esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The limitations of hyperspectral shadow compensation methods lie in the fact that model-based methods require assumptions about the interaction between ground objects and spectra. The generative deep learning methods are not suitable for hyperspectral data with high spatial resolution, which cannot maintain spatial details well [17; 18]. Moreover, current methods cannot effectively address the issue of nonlinear spectral attenuation caused by shadows, and lack consideration for the transition regions at the edges of shadows. In this work, we propose an adaptive shadow compensation method in hyperspectral images via multi-exposure fusion and edge fusion. Shadow detection is performed by obtaining shadow feature maps through color space conversion and applying adaptive threshold segmentation to identify shadow regions. In the first stage of shadow compensation, due to the spatial changes in shadow intensity, we adaptively compute exposure coefficients by contrasting spectral reflectance under shadowed and non-shadowed conditions. Fusion weights are derived from single-channel exposure, contrast, and inter-channel spectral variances, while the inter-channel spectral variances can effectively reduce the nonlinear attenuation caused by shadows. These weights facilitate the merging of Laplacian and Gaussian pyramids, which are constructed from exposure sequences and fusion weights, respectively. In the second stage, smooth transitions between shadowed and non-shadowed areas are achieved by using edge fusion based on the p-Laplacian operator. Specifically, the p-Laplacian operator is obtained by calculating the gradient of the image, and pixel-wise fusion is performed based on the confidence of edge pixels. It resolves the edge spectral distortions caused by variations in shadow components at the boundary. Additionally, we introduce a hyperspectral image dataset to evaluate spectral fidelity after shadow compensation and validate our method using the new dataset. In summary, the primary contributions of this work can be concluded as follows:  
1. We introduce a shadow compensation method for hyperspectral images utilizing multi-exposure fusion. This method adaptively computes exposure coefficients and effectively merges exposure sequences by employing spectral variance as the fusion weight, thus mitigating nonlinear attenuation in shadow regions. 2. We propose an edge fusion method using the p-Laplacian operator to achieve smooth transitions and seamless merging between shadowed and non-shadowed areas. 3. To address the issue that existing datasets cannot evaluate spectral fidelity after shadow compensation, we develop a hyperspectral image dataset with a uniform background to validate different methods using spectral similarity metrics. 4. Experimental results from both the public and proposed dataset demonstrate that our method can effectively compensate shadows, improving classification performance in hyperspectral images.
# A Systematic Survey on 5G and 6G Security Considerations, Challenges, Trends, and Research Areas
## 3 5G Security Architecture: A Systematic Approach
### Radio Access Network
#### 3.2.4 Reconfigurable Intelligent Surfaces (RISs) and RIS-Aided Beamforming  
Reconfigurable Intelligent Surfaces (RISs) are surface structures that are programmable in order to aid RF signal propagation in specific areas. Broadly, they can be categorized into RIS that use metamaterial structures comprised of individual elements typically much smaller than the wavelength of the signal they are aiding. These metasurfaces are highly complex and provide significant capabilities. A far simpler and cost-effective approach to RIS instead utilizes surface-attached antenna arrays to provide RIS-aided beamforming to enable efficient data repeating/reflection. These devices are intended for high-frequency applications for 5G and especially for 6G deployments, as they aid in directing highly directional signals and can do so with high energy efficiency [59]. Figure 7 illustrates the main systematic differences between an architecture with the aid of RIS and without. Principally, there are two main security implications when implementing RIS: passive eavesdroppers and authentication of the RIS equipment. Presented in [60], the authors showcase an approach of utilizing a multiplicative random process at the RIS to mitigate the effectiveness of a passive eavesdropper. This is accomplished by degrading the quality of the reflected signals along with a one-time pad system at the RIS. The authors claim that this method outperforms conventional additive randomness methods in terms of power efficiency, as there is no artificial interference being transmitted. Results from both [61; 62] share their claims on how RIS can heighten the security of the wireless communications in 5G and 6G applications at the physical layer.  
One major security challenge with 6G communications and RIS is the deployment of an Illegal RIS (IRIS) into a network, which provides for extensive research opportunities in this domain, some of which are described in the RIS 6G review [63]. The authors of this paper state that preventing IRIS attacks requires powerful and effective countermeasures to defend against passive jamming attacks [64], as well as interference and leakage attacks, both of which are affecting the physical layer security (PLS) of the system. It will therefore be important for network operators to enforce strict authentication methods for their RIS systems and to continuously monitor the radio conditions at the edge of the deployment, which may be feasible also through the use of AI/ML.
# Nonlinear Stability Analysis of Shallow-Buried Bias Tunnel Based on Failure Mode Improvement
## 5 Comparative Analysis with Existing Results  
To verify the reliability of the calculation method of the arc failure mode mentioned above, take the shallow tunnel burial depth of the slope section \(H=20\) m, rectangular tunnel height \(h=10\) m, span \(b=10\) m, bulk density of surrounding rock \(\gamma=20\) kN/m\({}^{3}\), uniaxial tensile strength \(\sigma_{\text{t}}=30\) kPa, cohesion of surrounding rock \(c_{0}=10\) kPa, angle of internal friction in the surrounding rock \(\varphi=18^{\circ}\), and slope surface inclination \(\delta=18^{\circ}\), the slope top load is\(p=0\) kPa, the ratio of horizontal support force to vertical support force of surrounding rock \(K=0.4\), \(0.5\), \(0.6\), \(0.8\), \(1.0\), and \(1.2\), and the nonlinear coefficients \(m=1.0\), \(1.1\), \(1.2\), and \(1.4\). To facilitate comparative analysis, the calculated angle of internal friction of the surrounding rock \(\varphi_{\text{c}}\) and friction angle \(\theta\) on both sides of the soil column at the top plate refer to the physical and mechanical indexes of the surrounding rock in Reference [17], i.e., grade VI surrounding rock \(\varphi_{\text{c}}=30^{\circ}\)\(\sim\)40\({}^{\circ}\) and \(\theta=0.5\)\(\varphi_{\text{c}}\); the calculation results obtained by different methods are demonstrated in Table 1.
## Appendix A Maximum Likelihood Estimation of the Experimental Tooth Root Fatigue Fracture SN Curve  
ML is a parameter estimation technique based on finding distribution parameters that are most likely to describe the collected data. Mathematically, those estimated parameters are the ones that maximize the likelihood. Such an estimation technique is typically adopted to estimate the stress-life relationship of components (e.g., fatigue specimens, insulations, etc.). More complete details about this estimation method can be found in [61; 63; 64; 79].  
One of the greatest advantages of ML is its capability to deal with different kinds of data. Indeed, within the same statistical framework, it is possible to take into account, together with its statistical meaning, failures (i.e., observed data), survivals (i.e., right-censored data), failures occurring within different inspections intervals (i.e., interval-censored data), and failures occurring before the first inspection interval (left-censored data). In the casediscussed here, only observed data and right censored data are present. For the Spindel-Haibach model (i.e., Equation (8)), the likelihood \(\mathcal{L}\) can be described as:  
\[\mathcal{L}=\prod_{i=1}^{n}\bigl{(}f_{x_{i};\mu,\sigma}\bigr{)}^{\delta_{i}} \cdot\prod_{i=1}^{n}\bigl{(}1-F_{x_{i};\mu,\sigma}\bigr{)}^{1-\delta_{i}} \tag{8}\]  
where \(f_{x_{i};\mu,\sigma}\) and \(F_{x_{i};\mu,\sigma}\) are the PDF/CDF describing the phenomena and \(\mu\) and \(\sigma\) are the parameters of the normal distribution (i.e., mean and standard deviation). \(\delta_{i}\) is equal to 1 in the case of observed data and 0 in the case of right-censored data. \(\prod_{i=1}^{n}\bigl{(}f_{x_{i};\mu,\sigma}\bigr{)}^{\delta_{i}}\) is the term used to refer to observed data and \(\prod_{i=1}^{n}\bigl{(}1-F_{x_{i};\mu,\sigma}\bigr{)}^{1-\delta_{i}}\) is the one referring to right-censored data. Here, \(\mu\) and \(\sigma\) are defined according to Section 5.  
However, in order to facilitate the parameter estimation procedure, it is typical to work on the log-likelihood \(\ell\) (i.e., the logarithm of \(\mathcal{L}\)), as the same properties apply:  
\[\ell=\ln\mathcal{L}=\sum_{i=1}^{n}\delta_{i}(\ln f_{x_{i}})+\sum_{i=1}^{n}(1- \delta_{i})(\ln(1-F_{x_{i}})) \tag{9}\]  
Considering a model with \(p\) parameters \(\gamma_{1},\gamma_{2},\ldots,\gamma_{p}\), the estimated parameters \(\hat{\gamma}_{1},\ \hat{\gamma}_{2},\ldots,\ \hat{\gamma}_{n}\) are those that maximize the log-likelihood:  
\[\hat{\ell}=\ell_{(\gamma_{1},\ \hat{\gamma}_{2},\ldots,\ \hat{\gamma}_{n})}= \max_{\gamma_{1},\gamma_{2},\ldots,\gamma_{p}}\ell_{(\gamma_{1},\gamma_{2}, \ldots,\gamma_{p})} \tag{10}\]  
where \(\hat{\ell}\) is the maximum log-likelihood.  
From a practical point of view, as typical calculation software does not present maximization algorithms, \(\hat{\gamma}_{1},\hat{\gamma}_{2},\ldots,\hat{\gamma}_{p}\) are estimated by minimizing \(-\ell\).
## 3 System Model and Problem Formulation
### System Model
#### 3.1.2 Analysis of Communication and Interference  
Suppose that all UAV nodes transmit data packets at a fixed rate \(R_{0}\). Then, UAV-\(j\) can correctly receive the packets from UAV-\(i\) if the maximum transmission rate between UAVs \(i\) and \(j\) is \(C_{ij}\geq R_{0}\):  
\[C_{ij}=B\log_{2}\left(1+\frac{p_{t}}{L_{ij}\sigma^{2}B}\right)\geq R_{0} \tag{3}\]  
\[d_{ij}\leq\frac{c}{4\pi f\sigma}\sqrt{\frac{p_{t}}{B(2^{R_{0}/B}-1)\eta_{los}}} \tag{4}\]  
Thus, the UAVs within the range \(d_{C}\) of UAV-\(i\) can communicate correctly, where \(d_{C}\) is given in (5).  
\[d_{C}=\frac{c}{4\pi f\sigma}\sqrt{\frac{p_{t}}{B(2^{R_{0}/B}-1)\eta_{los}}} \tag{5}\]  
The power of the signal of UAV-\(i\) after transmitting a distance \(d\) is given in (6).  
\[p_{r}=\frac{p_{t}}{\eta_{los}\left(\frac{4\pi f}{c}\right)^{2}d^{2}} \tag{6}\]  
Then, a UAV node within the range \(d\) of UAV-\(i\) receives the interference power \(p_{r}\) if it does not communicate with UAV-\(i\). As an approximation, when the power of the interference signal is lower than the noise power, it has no effect on communication. Thus, the interference range \(d_{I}\) of UAV-\(i\) is (7).  
\[d_{I}=\frac{c}{4\pi f\sigma}\sqrt{\frac{p_{t}}{\eta_{los}B}} \tag{7}\]  
As shown in Figure 2, UAV-2 is in the communication range of UAV-1, and UAV-3 is in the interference range of UAV-1. UAV-5 is in the communication range of UAV-4, but it is outside the interference range of UAV-1. Thus, UAV-4 and UAV-1 can simultaneously transmit signals to UAV-2 and UAV-5, respectively. UAV-3 and UAV-5 cannot transmit a signal when UAV-1 is transmitting a signal because this may affect the reception of UAV-6. In other words, when the distance between two UAVs is longer than \(d_{C}+d_{I}\), they can transmit signals in the same time slot.  
Figure 2: An illustration of communication distance and interference distance.
# Results and Achievements of the ALLIANCE Project: New Network Solutions for 5G and Beyond
## 1 Introduction
This element is in charge of providing accurate and relevant information from the networks and the users to the KDN to make proper dynamic decisions based on the current and future status of the network. In our KDN framework, decisions are taken according to the data coming from users and networks and, with the current proliferation of untrusted and malicious sources, it is of great importance having an efficient distributed system able to provide the necessary confidence to these data. For this very reason, we have proposed (Section 5) a new solution to distribute, control and authenticate routing information between different administrative domains using blockchains as an alternative to conventional Resource Public Key Infrastructure (RPKI). Finally, under the umbrella of KDN, we have investigated the appropriateness of two networking solutions for 5G/B5G: one focused on the well-established SDN/NFV technology and the other adopting an outside the box technology called RINA. The research activities in the SDN/NFV domain is presented in Section 6. For this domain, we have considered an overall architecture (i.e., both the control and the optical layers) where an IA-based orchestrator is in charge of controlling and managing an ultra-high-capacity spatially and spectrally flexible all-optical network infrastructure. On the contrary, we have only considered the packet layer for the RINA domain as this solution is still in development and limited technologies is currently supported. In Section 7 we describe our experiments and findings about its capability of offering digital services with isolated network slices and guaranteed Quality of Service (QoS). In addition to describing the main conclusions of the ALLIANCE project, in Section 8 we highlight the key lessons learned with a vision towards the future 6G.
## 7 Comparison of the Feasibility of Using a Hybrid SPV/WTG system vs. a Diesel Generator
### Hybrid SPV/WTG System  
1. IC cost: the hybrid SPV/WTG system IC cost is $25,880. The IC cost of the SPV/WTG system is high, due to that the components of the system are expensive comparing with DG. However, the global price of SPV/WTG system go down continuously.
2. O&M cost: by applying the proposed SPV/WTG system; a large benefits for mobile operators can be achieved for long term. Since the annual O&M cost can be decreasing to $752, which mean saving amounted 83.83% comparing with O&M cost for DG. However, savings rate will increase more and more in the future, due to the continuing rise in fuel prices.
3. Replacement cost: by applying the proposed SPV/WTG system, the batteries have a lifetime of 10 years, which is the same as the project lifetime, the SPV array has a lifetime of 25 years, and WTG and inverter have has a lifetime of 10 years, so neither requires replacement.
4. NPC cost: the total NPC include capital costs $25,880 + O&M costs $7028 \(-\) salvage $3380, equal $29,528 over the project lifetime (10 years).
5. GHG emissions: Renewable energy systems (RESs) are considered an icon of all that is green. Hence, the trend towards RESs is increasing all over the world to eliminate GHG emissions and minimise the effect on both the wallet and the environment.  
By applying the proposed SPV/WTG system, the total NPC that can be saving amounted up to 48.52%. Figure 21 summarized OPEX of the traditional power system (DG only) compare with the proposed SPV/WTG system over the project lifetime (10 years).
# Data-Free Area Detection and Evaluation for Marine Satellite Data Products
## 3 Results  
### Detection of Data-Free Area  
The confusion matrix was constructed using the validation sample set. Then, the obtained overall accuracy, Kappa coefficient, production accuracy and user accuracy were used to evaluate the accuracy of the classification results (i.e., the mean value of the classification results for each of the 11 scenes). The Kappa coefficient forms a complementary relationship with the overall accuracy [53].  
As shown in Table 4, the user accuracy of the results for all three sensor data is greater than 90%. The lowest Kappa coefficient is 0.86 for MODIS. Moreover, the lowest overall accuracy is 89.3% for MODIS, whereas the highest is 95.7 for OLCI. The obtained classification results have good accuracy. On the one hand, the separate extraction of the endmember data can eliminate the effect of the mixed pixel background for standard spectral data. On the other hand, the clustering process of the Clump algorithm can eliminate fragmentation while increasing the spatial continuity of the classification results, thus improving the classification accuracy. The accuracy of the results from using ISAM in this research is higher compared with the results of other studies that used SAM algorithms [54,55].
# State-of-the-Art Mobile Radiation Detection Systems for Different Scenarios
## 2 Scenarios
### Nuclear, Accelerator, Targets, and Irradiation Facilities--Scenario C
surrounding structural, and the specific activity of the radioactive materials produced varies significantly according to the accelerator type and setup. An example of a byproduct of accelerator-based experiments is tritium. Therefore, it is important to characterize the radiological risk, not only during normal operation but also due to beam losses, after beam shutdown, etc., as well as in the decommissioning process. Computer programs (e.g., by using Monte Carlo, deterministic or hybrid techniques) can be used to estimate radiation field intensities and accelerator structure's activation [36]. Decommissioning of particle accelerators can be challenging since activation distribution varies significantly in a facility with the possible presence of hot spots. Even knowing that, in some accelerators, the residual activation is low, this may add difficulty in the measurements due to the very restrictive radiation dose limits to humans. Despite the fact that fusion machines are not considered as accelerators, their decommissioning processes have some similarities [36]. Moreover, techniques used in the decommissioning of fission reactors were adapted to the decontamination and decommissioning of the Tokamak Fusion Test Reactor [37]. In this scenario, the mobile detection systems can be used to detect and localize possible leaks of radioactive material and quantification of the radiation field intensity (e.g., generated by the facility operation or activation products).
# A Mobile Anchor Node Assisted RSSI Localization Scheme in Underwater Wireless Sensor Networks
## 3 Detailed Description of the Proposed Localization Scheme
#### 3.1.1 Network System Architecture  
Figure 1 illustrates the network system architecture of the proposed localization scheme, which consists of one mobile anchor node and a number of underwater sensor nodes. The mobile anchor node is a ship which patrols on the sea surface of the sensing area based on a planned trajectory. The mobile anchor node is fitted with a Global Positioning System (GPS) receiver, which can obtain its real-time geographic location. The mobile anchor node can broadcast messages through a spherical omnidirectional acoustic transceiver. Sensor nodes are deployed in the sensing area and stay at their locations by anchor chains and sub-surface buoys. Each sensor node is equipped with an acoustic transceiver, which can receive messages and measure the RSSI value of each message, and has a pressure sensor installed, which can obtain its depth information.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Notation** & **Definition** \\ \hline \(L\) & The length of the trajectory \\ \(\Delta L\) & The interval between adjacent broadcast points \\ \(m\) & The number of broadcast points on the trajectory \\ \(x_{i}\) & The x-coordinate of the \(i\)th broadcast point \\ \(\mathbf{X}\) & The location vector consisting of x-coordinates of all broadcast points \\ \(r_{i}\) & The noiseless RSSI value of the message from the \(i\)th broadcast point \\ \(\mathbf{R}\) & The noiseless RSSI value vector \\ \(\widehat{r}_{i}\) & The measured RSSI value of the message from the \(i\)th broadcast point \\ \(\widehat{\mathbf{R}}\) & The measured RSSI value vector \\ \(\widehat{r}_{i}\) & The predicted RSSI value of the message from the \(i\)th broadcast point \\ \(\hat{\mathbf{R}}\) & The predicted RSSI value vector \\ \(\hat{x}_{lk}\) & The x-coordinate of the \(k\)th point inserted after the \(i\)th broadcast point \\ \(\hat{\mathbf{X}}\) & The extended location vector \\ \(\hat{r}_{ik}\) & The RSSI value of the \(k\)th point inserted after the \(i\)th broadcast point \\ \(\hat{\mathbf{R}}\) & The extended RSSI value vector \\ \(\mathbf{R_{i}}\) & The reference RSSI value vector of the \(i\)th reference trajectory \\ \(d_{i}\) & The perpendicular distance corresponding to the \(i\)th reference trajectory \\ \(\lfloor\cdot\rfloor\) & Floor function \\ \(\mathbf{\tilde{X}}\) & The vector formed by the location of all feature points \\ \(\mathbf{\tilde{R}}\) & The vector formed by the RSSI value of all feature points \\ \(\mathbf{\tilde{X}}\) & The vector formed by the location of selected feature points \\ \(\mathbf{\tilde{R}}_{i}\) & The vector formed by the RSSI value of selected feature points on the \(i\)th reference trajectory \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main notations used in this paper.
## 4 Experiments
### Simulation Test Based on Matlab  
First of all, we use the simulated high dynamic GNSS intermediate frequency data generated by Matlab and synchronous line-of-sight aiding data to verify the PLL performance with the following three aid methods and make a comparison:  
Aid method 1: PLL without assistance  
Aid method 2: acceleration aided PLL  
Aid method 3: jerk aided PLL based on a nonlinear tracking differentiator  
The above three aid methods are all based on Kalman-PLL. The acceleration-aided PLL involves adjusting the value of Kalman states according to Equation (11), and the jerk aided PLL method involves adjusting the process noise matrix coefficients of the Kalman filter according to Section 3.  
The one second duration high dynamic intermediate frequency data produced by the Matlab satellite signal simulator is used here, and the SNR is \(-15\) dB. As shown in Figure 4, the receiver's dynamic characteristic is: the initial line-of-sight velocity is 7,000 m/s, initial line-of-sight acceleration is 50 g and jerk is constant 30 g/s. Consider the conditions of aiding line-of-sight acceleration error as: the line-of-sight acceleration offset is 1 g [8; 9; 10], noise standard deviation is 0.1 g. See Figures 5 and 6 for the tracking results.  
As shown in Figure 5, where the line-of-sight acceleration bias is 1 g, noise standard deviation is 0.1 g, the PLL without assistance will fail to track; the acceleration-aided PLL can track duration scenarios, but long-duration tracking precision will be lowered; the jerk-aided PLL can track stably. As shown in Figure 6, the Doppler frequency estimated with the jerk-aided PLL is about 29,700 to 32,300 Hz, and the estimated Doppler shift rate is about 2,200 to 3,400 Hz/s, which matches with the receiver's dynamic characteristics.  
Figure 4: The high dynamic characteristics of simulated signal (line-of-sight direction).  
It is concluded on the above experimental results of simulated high dynamic signal that if there is a large offset or drift in the inertial aid information, the jerk-aided PLL based on a nonlinear tracking differentiator can be efficiently used to track the Compass B3 signal with line-of-sight jerk of 30 g/s.
# GPS/BDS RTK Positioning Based on Equivalence Principle Using Multiple Reference Stations
## Validation and Analysis  
In order to test the performance of the proposed MBRTK, we collect the static and kinematic dataset from Curtin University in Australia and Shandong University, Weihai in China, respectively. The static experiment is designed to validate the feasibility and reliability of the proposed MBRTK, and the kinematic experiment is to evaluate its positioning performance for different reference stations. The proposed MBRTK is compared with the conventional SBRTK with PAR method.