esa-sceva/satcom-chunk-collection huggingface dataset:
# Image Recovery with Data Missing in the Presence of Salt-and-Pepper Noise
## 5 Numerical Studies  
The numerical studies are conducted in this section to show the performance of the proposed joint estimation method. For comparison purposes, the results from adaptive median filter and edge preserving regularization (AM-IEPR) [11], standard median (MED) filter [6], adaptive median filter (AMF) [7], beta process factor analysis (BPFA) [4], and fields of experts (FoE) [16] are also provided. In simulations, for ADMM, the parameters of \(\lambda\), \(\rho\) and \(T\) for the estimation of \(\mathbf{n}\) are chosen as 0.25, 0.25 and 700, the parameters of \(\lambda\), \(\rho\) and \(T\) for the estimation of \(\mathbf{\alpha}\) are chosen as 0.75, 0.075 and 700, the parameters of \(\lambda\), \(\rho\) and \(T\) for the estimation of \(\mathbf{H}\) are chosen as 7.5, 750 and 50, and the maximum iteration of \(\mathbf{n}\), \(\alpha\) and \(\mathbf{H}\) are set to Therefore, the combination of framelet and APG, denoted by framelt+APG, outperforms all the other algorithms. Specifically, the _lena_ image reconstructed by framelt+APG shows the clean image with no noise residual, seen in Figure 2c. On the other hand, the best performance provided by the competing approaches is the AM-IEPR, but it still shows unclear effect, shown in Figure 2g. To observe the performance of the different algorithms in terms of the objective criterion, the results of PSNR, MAE and SSIM are provided in Figure 3, using 100 randomly selected images from SIPI Image Database. Inspecting Figure 3a shows that the framelt+APG produces the best PSNR index among all the methods, thereby yielding the best recovery performance, which is consistent with the conclusion in Figure 2. From the SSIM, it is seen that the value of the framelt+APG is very close to one, whereas the other algorithms drop significantly as the noise level increases.  
Figure 2: Restoration results of different algorithms in the case of salt-and-pepper noise only. (**a**) original image; (**b**) corrupted image with 70% salt-and-noise noise (6.95 dB); (**c**) reconstructed image obtained by framelt+APG (28.2 dB); (**d**) reconstructed image obtained by framelt+ADMM (26.5 dB); (**e**) reconstructed image obtained by wavelet+APG (26.1 dB); (**f**) reconstructed image obtained by wavelet+ADMM (23.7 dB); (**g**) reconstructed image obtained by AM-IEPR (27.6 dB); (**h**) reconstructed image obtained by MED (27.3 dB); (**i**) reconstructed image obtained by AMF (27.4 dB). AM-IEPR: adaptive median filter and edge preserving regularization, MED: median, AMF: adaptive median filter.
## 1 Introduction
### Methodology
The HS model can simulate the behaviour of both soft and stiff soils. This model is further divided into 2 options: undrained A, which considers stress-dependent elastic parameters, and undrained B, which does not account for the variation of the elastic parameters with changing overburden stress. In this study, the undrained A option has been chosen to consider changing stiffness of the soil with depth. Thus, the HS model with the undrained A option accounts for the stiffness stress dependency, allows plastic straining due to deviatoric and primary compression loading and can simulate the appropriate higher unloading stiffness, hence accounting for plastic straining before failure is reached. The clay behaviour is accounted for by considering the effects of two strain-hardening components; namely volumetric hardening (cap) and shear, where contraction and densification cause the yield surface to expand. The soil-stiffness parameters in the HS model can be determined as follows. \[\text{E}_{\text{oed}}=\text{E}_{\text{oed,ref}}\left(\frac{c^{\prime}\text{ cos}\phi^{\prime}-\frac{\sigma_{\text{s}}^{\prime}}{\text{K}_{\text{w}}^{ \text{o}}}\text{sin}\phi^{\prime}}{c\text{ cos}\phi^{\prime}+\text{p}_{\text{ref}}^{\prime} \text{sin}\phi^{\prime}}\right)^{\text{m}} \tag{2}\]  
\[\text{E}_{\text{50}}=\text{E}_{\text{50,ref}}\left(\frac{c^{\prime}\text{ cos}\phi^{\prime}-\sigma_{\text{s}}^{\prime}\text{ sin}\phi^{\prime}}{c^{\prime}\text{ cos}\phi^{\prime}+\text{p}_{\text{ref}}^{\prime}\text{sin}\phi^{\prime}} \right)^{\text{m}} \tag{3}\]  
\[\text{E}_{\text{ur}}=\text{E}_{\text{ur,ref}}\left(\frac{c^{\prime}\text{ cos}\phi^{\prime}-\sigma_{\text{s}}^{\prime}\text{sin}\phi^{\prime}}{c^{\prime} \text{ cos}\phi^{\prime}+\text{p}_{\text{ref}}^{\prime}\text{sin}\phi^{\prime}} \right)^{\text{m}} \tag{4}\]  
where c\({}^{\prime}\)\(\phi^{\prime}\) are soil effective cohesion and friction angle. P\({}_{\text{ref}}^{\prime}\) is the refences' mean effective stress and m is an exponent defining the variation of the Hs model parameters with depth. E\({}_{\text{oed}}\), and E\({}_{\text{ur}}\) are the one dimensional oedometer modulus and unloading/reloading stiffness values. The analysis involved four stages, including: The initial stage (initiation of geostatic stresses) in which equilibrium is established based on the lateral-earth-pressure coefficient at rest. \(\mathrm{k_{o}}\) is a construction stage in which all structures and interface elements are activated. Loading stages are where displacement-controlled loading is applied until failure is reached; and finally, the output stage where the structural forces and soil deformation are examined to establish the failure loads. In the FE model calculations, the lateral ultimate capacity was determined as either the maximum load at 3 m displacement of the tower head or the load causing yield stresses in the structural elements.
# Research on Underwater Wireless Optical Communication Channel Model and Its Application
## 2 Modeling of the Communication Channel of UWOC
### Analysis of Communication Range in UWOC
#### 2.1.2 Scattering Model  
The scattering of light waves in seawater primarily arises from the interaction between pure seawater and suspended particles, with the contribution of phytoplankton being disregarded. The relationship between the scattering coefficient and wavelength in transmission channels of seawater can be given by  
\[b(\lambda)=b_{w}(\lambda)+b_{d}(\lambda) \tag{4}\]  
Figure 1: Optical absorption coefficient of water.  
where the scattering coefficient of pure seawater is denoted as \(b_{w}\), and \(b_{d}\) is the scattering coefficient of other suspended particles. The scattering of light waves in seawater can be categorized into Rayleigh scattering and Mie scattering based on the particle size present in the water. Scattering caused by particles with diameters shorter than the wavelength of the light wave is called Rayleigh scattering, while Mie scattering occurs when particle diameters are comparable to the wavelength. Pure seawater molecules have sizes significantly smaller than the wavelength of light, making them subject to Rayleigh scattering. According to the test data in the literature [15], the scattering coefficient of pure seawater is lower than 0.01 for light wavelengths above 400 nm, which means the influence is minimal. The scattering coefficient of other suspended particles can be given as  
\[b_{d}(\lambda)=\frac{550}{\lambda}\times b_{d}(550) \tag{5}\]  
In the above equation, \(b_{d}(550)\) represents the scattering coefficient at a reference wavelength of 550 nm. Here, \(b_{d}(550)\) is equal to 0.125 D, and D means the concentration of other suspended particles in seawater, which is taken as 0.5 mg/m\({}^{3}\), 1 mg/m\({}^{3}\), and 2 mg/m\({}^{3}\) in different scenarios [16]. When the wavelength of light remains constant, an increase in the concentration of other suspended particles results in a higher scattering coefficient. Conversely, for suspended particles of identical concentration, as the wavelength of light increases, the scattering coefficient decreases.
# Defense against Adversarial Attacks in Image Recognition Based on Multilayer Filters
## 4 Experimental Results
### Combination of Multilayer Image Filters  
In the field of image processing, the role of filters is crucial. They assist us in removing noise from images, improving image clarity, and reducing the impact of adversarial noise generated by adversarial attacks. In this experiment, we selected five common filters for comparative testing. These five types of filters include average filters, Gaussian fil  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Datasets** & **FGSM** & **BIM** & **PGD** & **CW** & **AutoAttack** & **VNIFGSM** \\ \hline MNIST & 1080 & 3013 & 4420 & 5915 & 5876 & 5620 \\ CIFAR-10 & 4509 & 5223 & 5212 & 2293 & 5619 & 5081 \\ CIFAR-100 & 6239 & 6839 & 6825 & 6855 & 6875 & 6866 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The number of test samples used in different attack scenarios.  
ters, maximum filters, minimum filters, and median filters. These filters have their own characteristics in handling image noise, edge preservation, and other aspects.  
In order to comprehensively evaluate the performance of these five filters, this experiment tested all possible filter combinations using two different layers. During the experiment, the image classifier used CNN, and the filtering area size of all image filters was \(5\times 5\), which can effectively handle noise in the image. In addition, the standard deviation of the Gaussian filter is set to 1. In the method of using only one layer filter, the second layer filter is marked as \"null\". By comparing and experimentally verifying different filters, we can gain a deeper understanding of their advantages and disadvantages.  
Tables 2 and 3 show the average accuracy of each image filter combination defense model in six adversarial attack scenarios on the MNIST dataset and CIFAR10 dataset, respectively. The experimental results show that the \"Gaussian-Gaussian\" image filter defense model performs better, with an average accuracy of 66.7%. This method uses a two-layer Gaussian filter. Gaussian filters are very effective in suppressing adversarial attacks and generating adversarial perturbations. Meanwhile, Gaussian filters achieve smoothing of the entire image by reducing the intensity variation between one pixel and the next.
# Positioning Using IRIDIUM Satellite Signals of Opportunity in Weak Signal Environment
###### Abstract  
In order to get rid of the dependence of the navigation and positioning system on the global navigation satellite system (GNSS), radio, television, satellite, and other signals of opportunity (SOPs) can be used to achieve receiver positioning. The space-based SOPs based on satellites offer better coverage and availability than ground-based SOPs. Based on the related research of Iridium SOPs positioning in the open environment, this paper mainly focuses on the occluded environment and studies the Iridium SOPs positioning technique in weak signal environment. A new quadratic square accumulating instantaneous Doppler estimation algorithm (QSA-IDE) is proposed after analysing the orbit and signal characteristics of the Iridium satellite. The new method can improve the ability of the Iridium weak signal Doppler estimation. The theoretical analysis and positioning results based on real signal data show that the positioning based on Iridium SOPs can be realized in a weak signal environment. The research broadens the applicable environment of the Iridium SOPs positioning, thereby improving the availability and continuity of its positioning.  
Keywords:signals of opportunity positioning; Iridium; weak signal; instantaneous Doppler +
Footnote †: journal: Electronics
## 6 Opportunities and Challenges for the Single-Cell Protein (SCP) Market
There is a lack of specific research on this topic in the literature, and it is mostly only addressed theoretically. In the case of microalgae proteins, there is a risk of contamination with heavy metals such as cadmium, mercury, or lead. Toxins resulting from environmental contamination with toxinogenic cyanobacteria can also accumulate in them [138]. In products containing microalgae, there is an additional risk of microbial contamination with pathogenic bacteria or viruses. However, this can be prevented by the appropriate selection of culture media. Currently used in culture media, animal serum can bring with it many viruses. With the use of mycoprotein, the most significant risk is their potential allergenicity, especially in people with sensitive digestive systems. It can manifest as gastrointestinal problems, skin lesions, and even lead to anaphylactic shock. Allergic reactions are caused by certain mold species including _Fusarium vasinfectum_ or _Aspergillus fumigatus_. Bacterial proteins, on the other hand, are characterized by the highest content of nucleic acids (15-16%). They include purines, which are metabolized to uric acid. Its increased accumulation in the body, on the other hand, can lead to many serious conditions such as gout or hyperuricemia. There is therefore a need to reduce the nucleic acid content of proteins of bacterial origin. This effect can be achieved by using the heat treatment in processing [81]. Another limitation to the use of bacteria as SCP targets is the production of toxins by some of them. _Pseudomonas_ spp. and _Methylomonas methanica_ are examples of this. These bacteria are capable of producing large amounts of protein; however, at the same time, they can also produce endotoxins that cause febrile reactions [110]. In the production of SCPs, waste materials from various industries are often used as substrates. An example is the wastewater of the brewing industry. In this case, it is extremely important to use systems that monitor the presence of chemical and biological hazards that can cause various health problems [13]. The topic of efficiency should also be touched upon. There are challenges in increasing the production scale. The laboratory scale is not an economical but an expensive process. Moving the laboratory scale to the industrial scale involves continuous fermentation to optimize the process itself, and therefore, its cost-effectiveness. Nonetheless, the progress made in recent years in developing new processes offers high hopes for SCP products [13; 137; 139].
## 4 Results and Analysis
### Timeliness of The FSF
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**CAVE**} \\ \hline
**Method** & **PSNR** & **ERGAS** & **SAM** & **UIQI** & **SSIM** & **Time** \\ \hline Best Value & \(+\infty\) & 0 & 0 & 1 & 1 & 0 \\ CSTF & 40.691 & 0.463 & **7.953** & 0.914 & 0.957 & 386.388 \\ Hysure & 40.263 & 0.421 & 15.23 & 0.934 & 0.957 & 239.961 \\ LTTR & 34.482 & 1.138 & 12.268 & 0.81 & 0.914 & 768.284 \\ NLSTF & 24.63 & 1.521 & 14.475 & 0.717 & 0.813 & 246.843 \\ STEREO & 31.275 & 0.695 & 17.171 & 0.738 & 0.835 & 25.213 \\ NSSR & 38.379 & 0.552 & 14.337 & 0.922 & 0.952 & 125.605 \\ FSF(Ours) & **41.067** & **0.388** & 13.926 & **0.937** & **0.959** & **0.561** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The quantitative results of the experiment on the CAVE dataset. The best results are shown in bold among all methods. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Pavia University**} \\ \hline
**Method** & **PSNR** & **ERGAS** & **SAM** & **UIQI** & **SSIM** & **Time** \\ \hline Best Value & \(+\infty\) & 0 & 0 & 1 & 1 & 0 \\ CSTF & 43.072 & 0.144 & 2.123 & **0.993** & **0.989** & 289.643 \\ Hysure & 42.313 & 0.158 & 2.255 & 0.992 & 0.988 & 60.61 \\ LTTR & 32.932 & 0.623 & 7.263 & 0.918 & 0.937 & 485.533 \\ NLSTF & 29.551 & 0.672 & 7.388 & 0.924 & 0.936 & 85.319 \\ STEREO & 27.051 & 0.812 & 9.108 & 0.767 & 0.689 & 8.088 \\ NSSR & 38.27 & 0.229 & 2.466 & 0.985 & 0.981 & 60.676 \\ FSF(Ours) & **43.077** & **0.141** & **2.058** & **0.993** & **0.989** & **0.26** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The quantitative results of the experiment on the Pavia University dataset. The best results are shown in bold among all methods. our method achieves similar or even slightly better fusion results than Hysure and CSTF methods. However, in terms of the time required for fusion, Hysure and CSTF required 239.961 s and 386.388 s, respectively, while our proposed FSF method required only 0.561 s. Thus, the time required was less than 0.2% of that for CSTF and less than 0.3% of that for Hysur. In addition to FSF, the shortest fusion time, STEREO, also required 25.213 s. Thus, FSF needed only 1/20 of STEREO, while the fusion effect was much better than STEREO. Figure 4: First row: the competing approaches to reconstructed images of Pavia University at 30 th band. Second row: the corresponding error images of the 30 th band. Third row: the competing approaches to reconstructed images’ false-color images formed by bands 30 th, 60 th, and 90 th. Fourth row: the corresponding error images of the false-color images. Figure 3: First row: LR-HSI, fusion images, and ground truth images’ false-color images (formed by bands 30 th, 60 th, and 90 th) in Pavia university. Second row: LR-HSI, fusion images, and ground truth images’ false-color images (formed by bands 10 th, 19 th, and 28 th) of oil_painting in the CAVE database.
# Formal Matters on the Topic of Risk Mitigation: A Mathematical Perspective
## 2 Formalizing Risk  
To formalize the risk assessment Equation (1), we must first define hazard, exposure, vulnerability, and risk in a mathematically rigorous sense, while preserving the spirit and, as far as possible, the actual functional relationship of (1).  
To this end, consider a two-dimensional set \(\Omega\subset\mathbb{R}^{2}\) that represents our _area of study_ in some appropriate reference system, e.g., a two-dimensional section of the Earth surface with a specific choice of coordinate system. such as, for example, EPSG:32633 (WGS-84 spheroid with UTM projection, zone 33 north) to study Mt Etna [13] and references within, or EPSG:32740 (WGS-84 spheroid with UTM projection, zone 40 south) for Piton de la Fournaise [14].  
(Our formalization is actually independent from the dimensionality of the problem: we could just as well consider \(\Omega\subset\mathbb{R}^{3}\) and reason in three dimensions, e.g., for the risk associated with hazards in industrial complexes, taking into account the three-dimensionality of the distribution of people and other exposed elements. Time as a parameter could be included in a similar fashion.)
# A Multi-Layer Erbium-Doped Air-Hole-Assisted Few-Mode Fiber with Ultra-Low Differential Modal Gain
## 4 Conclusions  
In conclusion, a multi-layer-doped AH-FM-EDF is proposed and the PSO is applied to the doping concentration adjustment for low DMG. The adjustment of the modal intensity distribution to different guided modes is achieved with the introduction of the central air hole. A higher degree of overlap of integral factors can be obtained. According to the multi-layer doping design of the ring-shaped core, the DMG of the AH-FM-EDFA can be effectively reduced from 4 dB (uniform doping) to 0.14 dB (triple-layer doping) with gains of more than 20 dB. The fiber has a good gain-flattening characteristic in the whole C-band. From the simulations, the fiber we proposed has a good fabrication tolerance to the variation of doping concentration and air hole radius. Low DMG (<0.8 dB) can be realized under the \(\pm\)10% offsets of doping concentration and air hole radius.  
Figure 8: DMG versus wavelength under the shift of doping concentration of each layer, where (a–c) corresponds to the \(N_{1}\) to \(N_{3}\).  
Figure 7: DMG versus wavelength under the shift of central air hole.  
**Author Contributions:** Conceptualization, Z.L. and L.P.; methodology, J.W.; software, Z.L. and W.X.; validation, Z.L. and W.X.; formal analysis, Z.L. and J.Z.; investigation, T.N. and J.L.; resources, T.N. and J.L.; data curation, Z.L.; writing--original draft preparation, Z.L.; writing--review and editing, Z.L. and J.W.; visualization, Z.L.; supervision, L.P. and J.Z.; project administration, L.P.; funding acquisition, L.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was funded by the National Key R & D Program of China (2018 YFB1801003), the National Natural Science Foundation of China (61827817, 62005012) and the Fundamental Research Funds for the Central Universities (2021 YJS010).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare that they have no conflict of interest.
## 4 Calculation Results and Discussion
### Case 3: Flat-Nosed Projectile
The instability is induced by the LSW. When the LSW sweeps over the CS of the first-blast in the radial direction, the instability develops very quickly and becomes significant. The interaction between the separated flow and the CS aggravates the instability to some extent. The generation of the RMI can be observed more clearly from Schlieren images, which indicate the density gradient. Table 3 shows the time sequence of Schlieren photos of the RMI development obtained using numerical simulation in time sequence, corresponding to \(t\) = 1.091 ms, \(t\) = 1.615 ms and \(t\) = 1.833 ms. It can be observed from the photos that at \(t\) = 1.091 ms, in the vicinity of the CS, only a small disturbance exists. The disturbance then develops rapidly and causes the rotation and distortion of the CS. \begin{table}
\begin{tabular}{c c c}  
In addition, Figure 7c-f show the process of the BSW being created, chasing, and surpassing the PSW, from \(t=0\). 590 ms to \(t=0\). 989 ms. To illustrate this process more directly, the contours around the projectile nose at these time are depicted in Figure 8. The upper half and the lower half of the figure are separated by the axis of symmetry and correspond to pressure and density contours, respectively. In Figure 8a, when the projectile moves, under the flow stagnation effects, the density and the pressure rise in the region between the front of the projectile and the BSW. However, the projectile is located between the SSW and the CS. Therefore, the density and pressure of the front region of the projectile are relatively low. The maximum values of pressure and density in this region are 0.2 MPa and 1.7 kg/m\({}^{3}\), respectively. The forward-moving projectile compresses the air between the BSW and the PSW, causing a drastic increase in density and pressure. The pressure and density reach maximum values of 5.4 MPa and 10.4 kg/m\({}^{3}\), respectively, as shown in Figure 8b. At this moment, the BSW begins to merge with the PSW, which leads to an augmentation of the shock wave intensity. In Figure 8c, the projectile has caught up with PSW, and the intensity of the BSW reaches its maximum, corresponding to a maximum pressure of 5.7 MPa and a maximum density of 15 kg/m\({}^{3}\) in front of the projectile. Then, the projectile flies steadily in the undisturbed air, and the expansion waves between the BSW and the projectile nose are reflected in this region and finally vanish. This process causes the formation of the BSW adapted with the relative velocity of the projectile. This implies the end of the transient process and the formation of a relatively stable flow field and the BSW in front of the projectile. The corresponding maximum pressure and maximum density in front of the projectile at this time are 4.2 MPa and 11 kg/m\({}^{3}\), respectively. Additionally, the series of fan-shaped expansion waves caused at the inflection ahead of the projectile result in a rapid decline of the pressure behind the inflection. Therefore, the projectile nose resistance is primarily due to the BSW created ahead of the projectile.