esa-sceva/satcom-chunk-collection huggingface dataset:
# Superpixel-Based Singular Spectrum Analysis for Effective Spatial-Spectral Feature Extraction
## 3 Results and Discussion
### Dataset Description
#### 3.1.3 Salinas Dataset  
The \"Salinas\" (SAL) dataset was captured over the Salinas Valley, California, USA, using the AVIRIS Sensor. The sensor has 224 channels with spectral range varying from 0.43 \(\upmu\)m to 2.5 \(\upmu\)m. This scene has a size of 512 \(\times\)217 pixels and spatial resolution of 3.7 m per pixel. The number of bands reduces to 204 after discarding 20 water absorption bands: [108, 109, 110, 111, 112], [154, 155, 156, 157], 224. The scene is mainly an agricultural area, with 16 classes in its Ground Truth. A false color representation, the Ground Truth, and the class names for the Salinas dataset are shown in Figure 7.  
Figure 5: (a) False Color Composite Image, (b) Ground Truth Image and (c) Class names for the Indian Pines Dataset.  
Figure 6: (a) False Color Composite Image, (b) Ground Truth Image and (c) Class names for the Pavia University Dataset.
## 2 Materials and Methods
### Preparation of GPP and VSCM  
A GPP with diameters of fibers varying from 90 to 680 nm was fabricated by Neo Modulus [Suzhou] Medical (Suzhou, China). The Freiburg Materials Research Center and Institute for Macromolecular Chemistry of the Albert-Ludwigs University Freiburg supplied the technology used to manufacture GPP, and the detailed structure and production methodology have been described in previous studies [30; 35]. The VSCM (Fibro-Gide(r)), comprising type I and type III porcine collagen with a porosity of 93% by volume and an average pore size of 92 um[11], was purchased from Geistlich Pharma (Wolhusen, Switzerland). GPP was prepared by punching out 6 mm diameter cylindrical specimens utilizing a corneal trephine (Yanlijian Technologies, Shenzhen, China) under sterile conditions, and VSCM was first cut into 3 mm thick slices and then punched out 6 mm specimens, which were placed at the well bottoms of 96-well plates for the experiments. The representative samples and scanning electron microscopy images of both materials are provided in Supplementary Figure S1. Tissue culture plastic (TCP) served as a control according to the previous studies [31; 35]. Scanning electron microscopy of GPP and VSCM was performed as described previously [35; 37].
## 2 New Results on Closed-Form Formulas
It illustrates that, after having a \(1\)-flip search implemented, if an \(r\)-flip search is next servedas a locally optimal solution, only those elements with the sum of the absolute value of derivatives less than \(M\) are eligible for consideration. Furthermore, when deciding on the elements of an \(r\)-flip search, we can easily check to see whether any element \(x_{i}\) by itself or with a combination of other elements is eligible to be a member of an improving \(r\)-flip move \(S\). Example 1 below illustrates this situation. **Example 1**. : _Consider an \(x^{T}Qx\) problem with \(n\) variables. Let \(x=(x_{1},\cdots,x_{n})\) be a given locally optimal solution with respect to a 1-flip search. Consider \(S=\{i,j,k,l\}\) for a possible 4-flip move. In order to have \(S\) for an improving move, all 15 inequalities, given below in Equation (18), must be satisfied. Of course, if the last inequality in Equation (18) is satisfied, all other inequalities are also satisfied. This means that each subset of the \(S\) is also an improving move. This is important in any dynamic neighborhood search strategy \(k\)-flip move for \(k\leq r\) in consideration._  
Here we have \(\varphi=\underset{i,j\in N}{Max}\big{\{}|q_{ij}|\big{\}}\) and \(M=6*\varphi:\)  
\(|E(x_{a})|<M\) for \(a=i,j,k,l\),  
\(|E(x_{a})|+|E(x_{b})|<M\) for \((a\
eq b),a,b=i,j,k,l\),  
\(|E(x_{a})|+|E(x_{b})|+|E(x_{c})|<M\) for \((a\
eq b\
eq c),a,b,c=i,j,k,l\),  
\(|E(x_{a})|+|E(x_{b})|+|E(x_{c})|+|E(x_{d})|<M\) for \(a=i,b=j,c=k,d=l\)  
Obviously, choosing the appropriate subset \(S\) to implement a move is critical. There are many ways to check for an improving subset \(S\). Below, we explain two such strategies. In addition, a numerical example is given in Appendix A.
# A Spaceborne Mounting Method for Fixing a Cubic Fabry-Perot Cavity in Ultra-Stable Lasers
## 1 Introduction  
Ultra-stable lasers (USLs) have been widely used in gravitational wave detection, optical atomic clocks, fundamental physical constant measurements, and so on [1, 2, 3, 4, 5, 6]. With the requirements of several space missions, such as the Laser Interferometry Space Antenna (LISA), the Taiji Program in Space, Space Optical Clock (SOC2), the Gravity Recovery and Climate Experiment Follow-On (GRACE-FO), etc., the development of space-based USLs is increasingly urgent [7, 8, 9, 10].  
USLs use the Pound-Drever-Hall (PDH) technique to lock continuous-wave lasers to the resonance frequency of a high-finesse Fabry-Perot (FP) cavity via large-bandwidth and high-speed electronic control systems [11, 12]. The fractional frequency instability of a laser is determined by the FP cavity's length instability [13]. Differently shaped FP cavities, including cylindrical, spherical, cubic, notched, multiple-bore, midplane, etc., have been proposed [14, 15, 16, 17, 18]. Among them, the symmetrical cubic FP cavity has lowest force- and vibration-insensitive design, which seems to be the best choice for space missions. Based on cubic FP cavities, a four-point tetrahedral mounting method was proposed for nonlaboratory applications [19]. With this configuration, a few ground-transportable USLs have been developed [13, 20, 21]. However, to the best of our knowledge, no studies describing USLs that can withstand aerospace vibration tests have been reported.  
In the present work, a spaceborne mounting method for fixing a 100 \(\times\) 100 \(\times\) 100 mm cubic FP cavity is proposed since the FP cavity is the weakest part of a USL during periods of vibration. Based on the proposed method, aerospace vibration tests of the FP cavity were carried out. To judge the performance of the FP cavity during vibration tests, three criteria are also proposed.
## 2 Methodology
### Establishment of the Computational Model  
The main characters of the MIDAS/GTS software are that it can be used to establish large-scale and complex geological models visually and generate high-quality meshes automatically. The discrete element modeling software 3 DEC is inherently quite suitable for numerically investigating the deformations and failures of jointed rock masses. However, it is difficult to establish large-scale and complex geological models, and also cannot be used to generate high-quality meshes automatically.  
Therefore, first, the MIDAS/GTS software is used to establish geological models and generate high-quality mesh models; and then, the 3 DEC software is used to numerically investigate the deformations and failures of jointed rock masses on the basis of the geological and mesh models created by the MIDAS/GTS software.  
The procedure of establishing the geological and calculation models is as follows. First, according to the geological and topographic map of the study area, the elevation of the ground surface is extracted; the ground surface is generated by MIDAS/GTS software, and the ground surface is divided into triangular surface meshes. Then, the initial geological model is established in the 3 DEC software according to the prisms generated by triangular surface meshes. Finally, the initial geological model is divided into regions by cutting multiple planes; and key joints are added to generate the final computational model.  
The boundary conditions in the numerical analysis are set mainly in terms of displacement. In this paper, the boundary conditions are as follows. Specifically, for the computational model, there are typically six boundary faces or surfaces, including the bottom, the top, and four sides around. The displacements of all nodes locating on the bottom are strictly fixed, without allowing to move. In contrast, all nodes locating on the top surface are free, i.e., the displacements are not fixed. For the four sides around, the displacements in the normal direction of the surface are fixed. For example, the x-displacements or y-displacements are fixed, while the displacements in the other two directions are not fixed.
# Removal of Fluoride and Arsenate from Aqueous Solutions by Aluminum-Modified Guava Seeds
## 2 Materials and Methods
### Effect of pH on Fluoride Removal  
In order to establish the pH value at which Al-GSs have a higher fluoride removal and to determine the influence of this parameter on the biosorbent material, batch type adsorption experiments with Al-GSs and 10 mg/L aqueous solutions of sodium fluoride (NaF) were performed. Different pH values of solutions (between 2 and 9) were established to determine their influence on the adsorption process and set this as a parameter to carry out further experiments. These values were adjusted by addingsmall volumes of 0.1 M solutions of HCl or NaOH, as appropriate. Adsorption experiments were performed as mentioned above, at different pH values of the NaF solution. Solutions were stirred at 100 rpm and 25 \({}^{\circ}\)C. Fluoride concentrations were determined as mentioned before.
# Naturally Occurring Flavonoids and Isoflavonoids and Their Microbial Transformation: A Review
## 4 Development and Utilization of Flavonoids Synthesis Genes in _Streptomyces_
venezuelae_ DHS2001, with the deletion of the native pikromycin polyketide synthesis gene, and the resulting strain produced racemic naringenin and pinocephrin from 4-coumaric acid and cinnamic acid, respectively. The yield of these two flavonoids was considerably increased by codon optimisation, and this is the first report of the phenylpropanol biosynthesis pathway in the _Streptomyces_ genus [107]. [PERSON] et al. used the heterologous biosynthesis of industrial actinomyces _S. coelicolor_ and _Streptomyces albus_ to express the novo biosynthesis genes of three important flavonoids, namely myricetin, kaempfer, and quercetin [108]. The shuffled biphenyl dioxygenase holoenzyme encoded by the _bphA1 (2072) A2A3A4_ gene cluster of _S. lividans_ exhibits a wide range of substrate specificity and hydroxylateds several flavonoids and isoflav; some of the obtained products possess free radical scavenging activity [89]. Prenyltransferase expressed by the prenyltransferase gene from _Streptomyces_ prenyltransferase HpySc (SCO7190) was shown to exhibitextensive substrate specificity in the host plant, tomato, and therefore, tomatoes accumulate prenylated flavonoids. This is the first report of prenylated flavonoid accumulation in transgenic plants [109]. Through gene exploration of _S. clavuligerus_, three naringenin-producing genes, namely _ncs_, _ncyP_, and _tal_, were found, all of which are indispensable for the process. This is the first report on the natural production of naringenin in actinomycetes [110]. The queD gene from _S. eurylhermus\({}^{\text{T}}\)_ encodes quercetinase (QueDHis6), which can convert several flavonoids. The biological function of this enzyme was studied and was found to mainly play a role in detoxification rather than in catabolism [111]. The discovery of genes involved in the production of flavonoids by _Streptomyces_ combined with newly developed techniques and increased genetic knowledge to manipulate the biosynthesis pathways is an effective way to achieve large-scale production of the essential flavonoids.
## 3 Reduced-Complexity ICFO and CIDS Detection Method  
In this section, we present a simplified formulation for joint ICFO and CIDS synchronization in 5G NR communication systems, which exploits a good autocorrelation feature of differential NR-PSS sequence. In addition, we validate the viability of the proposed NR-PSS detection method by evaluating the probability of detection failure and computational complexity.
# Stability Analysis of the Landfill Slope with an Engineered Berm Under Composite Failure Mode
## 3 Results
### Error Analysis
#### 3.1.1 Analysis of the Safety Factor When Over-Berm Failure Occurs  
From Figure (a)a, it can be seen that \(FS_{\text{max}}\), \(FS_{\text{min}}\), and \(FS_{\text{ave}}\) all increase with the increase in \(H_{\text{B}}\). When \(H_{\text{B}}\) increases from 3 m to 15 m, \(FS_{\text{max}}\) increases by 8.56%, \(FS_{\text{ave}}\) increases by 6.60%, and \(FS_{\text{min}}\) increases by 4.53%. It can be seen that the increase in the above three types of safety factors gradually decreases.%  
Figure (b)b shows that that \(\Delta\) increases with the increase in \(H_{\text{B}}\). Specifically, when \(H_{\text{B}}\) = 3 m, 6 m, 9 m, 12 m, and 15 m, \(\Delta\) is 2.86%, 3.14%, 3.58%, 4.16%, and 4.89%, respectively. Thus, the maximum value of \(\Delta\) is 4.89%.  
From Figure (a)a, it can be seen that \(FS_{\text{max}}\), \(FS_{\text{min}}\), and \(FS_{\text{ave}}\) all slightly decrease and then increase with the increase in \(\xi\).  
Figure 10b shows that that \(\Delta\) increases with the increase in \(\alpha\). And it can be determined that when \(\xi\) = 15\({}^{\circ}\), 20\({}^{\circ}\), 25\({}^{\circ}\), 30\({}^{\circ}\), 35\({}^{\circ}\), 40\({}^{\circ}\), 45\({}^{\circ}\), \(\Delta\) = 3.22%, 3.26%, 3.32%, 3.41%, 3.53%, 3.71%, 3.95%. It can be concluded that the maximum value of \(\Delta\) is 3.95%.  
From Figure 11a, it can be seen that \(FS_{\text{max}}\), \(FS_{\text{min}}\) and \(FS_{\text{ave}}\) all increase with the increase in \(\delta_{\text{B}}\). When \(\delta_{\text{B}}\) increases from 12\({}^{\circ}\) to 30\({}^{\circ}\), \(FS_{\text{max}}\), \(FS_{\text{ave}}\), and \(FS_{\text{min}}\) increase by 2.55%, 2.19%, and 1.81%, respectively. The changing trend of the safety factor is the same as the effect of \(H_{\text{B}}\) on the safety factor.  
Figure 11b shows that \(\Delta\) increases as \(\delta_{\text{B}}\) increases. Specifically, when \(\delta_{\text{B}}\) = 12\({}^{\circ}\), 15\({}^{\circ}\), 18\({}^{\circ}\), 21\({}^{\circ}\), 24\({}^{\circ}\), 27\({}^{\circ}\), and 30\({}^{\circ}\), \(\Delta\) is 3.25%, 3.29%, 3.35%, 3.40%, 3.47%, 3.55%, and 3.64%, respectively. Thus, the maximum value of \(\Delta\) is 3.64%.  
From Figure 12a, it can be seen that \(FS_{\text{max}}\), \(FS_{\text{min}}\) and \(FS_{\text{ave}}\) all increase with the increase in \(c_{\text{B}}\). When \(c_{\text{B}}\) increases from 0 to 30 kN/m\({}^{2}\), \(FS_{\text{max}}\), \(FS_{\text{ave}}\), and \(FS_{\text{min}}\) increase by 2.36%, 2.28%, and 2.20%, respectively. It can be seen that the increase above three safety factors gradually decreases, but the difference among those not significant.  
Figure 12b shows that \(\Delta\) increases with the increase in \(c_{\text{B}}\). And it can be determined that when \(c_{\text{B}}\) = 0, 5 kN/m\({}^{2}\), 10 kN/m\({}^{2}\), 15 kN/m\({}^{2}\), 20 kN/m\({}^{2}\), 25 kN/m\({}^{2}\), 30 kN/m\({}^{2}\), \(\Delta\) = 3.31%, 3.33%, 3.34%, 3.36%, 3.37%, 3.38%, 3.40%. It can be concluded that the maximum value of \(\Delta\) is 3.40%.
# Experimental Validation of Peer-to-Peer Distributed Voltage Control System
## 6 Results of the Experiment
### Key Performance Indicators
#### 6.3.2 Voltage Quality  
The voltage quality is quantified by the metric \(E\geq 0\) defined by Equation (4). The metric \(E\) integrates the over and under voltages as shown in Figure 13. This means that both the duration of a voltage problem and its severity will increase the metric \(E\). A value of zero is the best possible value and indicates that there are no over or under voltage issues: the higher the \(E\), the worse the voltage problem.  
\[E=\sum_{i=1}^{N}\int_{t_{\text{start}}}^{t_{\text{end}}}\Big{(}\text{max}\big{(}V_ {i}-V^{\text{max}}\,\ 0\big{)}+\text{max}\big{(}V^{\text{min}}-V_{i}\,\ 0\big{)}\Big{)}dt \tag{4}\]  
Table 1 shows a comparison between the regulated and the unregulated voltage profiles based on the voltage quality metric. \(E\) is the sum of the metrics \(E_{0}\), \(E_{1}\) and \(E_{2}\) of the nodes 0, 1, and 2, respectively. The P2P voltage control reduced the metric \(E\) from 58.724 to 2.633. \(E\) of the regulated voltage profiles is slightly higher than zero, because it takes the algorithm some time until it has resolved the voltage issues.
# A Safety Prediction System for Lunar Orbit Rendezvous and Docking Mission
## 2 Mission Objectives and Characteristics
### Mission Characteristics  
The space rendezvous and docking optical navigation uses the cooperative target vision measurement method to obtain the relative position and attitude between the target coordinate system and the chaser [20; 21], and the calculation method is the linear transformation method or the space rear intersection method [22]. The literature [23; 24] shows that to extract features and recognize the target in a space environment, the uncertainty and influencing factors come from the following three aspects: imaging noise, geometric distortion and scene complexity. The imaging noise can be tested on the ground to find an appropriate image filtering and denoising algorithm [25; 26].  
Geometric distortion is a key issue that affects measurement accuracy. Taking into account the changes in the imaging environment before and after launch, this distortion is difficult to accurately calibrate on the ground. It needs to be adaptively calibrated according to the structure information of the imaging target during the measurement process, which can overcome the effects of distortion to some extent [23].  
How to distinguish the target from the complex and changeable background is an important issue in the research of target recognition. The scene of the space target is relatively simple, and there is a clear distinction between the foreground and the background. However, the scale and posture of the space target in the scene vary greatly. During the process of rendezvous and docking, the distance of the target in the scene can vary from several thousand kilometers to less than 1 m [27], and the brightness of the target is affected by the direction of the sun, the direction of sight, the normal direction of the target surface and self-occlusion, resulting in a wide range of uneven brightness changes [2]. On the other hand, due to the constraints of factors such as space environment, power consumption and volume, the devices and processors that make up the computing platform on the spacecraft are highly reliable but have low operating frequencies. In order to meet the real-time nature of the control system, there are very high requirements for the efficiency of image processing and analysis algorithms [4].
## 4 Simulation Results
### Case Study 1  
Let us consider a large network partitioning into 4 disjoint sub-networks. Each sub-network has only one super-node. These super-nodes interact with each other by the graph \(G=(V,E)\), depicted in Figure 3.  
Figure 3: A network constituted by 4 nodes.  
Figure 2: Network partitioned in 4 subsets. Super-nodes are depicted in black.  
This network does have the Laplacian eigenvalues \(sp(\mathbf{L})=\{0,4,4,4\}\). With the parameter of each super-node, denoted as \(\mathbf{x}(0)=\{0.7417,0.7699,0.3216,0.5466\}\), after deploying Algorithm 1, the set of \(\mathbf{\alpha}_{t},t=1,\ldots,3\) is obtained. The nodes trajectories are described as in Figure 4.  
As can be seen, all \(\mathbf{\alpha}_{t}\) execute the consensus problem at the beginning of the procedure, and then dig into satisfying the constraint.  
Figure 5 illustrate the convergence of the cost function \(\frac{1}{2}\sum_{t=1}^{N-1}\mathbf{\alpha}_{t}^{\mathsf{T}}\mathbf{L}\mathbf{\alpha}_ {t}\) in according to the constraint \(\mathbf{x}(N-1)=\tilde{x}\mathbf{1}\).  
Furthermore, the Algorithm 2 is applied to eliminate the unexpected eigenvalues. As a result, we receive only one eigenvalue \(\lambda=\frac{1}{0.25}=4\). In order to accomplish the Laplacian spectrum estimation's procedure, we make use the Proposition 1 to pick out \(m=3\). Now, the entire Laplacian spectrum is achieved.  
At this step, the robustness index such as Kirchhoff index or the number of spanning trees can be calculated.  
\[\mathcal{R}_{\mathbf{L}}=N\sum_{i=2}^{D+1}\frac{m_{i}}{\lambda_{i}}=4\frac{3} {4}=3.\]  
Figure 4: Trajectories convergence of \(\mathbf{\alpha}_{t}\).  
Figure 5: Convergence of the cost function according to its constraint.  
Now, let us see how the proposed procedure works via the table below.  
Table 1 is obtained after executing the proposed procedure.  
As can be seen in Figure 4, at the iteration of around 90 the procedure can be stopped. In order to access the robustness of the whole large network, it is necessary to define the critical threshold, introduced in [16].  
Next, we implement a comparison with the Lagrange method described in [1] by considering Case study 2.
## 5 Experiments
### Diagnosis by the Proposed Method  
The proposed method was applied to the fault data of intershaft bearings, and the analysis results are shown in Figures 26 and 27.  
Figure 24: Analysis results for fault data of outer ring using envelope order demodulation algorithm based on spectral kurtosis (SK): (**a**) kurtogram and (**b**) envelope order spectrum.  
Figure 25: Analysis results for fault data of inner ring using envelope order demodulation algorithm based on SK: (**a**) kurtogram and (**b**) the envelope order spectrum.  
Figure 26 shows the results for the fault data of the outer ring using the proposed method. Figure 26a is the kurtogram. The center frequency of the optimum demodulation band is 9200 Hz, and the bandwidth is 800 Hz. In other words, the optimum demodulation band is [8800,9600] Hz. Figure 26b shows the envelope order spectrum obtained by processing the data according to the demodulation bandwidth selected by (a). An order of 15.88 and its harmonic are the dominant components, which match the order of 15.9 for the FCFs of the intershaft bearing's outer ring.  
Figure 27: Cont.  
Similarly, Figure 27 shows the analysis results for the fault data of the inner ring. From Figure 27a, the center frequency of the optimum demodulation band is 33,600 Hz, and the bandwidth is 3200 Hz. In other words, the optimum demodulation band is [32000,35200] Hz. Figure 27b shows the envelope order spectrum. An order of 18.09 and its harmonic are the dominant components, which match the order of 18.1 for the FCFs of the intershaft bearing's inner ring.  
The analysis of the experimental data shows that the proposed method is effective at extracting the weak fault features of intershaft bearings even in the presence of strong noises.  
In addition, the demodulation bands in Sections 5.4 and 5.5 are completely different, leading to different calculation results. Compared with the algorithm in Section 5.5, the algorithm in Section 5.4 lacks the prewhitening step based on linear prediction. Prewhitening plays a key role in preprocessing the vibration signals of the intershaft bearing. This step retains the impulsive components of the bearing and filters out other noise components.