system operation data. The data-driven methods are only components of the model-based approaches, aiming to increase the accuracy and credibility of results. In the case study, 100 system candidates are provided to be optimized on the scenario of anti-missile. By automatically simulating the operation scenario for 10,000 times, 10,000 simulation results are generated, according to which, the maximal information coefficients between four capabilities and the variable of intercepted missile quantity are calculated as the weight of capabilities. It quantitatively indicates that the capability of decision time has the biggest impact on the interpreted missile quantity. In addition, by running the simulator of C-MAPSS for 200 times, 200 groups of system operation data are generated, according to which, systems risks are obtained through prediction of RUL. In the system portfolio optimization, considering the great number of \(2^{100}-1\) candidate system portfolios, the NSDE algorithm is applied to solving the optimization problem. To guarantee the optimality of the result as far as possible, 10 Pareto sets are obtained by running the NSDE for 10 times. 200 non-dominated individuals are reserved by comparing the 10 Pareto sets. However, it can be not proved the best Pareto set, due to the almost infinity of candidate system portfolios and the randomness of genetic algorithms. By further analyzing the characters of generated Pareto set, 22 systems are selected at least one time, and 16 association rules are mined. These characters can play an assistant rule for decision-makers to make a deeper understanding of the system portfolios. In conclusion, the system portfolio selection is the mainstream trend of future equipment development. Compared to other traditional system portfolio decision and optimization methods, the proposed model and data-driven approach provide a solution to avoid the excessive dependence on subjective expert experience in the evaluation and decision process. Traditionally, determining these parameters requires cumbersome processes of organizing experts, collecting expert opinions, analyzing expert scores etc., which are time and effort-consuming and more likely to be questioned. The model and data-driven methods can make use of models that have been proved to be effective on one hand, and on the other hand, it can determine the required parameter values in the model through data analysis. Therefore, it supports more efficient, more credible, and more practical evaluation and decisions in system portfolio selection and other fields applications.
# Forecasting Ionospheric foF2 Based on Deep Learning Method
## 3 Results and Discussion
### The Forecasting Performance
A possible explanation is that the variation of foF2 in low latitude areas is larger than that in high latitude, which increases the difficulty for prediction and produces larger forecasting errors. The interesting conjecture is also similar to that was found in previous work [48], but during the geomagnetic storm days. For the same ionosonde station, the RMSE in the year of solar maximum is usually greater than that in the year of solar minimum. While the PD in the year of solar maximum is often lower than that in the year of solar minimum. A possible reason is that the electron density of the ionosphere changes more dramatically due to solar activity, and the foF2 during the year of solar maximum is higher than that during the year of solar minimum, resulting in a larger RMSE and absolute error during the high solar activity period. Similar speculation can be found in previous works [17; 20]. According to Formula (4), PD is the  
Figure 5: Bar graph illustrations of RMSE differences and PD differences between observed and forecasting foF2 values by 4 models: (**a**) During the year of solar maximum (2015); (**b**) During the year of solar minimum (2019). ratio of absolute error to the observed value, therefore, the PD is lower during high solar activity year than that during low solar activity year.
## 2 Literature Work
### Attribute-Revocation Attribute-Based Encryption (AR-ABE)  
The ABE approaches focused on implementing abundant access strategies and neglected the attribute revocation in the encryption mechanism [32]. For example, the authors of [33] introduced the re-encryption method into attribute revocation. The proposed scheme deals with data outsourcing and enforces authorization policies and policy updates. The authors of [34] suggested the notion of access-control policies and end-user revocation capabilities. The system produces the group keys to the revocation list in this case. The authors of [35] provided the MR-ABE by utilizing the attribute-group key under certain error assumptions to solve the attribute revocation and granting problems. However, due to the high computational overhead, the system is inappropriate for resource-constraint devices. The authors of [14] proposed collision avoidance CP-ABE and attribute revocation for cloud storage. Finally, the authors of [31] considered security requirements for an AR-ABE to prove confidentiality and integrity.  
An outsourcing architecture in the above may lead to attribute and end-user revocation problems. In addition, the proposed solutions mentioned above are unsuitable for fog-computing and resource-constraint devices due to their high computational complexity. Moreover, the techniques lack information about data outsourcing for the resource-constraint devices and the multiauthority approach.
## 1 Introduction
### Integral Use of InSAR, GPR, and ERT Methods
Common themes across the studies include the following: Use of InSAR for Surface Deformation Monitoring--in all cases, InSAR techniques provided crucial insights into surface movements, demonstrating their utility in tracking subsidence, structural shifts, and landslide activity. Application of GPR and ERT for Subsurface Characterization--both methods proved effective in detecting voids, sediment variations, and groundwater-related features, contributing to subsidence and instability. Integration of Multiple Methods for Improved Accuracy--each study highlighted the limitations of relying on a single technique and emphasized the necessity of integrating geodetic, geophysical, and hydrogeological approaches to enhance data reliability and interpretation. The findings across the studies have significant implications for risk assessment, land-use planning, and civil safety, particularly in regions with karst, subsidence, or landslide-prone conditions. Overall, these studies collectively reinforce the importance of a multi-method approach to geohazard assessment, showcasing the effectiveness of integrating InSAR, GPR, and ERT with other geophysical, geodetic, and hydrogeological techniques to improve the detection, characterization, and mitigation of subsidence, sinkholes, and landslide risks.
## 3 Experimental Results
### True Positive Rate  
The True Positive Rate (TPR) is a measure used in statistical analysis, particularly in binary classification. TPR measures the proportion of actual positive cases that are correctly identified by a model. A high TPR indicates how well the model is able to detect positive cases. TPR is defined as  
\[\text{TPR}=\ \frac{TP}{TP+FN} \tag{16}\]  
where \(TP\) is true positive and \(FN\) is false negative. \(TP\) means the cases where the model correctly predicts a positive outcome, and \(FN\) denotes the cases where the model fails to predict a positive outcome that actually exists. As shown in Figure 10, two types of collage attacks are conducted to test the performance of tamper detection. The first attack tampers an image by copying image blocks from a plaintext image and inserting them to arbitrary positions in the encrypted stego image. The second attack forges an image by copying  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **[PERSON] et al. [12]** & **[PERSON] et al. [13]** & **[PERSON] et al. [14]** & **[PERSON] et al. [15]** & **[PERSON] et al. [16]** & **Proposed Method** \\ \hline BPP & 1.0349 & 1.1880 & 2.0129 & 1.4883 & 1.4553 & 2.1111 \\ \hline PSNR & 35.8347 & 34.1092 & 33.6419 & 30.7286 & 39.2284 & 35.2410 \\ \hline BPP Difference & 0 & 0.1531 & 0.9780 & 0.4534 & 0.4204 & 1.0762 \\ \hline PSNR Difference & 0 & \(-\)1.7255 & \(-\)2.1928 & \(-\)5.1061 & 3.3937 & 0.5937 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison among five existing schemes and our proposed scheme.  
encrypted blocks from an encrypted image and inserting them into arbitrary positions in the encrypted stego image.  
Figure 11 shows the tamper results of \"Pepper\". Some blocks are replaced by the blocks copied from the other plaintext image and an encrypted image. The tamper detection results delivered by the proposed scheme are listed in Table 2. We use the 3-bit authentication data to detect any changes or alterations made to the encrypted stego image. Obviously, there is a 1/8 probability of a collision occurring, leading to incorrect detection results. Therefore, we propose a hierarchical tamper detection strategy to further refine the tamper detection results. Table 3 lists the hierarchical tamper detection results for two types of attacks. We can see that the hierarchical strategy offers a higher TPR,
# A Metal Detector for Clip Location Tracking of Stomach and Colon Cancer during Laparoscopic Surgery
## 2 Physical Analysis and Design Planning
### Magnetic Coupling Analysis of Clip-Detection for Tumor Excision
The diameter (\(t_{w}\)), length (\(l_{coil}\)), and turn number (\(N\)) are 0.12 mm, 18.6 mm, and 22 turns, and the space (\(d_{h}\)) between the coils and air capacitance (\(C_{h}\)) is 0.206 mm and 68 pF, respectively. Moreover, the inductance of coil is 4.8 mH. A titanium clip (Olympus, Tokyo, Japan) is used, which features a diameter (\(D_{c}\)) and height (\(h_{c}\)) of 2.5 mm and 11 mm, respectively (Figure 5). Then, the magnetic flux density is 2.2 \(\mu\)T.
# Directional Suppression of Monotone Noises with A Parametric Array Loudspeaker
## 4 Experimental Characterization of PAL's Pressure Field
### Characterization of PAL's Axial Pressure  
Apart from the directivity characterization, we further examine and compare the axial pressure distribution of the PAL against traditional VCL. Due to the non-linear nature during the propagation of parametric acoustic beams, the self-demodulated audible frequency beam can be considered as an end-fire array created along the directivity of the primary beam defined by the carrier frequency wave [25]. Hence, this confinement from the carrier wave may mitigate the attenuation of the resultant audible signal. To evaluate this, we characterize both the PAL and VCL by measuring the attenuated sound levels in dB using the sound level meter along the axial direction. The measurement and comparison results are shown in Figure 4. For both the PAL and VCL, the audible signal of interest is 1 kHz with the initial sound pressure at the source surface calibrated as 90 dB. Then, the SPL is recorded at every 20 cm step away from the source in a range of 0\(\sim\)300 cm. Themeasurements indicate that the PAL attenuates less and gives a 20 dB higher SPL value than the VCL at various distances on average. This result further verifies PAL's advantage as the secondary source for ANC to provide the possibility of long-distance operation.  
Figure 4: Comparison of measured axial sound attenuation characteristics of the PAL against VCL when projecting 1 kHz audible signal with 90 dB initial SPL at the source surface.  
Figure 3: Experimental examination of the PAL’s directivity in the far field of 1 m away from the source. (**a**) The PAL constructed with ultrasound transducers. (**b**) Comparison of the measurements by using the condensed microphone and the sound level meter. (**c**) Comparison of the normalized amplitudes showing directivities of the PAL and conventional VCL measured by the sound level meter. (**d**) The directivities of the demodulated audio beams of different frequencies.
## 4 Results and Discussion
### Application Conditions  
Some countries in different regions of the world are already implementing programs that contribute to energy sustainability through renewable energies. There are still many areas of opportunity, particularly in Mexico due to its high solar radiation indexes throughout its territory. According to data from the Geographic Information System for Renewable Energies in Mexico and the Solar Radiation Observatory of the Geophysics Institute of the UNAM (National Autonomous University of Mexico), irradiance values in most states are higher than 5 kW/m\({}^{2}\), reaching maximum values of 6.89 kW/m\({}^{2}\), [41; 77], which indicates the large amount of solar resource available during most of the year. This competitive advantage of Mexico motivates the generation of electric energy through the implementation of small, medium and large-scale distributed generation systems.  
On the other hand, Figure 17 identified the following strengths, weaknesses, opportunities, and threats (SWOT analysis) for the implementation of On Grid-PV in the Mexican electricity market [72].
## 5 Conclusions  
Inspired by contrastive learning methods, an end-to-end image-dehazing model was proposed in this paper. Specifically, a one-sided image-dehazing model was proposed to ensure a meaningful one-sided mapping from haze images to haze-free images by contrastive loss, and a feature fusion module and a multi-scale skip connection based on the residual network were proposed. The feature fusion module considered the connection between features of different scales and adaptively implemented feature fusion between different scales. The multi-scale skip connection based on the residual network considered the loss of feature information caused by convolution operation to use the residual network to supplement the loss of feature information and considered the feature connections between different layers to combine the image texture detail information represented by the low-level features and the image semantic information represented by the high-level features in order to better preserve the detailed features of the original image. The quantitative results and visualization results on both synthetic haze image datasets and real-world haze image datasets showed that the proposed method achieved better results with better dehazing results and better visualization of the obtained haze-free images compared with existing dehazing algorithms.  
Compared with other dehazing algorithms, the unsupervised dehazing algorithm proposed in this paper had a relatively large number of parameters; hence, it would not be very dominant in engineering applications, indicating that the number of parameters in the algorithm needs to be optimized. Therefore, in future work, research on the tailoring method of this algorithm will be carried out to reduce the computational complexity without changing the algorithm performance and further enhance the engineering application value of the algorithm in this paper.  
Conceptualization, Y.Y. and Z.T.; methodology, Y.Y.; software, Y.Y.; validation, Y.Y.; formal analysis, Y.Y. and B.Z.; investigation, Y.Y., B.Z., X.S. and Y.Z.; writing--original draft preparation, Y.Y.; writing--review and editing, Y.Y., Q.L. and Z.T.; project administration, Q.L.; funding acquisition, Z.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key Program Project of Science and Technology Innovation of the Chinese Academy of Sciences (No. KGFZD-135-20-03-02) and the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA28050401).  
Not applicable.  
The authors declare no conflict of interest.  
The following abbreviations are used in this paper:  
\begin{tabular}{l l} CNN & Convolutional Neural Network \\ GAN & Generative Adversarial Network \\ CycleGAN & Cycle-Consistent Adversarial Network \\ FFM & Feature Fusion Module \\ MSC & Multi-scale Skip Connection Structure Based on Residual Network \\ DCP & Dark Channel Prior \\ CAP & Color Attenuation Prior \\ cGAN & Conditional Generative Adversarial Network \\ WGAN & Wasserstein GAN \\ SNGAN & Spectral Normalization GAN \\ LSGAN & Least Square GAN \\ IN & Instance Normalization \\ \end{tabular}