esa-sceva/satcom-chunk-collection huggingface dataset:
# Blind Image Deblurring Based on Local Edges Selection
## 2 Method
### Estimation of Blurring Kernel  
In the proposed method, the blurring kernel is obtained by Equation (12). Because of the effectiveness of the proposed edges selection method, we do not make more constraints on the blurring kernel. So, in each iteration, the intermediate blurring kernel in Algorithm 1 can be easily obtained by the fast Fourier transform (FFT) method, which is defined in Equation (13).  
\[\underset{H}{\text{argmin}}\big{\|}y-H\otimes x\big{\|}_{2}^{2}+\big{\|} \hat{y}-H\otimes x\big{\|}_{2}^{2}+\beta\|H\|_{2}^{2} \tag{12}\]  
\[H=\mathcal{F}^{-1}\bigg{(}\frac{\mathcal{F}(\hat{x})\mathcal{F}^{T}(\hat{y})+ \mathcal{F}(\hat
## 3 Result and Discussion  
### Optical Constant and Thickness Analysis of SiO\({}_{2}\) Thin Film  
The transmittance of the SiO\({}_{2}\) thin film was measured by the ultraviolet/visible/NIR spectrometer. It can be found that the curves of the transmittance spectrum are almost overlapped, as shown in Figure 6. There is no obvious waveform drift phenomenon, which means that the films deposited in different segments thickness with good consistency. The average transmittance is higher than 90% in the wavelength of 400 to 800 nm. The transmission spectrum with high transmittance shows a good optical performance. The transmission spectrogram is measured from different positions. The peaks and valleys at different positions are very close, and the difference is less than 1%.  
The relative relationship between the refractive index and the extinction coefficient can be obtained from the transmittance. The refractive indices of the five different segments are 1.429, 1.437, 1.443, 1.438, and 1.449, as shown in Table 1. The highest refractive index is 1.449 for segment five. The lowest refractive index is 1.429 for segment one. The extinction coefficients of the deposited oxide films at different positions are analyzed. The analyzed  
Figure 6: Transmission spectra of SiO\({}_{2}\) thin film deposited on different segments from one to five.  
extinction coefficients of the five different segments are 0.0007, 0.0007, 0.0004, 0.0005, 0.0006, respectively. The maximum extinction coefficient is 0.0007 for segments one and two. The minimum extinction coefficient is 0.0004 for segment three. The analysis results show that the extinction coefficients are very close. Because the analyzed wavelength is 656 nm, the absorption of the thin film is very small at longer wavelengths, and the SiO\({}_{2}\) thin film has a high transmittance.  
The surface profiler \(\alpha\)-step was used to measure and analyze the film thickness of different segments. The film thicknesses of the five segments is 566.76 nm, 567.58 nm, 566.17 nm, 567.76 nm, and 570.47 nm, respectively. The results show that the film thickness of segment five is the thickest. The film thickness of segment three is the thinnest. The average film thickness of the five segments is 567.74 nm. The thickness different between the thickest and thinnest film is 4.3 nm, so that the uniformity of the film thickness distribution is good. The film thickness of the five different segments is normalized. The thickness uniformity of the SiO\({}_{2}\) film can be calculated to be 0.38%. The relative thickness of segments one to five is very close, as shown in Figure 7.
# Ceramic Matrix Composites for Aero Engine Applications--A Review
## 4 Oxide CMCs  
The main reason for using Oxide CMCs (O-CMCs) compared with using other technical ceramics is due to the low fracture point under high mechanical or thermal load occurring to cracks made by small defects or scratches [21]. This is because O-CMCs include inorganic oxide compounds of metallic or metalloid elements with oxygen, such  
Figure 3: Oxide vs. non-oxide CMC materials research.  
as alumina, beryllia, ceria, and zirconia. According to the reported research, though, not great breakthroughs have been achieved in terms of improving crack propagation, with only the multi-strand fibers enabling a small increase in the crack, thermal shock, and elongation resistance, allowing for the usage of these materials in new applications [20]. Furthermore, the applications in which O-CMCs are used are limited to a maximum temperature of 1200 \({}^{\circ}\)C [21]. This is due to degradation in the mechanical performance of the oxide reinforcing fibers occurs, resulting in minimizing the tensile strength values of the composite [22]. To avoid this, the literature suggests the use of a weak fiber-matrix interface so that when the material is under load, the crack will occur in the weak fiber-matrix interface. This allows composites to be able to withstand a larger amount of mechanical load [23].  
The commercialization of high-strength oxide fibers started in the 1990s when the first alumina-mullite CMCs were manufactured [2]. Alumina-mullite composites with almost pure alumina fibers have the highest strength among the oxide fibers and also high creep and oxidation resistance [5]. Nowadays, new material matrices have been implemented for the construction of oxide CMC matrices, as shown in Figure 4.  
Figure 4 shows the available oxide ceramic matrix composite materials and the different types of matrices and material compositions. This type of CMCs can be used for both structural and ultra-high temperature applications. More specifically, there are two basic types of oxide base material matrices, alumina, and aluminosilicate matrix, which can be used either as the base material or the filler/reinforcement. The alumina matrix can respectively be combined with boron and titanium carbides, silicon nitrides, and titanium dioxides. On the other hand, the alumino-silicate matrix can be successfully combined with alumina/mullite fibers to create an oxide CMC material composition. These categories will be discussed in more detail in the following paragraphs.
# An Efficient Reliability Method with Multiple Shape Parameters Based on Radial Basis Function
###### Abstract  
Structural reliability analysis has an inherent contradiction between efficiency and accuracy. The metamodel can significantly reduce the computational cost of reliability analysis by a simpler approximation. Therefore, it is crucial to build a metamodel, which achieves the minimum simulations and accurate estimation for reliability analysis. Aiming at this, an effective adaptive metamodel based on the combination of radial basis function (RBF) model and Monte Carlo simulation (MCS) is proposed. Different shape parameters are first used to generate the weighted prediction variance, and the search for new training samples is guided by the active learning function that achieves a tradeoff of (1) being close enough to limit state function (LSF) to have a high reliability sensitivity; (2) keeping enough distance between the existing samples to avoid a clustering problem; and (3) being in the sensitive region to ensure the effectiveness of the information obtained. The performance of the proposed method for a nonlinear, non-convex, and high dimensional reliability analysis is validated by three numerical cases. The results indicate the high efficiency and accuracy of the proposed method.  
structural reliability analysis; adaptive metamodel; radial basis function; Monte Carlo simulation; active learning function +
Footnote †: journal: Applied Sciences  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
## 2 The Evolution and Trends of 6G Network Architecture
### Opportunities for Fundamental Change  
The base infrastructure, which includes the transport networks, needs to be significantly redesigned to handle the much higher traffic volume that 6G networks are anticipated to carry. This forecasted traffic load is predicted to be orders of magnitude higher than what 5G networks are likely to generate in the next few years. In response to these demands, we plan to make several significant adjustments.
# Linked Coupling Faults Detection by Multirun March Tests
## 3 Necessary and Sufficient Conditions for \(LCF\) Detection
Returning to the fault in Figure 2 for a sequence of addresses \(i,j\), and \(k\) of a test that detects single communication faults and its description \(\{\langle a_{i},-,A\rangle;\langle-,a_{j},A\rangle;\langle V,V,a_{k}\rangle\}\), this fault was not detected because the conditions of Statement 2 were not met. Using other address sequences (other scenarios) can ensure its detection, as demonstrated by various descriptions of this fault, depending on the time sequence of access to the cells in this fault:  
\[ \begin{array}{l}LCF(a_{i},a_{j},a_{k})=\{\langle a_{i},-,A\rangle;\langle-,a_{j},A\rangle;\langle V,V,a_{k}\rangle\};\\ LCF(a_{i},a_{k},a_{j})=\{\langle a_{i},A,-\rangle;\langle V,a_{k},V\rangle; \langle-,A,a_{j}\rangle\};\\ LCF(a_{j},a_{i},a_{k})=\{\langle a_{j},-,A\rangle;\langle-,a_{i},A\rangle; \langle V,V,a_{k}\rangle\};\\ LCF(a_{j},a_{k},a_{i})=\{\langle a_{j},A,-\rangle;\langle V,A_{k},V\rangle; \langle-,A,a_{i}\rangle\};\\ LCF(a_{k},a_{i},a_{j})=\{\langle a_{k},V,V\rangle;\langle A,a_{i},-\rangle; \langle A,-,a_{j}\rangle\};\\ LCF(a_{k},a_{j},a_{i})=\{\langle a_{k},V,V\rangle;\langle A,a_{j},-\rangle; \langle A,-,a_{i}\rangle\}.\end{array}  \tag{5}\]  
In two (_LCF\((a_{i},a_{k},a_{j})\)_ and _LCF\((a_{j},a_{k},a_{j})\)_) of the six cases in (5), the conditions of Statement 2 are satisfied, providing the necessary and sufficient conditions for their detection.
## 3 Results and Discussion
### Establishment of LMO Safety Management System  
From 2014 to 2018, the number of collected LM cotton plants increased, owing in part to advances in the LMO monitoring process. However, as time went on, the post-monitoring process became more challenging. This is because, as the numbers of management areas increased, the capacity to cover the increased areas decreased. The LMO monitoring project is important for the fulfillment of the LMO safety policy and ongoing research by the MOE. For this reason, it is necessary to establish an LMO safety management system for efficient maintenance of the conservation of the natural ecosystem in South Korea.  
Based on the study results, we proposed an LMO safety management system that combined LMO safety policy and research with LMO monitoring practices (Figure 5). The new LMO monitoring strategy is divided into four stages: survey, LMO identification, data analysis, and post-management, which are individual parts of LMO policy and research by the MOE. The survey step affects LMO import/export/use, while the LMO identification step is part of the LMO policy's biosafety plan. Furthermore, the analysis and post-management steps are linked to the LMO risk review and risk assessment/management in a sequential manner. If unintentional spills of LMOs into the natural environment occur during transportation and use, the MOE and the NIE remove the released LMOs to prevent further dispersal into the natural environment. Furthermore, the putative risk of the LMO collected environments is estimated, and it is determined whether the overall risk in the environments is acceptable and/or manageable. All information from the risk assessment is used for further LMO risk review in South Korea as part of the LMO approval process. Finally, the results of the survey, risk assessment, and review are used for the establishment of the next biosafety plan of the MOE to manage the import/export/use of LMOs in South Korea. To preserve the natural ecosystem in South Korea, this effort is critical for transporting, using, and managing approved LMO, as well as the post-management of released LMO into the natural environment.  
Figure 5: Scheme of the new LMO safety management system for natural environments. The four steps of the LMO monitoring process are as follows: (1) survey, (2) identification of LMOs, (3) data analysis, and (4) post-management. The LMO monitoring strategy is necessary for the fulfillment of LMO safety policy and research in South Korea.
# A Dynamic Surface Gateway Placement Scheme for Mobile Underwater Networks +
## 3 Background
### Design Challenges
#### 3.1.1 Long Propagation Delays  
Due to the low propagation speed of acoustic signals, UWSNs suffer from long propagation delays. This significantly increases the end-to-end latency between data transmission at the sensor and reception at the base station situated in a facility onshore or on a boat. The primary goal of surface gateway deployment is to reduce this latency. Such a deployment should maximize coverage of the sensor nodes to reduce the number of relays performed by the underwater network. This will enable signals to travel shorter distances, lowering the associated delays.
# Denoising Multiscale Back-Projection Feature Fusion for Underwater Image Enhancement
## 5 Experiment and Analysis
### Datasets  
Three underwater image datasets were used to validate the efficiency of our method: the underwater image enhancement benchmark dataset (UIEB) dataset [20], the EUVP dataset [28], and the real-world underwater image enhancement (RUIE) dataset [46]. For testing, the images with inconsistent resolution sizes in the dataset were uniformly cropped to \(256\times 256\) pixels.  
1. The UIEB dataset is an underwater real image dataset with clear images, where the clear images and the enhanced images serve as ground truth. It consists of 950 real-world underwater images, 890 of which have the corresponding clear images.
2. The EUVP underwater dataset contains a large collection (20,000) of paired and unpaired underwater images of poor and good perceptual quality. A total of 12,000 of the images have paired clear images, and 8000 of the images have unpaired clear images. The dataset was captured using seven different cameras and contains photos of different visibilities and sea areas.
3. The RUIE real underwater dataset comprises 4200 unpaired underwater images captured by moving cameras at different moments in an underwater scene. The images in the dataset exhibit different color shifts and visibility.  
In addition, three other kinds of degradation datasets were utilized to evaluate the multiple degradation knowledge distillation process, including the Rain 1400 dataset [47], the RESIDE hazy dataset [48], and the CSD snowy dataset [49].  
1. The Rain 1400 dataset consists of 1000 pairs of rainy and clear images. The dataset generates 14 rainy images for each clear image, which have different orients.
2. The RESIDE hazy dataset is a hazy image dataset with several conditions, which include the training set and the test set. The training data consists of a collection of \(13,990\) paired hazy images and clear images. The test set consists of 510 pairs of hazy and clear images.
3. The CSD snowy dataset contains 10,000 pairs of snowy images and clear images. The synthetic snowy images simulate snowflakes and snow stripes with different transparencies, sizes, and positions, effectively reflecting real-world snowy scenes.
# LP-Order Time-Frequency Spectra and Its Application in Edge Detection
## 2 Generalized Hilbert Transform (GHT)
### Theory of GHT  
If complex numbers \(S(t,f)\) denote the time-frequency spectra of the inputted real signal \(x(t)\), which are obtained by the short-time Fourier transform (STFT) [3], wavelet transform [4; 5; 6], S transform [7; 8], generalized S transform [9], EMD [10; 11; 12], VMD [13], modified short-time Fourier transform [14; 15; 16], synchrosqueezing transform [17; 18; 19; 20], and so on; then, \(S(t,f)\) can be modified as  
\[R(t,f)=\left\{\begin{array}{cc}2S(t,f)&f>0\\ S(t,f)&f=0\\ 0&f<0\end{array}\right. \tag{1}\]  
and further considering two real numbers \(S_{1}(t,f)\) and \(S_{2}(t,f)\) as the real and imaginary parts of \(R(t,f)\), respectively, then, we have  
\[\left\{\begin{array}{c}S_{1}(t,f)=\text{Re}[R(t,f)]\\ S_{2}(t,f)=\text{Im}[R(t,f)]\end{array}\right. \tag{2}\]  
where \(\text{Re}[\,]\) and \(\text{Im}[\,]\) takes the real and imaginary parts of a complex number, respectively.  
Thus, the GHT result of \(x(t)\) can be expressed as  
\[g(t)=g_{3}(t)+i\cdot g_{4}(t) \tag{3}\]  
and  
\[\left\{\begin{array}{c}g_{1}(t)=\int_{0}^{f_{N}}{[S_{1}(t,f)]^{p}df}\\ g_{2}(t)=\int_{0}^{f_{N}}{[S_{2}(t,f)]^{p}df}\\ g_{3}(t)=[g_{1}(t)]^{p^{-1}}\\ g_{4}(t)=[g_{2}(t)]^{p^{-1}}\end{array}\right. \tag{4}\]  
where \(f_{N}\) is the Nyquist frequency [24; 25; 26]; \(p\) is the order parameter and \(p>0\).  
Therefore, the instantaneous amplitudes, phases, and frequencies of the GHT result \(g(t)\) can be obtained by  
\[\left\{\begin{array}{c}\tan[\theta_{1}(t)]=g_{3}{}^{-1}(t)\cdot g_{4}(t)\\ f_{1}(t)=\frac{1}{2\pi}\frac{\partial\theta_{1}(t)}{\partial t}\\ A_{1}(t)=\sqrt{g_{3}{}^{2}(t)+g_{4}{}^{2}(t)}\end{array}\right. \tag{5}\]  
and \(A_{1}(t)\), \(\theta_{1}(t)\), and \(f_{1}(t)\) are referred to as the instantaneous amplitudes, phases, and frequencies of \(x(t)\) obtained by GHT, respectively.
# Investigation on the Tip Positioning Accuracy of Cable-Driven Serpentine Manipulators
## 2 Kinematic Model and Position Accuracy Analysis
### Kinematic Modeling  
#### 2.2.1 The Mapping between the End Pose and the Joint Angle  
For the \(n\)-DOF CDSM that can realize planar 3-DOF motion, there is a certain mapping relationship between the joint and the end pose. According to the frames in Figure 2, the homogeneous  
Figure 1: The degree of freedom (\(n\)-DOF) planar cable-driven serpentine manipulator (CDSM) (driven by \(m\) cables, \(m=2n\)).  
Figure 2: Kinematic relationships of adjacent units of the CDSM.  
transformation matrix from the frame \(R_{i,\alpha}\Big{\{}O_{i,\alpha},x_{i,\alpha},y_{i,\alpha}\Big{\}}\) to the frame \(R_{i-1,\mathrm{b}}\Big{\{}O_{i-1,\mathrm{b}},x_{i-1,\mathrm{b}},y_{i-1,\mathrm{ b}}\Big{\}}\) can be written as:  
\[O_{i-1,\mathrm{b}}\mathbf{T}_{o_{i,\alpha}}=\left[\begin{array}{ccc}\cos \theta_{i}&-\sin\theta_{i}&\mathrm{link}_{i-1,\mathrm{J},i}+\mathrm{link}_{ j,i}\cdot\cos\theta_{i}\\ \sin\theta_{i}&\cos\theta_{i}&\mathrm{link}_{j,i}\cdot\sin\theta_{i}\\ 0&0&1\end{array}\right], \tag{1}\]  
where \(\theta_{i}\) represents the rotation angle of the \(i^{\mathrm{th}}\) joint, and \(\mathrm{link}_{i-1,\mathrm{J},i}\) and \(\mathrm{link}_{j,i}\) represent connecting rods before and after the \(i^{\mathrm{th}}\) joint.  
The homogeneous transformation matrix from the frame \(R_{i,\mathrm{b}}\Big{\{}O_{i,\mathrm{b}},x_{i,\mathrm{b}},y_{i,\mathrm{b}}\Big{\}}\) to the frame \(R_{i,\alpha}\Big{\{}O_{i,\alpha},x_{i,\alpha},y_{i,\alpha}\Big{\}}\) is:  
\[O_{i,\alpha}\mathbf{T}_{o_{i,\mathrm{b}}}=\left[\begin{array}{ccc}1&0& \mathrm{B}\\ 0&1&0\\ 0&0&1\end{array}\right], \tag{2}\]  
where B represents the thickness of the disks.  
Thus, when the joint angles are given, the pose vector of end-effector can be obtained by:  
\[\mathbf{S}=\prod_{i=1}^{n}\Big{(}O_{i-1,\mathrm{b}}\mathbf{T}_{o_{i,\alpha}} \cdot O_{i,\alpha}\mathbf{T}_{o_{i,\mathrm{b}}}\Big{)}\left[\begin{array}{c} 0\\ 0\\ 1\end{array}\right], \tag{3}\]  
where \(i=1,\ldots,n\). \(\theta=\left[\begin{array}{ccc}\theta_{1}&\theta_{2}&\ldots&\theta_{i}& \ldots&\theta_{n}\end{array}\right]^{\mathrm{T}}\) and \(\mathbf{S}=\left[\begin{array}{c}X&Y&\phi\end{array}\right]^{\mathrm{T}}\), respectively, represent the joint angle vector of joints and the pose vector of end-effector.  
It should be noticed that we could obtain a unique end pose when the joint angles were determined. However, given the pose vector of the end-effector, multiple vectors of the joint angles could be worked out. This was the multiplicity of inverse kinematics due to the redundancy of the manipulator.
## 3 Proposed Method
### Proposed Proof of Familiarity (PoF) Consensus Algorithm
#### 3.2.3 Block Architecture  
Architecturally, all blocks are connected to a previous block (block 0 to block N). A block stores a decision and identities after a PoF approval. PoF does not allow any mining award or transaction fee. Since the proposed blockchain stores identities of P, C\({}_{\rm p}\), D, and I\({}_{\rm c}\) in a well-secured manner, no nonce is used in PoF. The only incentive D, I\({}_{\rm c}\), and C\({}_{\rm p}\) achieve is uprise of IFI if the patient receives a successful decision through PoF. Block 1 (Figure 7) is described here:  
* Hash of last block: Hash of recently created block is stored by this field. This tracks originality of the data of this block. If modification happens to block 0, the header of block 1 will no longer point to the header of block 0. This corrupted block is disconnected from the rest of the block (blockchain).
* Timestamp: This field stores the approval time of the final collaborative medical decision, \(\mathrm{C_{d}}\).
* Flag 1: Patient's NPII and clinical history are stored in 'Flag 1' field.
* Flag 2: Doctor's NPII and decision history are stored in 'Flag 2' field.
* Flag 3: Insurance company's NPII and policy history are stored in 'Flag 3' field.
* Flag 4: Cured patient's NPII and clinical history are stored in 'Flag 3' field.
* PII Hash: Hash of the locally stored PII is stored in 'hash' field. This can cross check the originality of the information stored in local databases of P, D, I, and \(\mathrm{C_{P}}\).
* Decision: Accepted final collaborative medical decision, \(\mathrm{C_{d}}\) are stored in 'decision' field.
* NPII: All other significant NPII (rules, conditions) is stored in the 'NPII' field of the block.  
Figure 6: Transaction among stakeholders in proposed PoF.  
Figure 7: Sample block architecture of the proposed scheme (Block 1).
# AidA*-RL: Anytime Graph-Based Path Planning with Deep Reinforcement Learning for an Autonomous UAV
## 3 Case Study: Mission Planning for an Autonomous UAV  
### Experimental Setting in Simulation  
Our experiments were carried out using two computers, one of which acted as a companion computer and one as a host. The companion computer is a powerful compute unit (compared to the flight controller) that is responsible for the processing of the high level, computationally intensive tasks such as vision processing. The architecture of the experimental system setting in the simulation is shown in Figure 10. The aim of the experiment run on the companion computer was to test the behavior of a real drone. This computer was responsible for running the path planner and for generating the octree [32; 33], for which we used the Robotics Operating System (ROS) running on Ubuntu 16.04. The iADA* path planner algorithm was implemented in C++. The specifications for the companion computer were as follows: Intel Core i7-7700, 16 GB RAM, and Nvidia GTX 2070 GPU. This computer was responsible for running the collision avoidance DRL algorithms, while the AirSim simulator and environment were run on the host computer  
Figure 8: Training: \(c\)-greedy policy of DQN model.  
Figure 9: Loss values for the DQN model.  
The DRL algorithm was implemented in Python using the Keras-rl library and the hardware specifications of the host computer were as follows: Intel Core 17 (10 th generation), 16 GB RAM, and Nvidia GTX 2070 Super GPU.  
The overall system was tested, and the results were evaluated based on the success and failure rates of the system, in static (block) and dynamic environments, with the same settings for the implementation of DQN. The size of the static environment was 300 \(\times\) 200 m, while that of the dynamic environment was 100 \(\times\) 100 m, with no limitations on altitude. The dynamic environment was restricted to a small, indoor area because the focus was on avoiding very complex dynamic obstacles. There were 17 obstacles of different sizes, seven of which were static and the remaining 10 dynamic. The obstacles moved randomly within the environment with velocities ranging between 5 and 25 m/s, with no limitations on the possible directions.