esa-sceva/satcom-chunk-collection huggingface dataset:
# Feature Extraction of Bearing Weak Fault Based on Sparse Coding Theory and Adaptive EWT
## 4 Algorithm Implementation Process  
This paper proposes an algorithm based on sparse coding theory and AEWT to realize feature extraction and noise reduction of a bearing weak fault signal. The algorithm has the characteristics of self-adaptability, few unknown parameters, and high accuracy. Its implementation steps are shown in Figure 2.  
1. Firstly, the signal is transformed by FFT to obtain the Fourier spectrum. Then, the scale space method based on a rule of thumb is utilized to segment the spectrum adaptively, and the dividing points of each frequency band are obtained. Based on this, a wavelet filter bank is designed to decompose the signal and obtain component signals.  
Figure 2: Flowchart of Algorithm for Feature Extraction of Weak Faults of Rolling Bearings.  
2. The KSES of each signal component and the cross-correlation coefficient with the original signal are calculated. The component with the highest KSES value and CN value is selected. According to the spectrum segmentation map, the resonance frequency band corresponding to the selected component is checked to see if it is segmented to determine whether it is necessary to merge the adjacent components. The selected components are inversely transformed by an empirical wavelet to obtain the signal \(\overline{y}(t)\).
3. The peak frequency of the component frequency band is taken as the value of the resonance frequency parameter \(f\) of dictionary \(D\). At the same time, the appropriate value in the range of damping ratio \(\varepsilon\) is selected, which needs to ensure that the basis function decays to zero over its own length.
4. According to the selected signal characteristic morphological parameters \(\left(\overline{f},\bar{\varepsilon}\right)\), it is brought into the basis function formula (4) and expanded according to the different time shift parameters \(u\), and then the dictionary \(D\) is constructed. The value of u is \(1/f_{\text{s}}\).
5. The signal \(\overline{y}(t)\) and dictionary \(D\) are substituted into the FSS algorithm to calculate the sparse coefficient s. The sparse coefficient \(s\) and dictionary \(D\) are combined for sparse reconstruction to achieve signal reconstruction and noise reduction.
###### Abstract  
SpaceFibre is an upcoming on-board high-speed communication protocol for space applications. It has been developed in collaboration with the European Space Agency to answer the growing data-rate requirement of satellite payloads such as Synthetic Aperture Radars or hyper-spectral imagers. SpaceFibre offers a complete set of features (i.e., Fault Detection, Isolation and Recovery, and Quality of Service) that guarantees robust communication at the price of higher complexity. This article proposes an innovative modified implementation of the SpaceFibre standard: R-SpaceFibre. It has been designed to reduce hardware resources while keeping high data-rate capability and flow control. Attention is given to the trade-off between Data link layer complexity reduction and protocol features. The proposed protocol is particularly suitable in scenarios where very low bit error rate is foreseen and data integrity is not critical, for example in imaging instruments. The main advantage is a reduction of more than 40% of logical resources required per single interface. R-SpaceFibre may be a suitable solution for several applications, such as low earth orbit CubeSats, which have strict requirements in terms of available logic resources, mass, volume and cost, and more relaxed constraints in terms of upset immunity.  
SpaceFibre; CubeSats; communication protocol; FPGA; data-handling; hardware optimisation; high-speed Article Design of a Reduced SpaceFibre Interface: An Enabling Technology for Low-Cost Spacecraft High-Speed Data-Handling [PERSON] _et al._ (2019), [PERSON] _et al._ (2019), [PERSON] _et al._ (2019) and [PERSON] _et al._ (2019)
## 5 Conclusions  
In this paper, an intelligent fault diagnosis method based on GAN is proposed to identify various broken wires of steel wire ropes. The MFL signals of broken wires are transformed into time-frequency images by CWT. Data generation is carried out through the GAN, and fault identification is realized by a CNN. By combining GAN and CNN, the recognition accuracy of broken wires is improved when compared with a BP network or a conventional CNN. The experimental results show that the accuracy of the proposed method can reach 100% for 6 kinds of internal and external broken wires. The GAN-CNN based model has great application prospects for faults diagnosis of steel wire ropes.  
As for the GAN based model, this paper only studies the identification effect of several internal and external faults under laboratory conditions. The recognition performance of the model for more faults under complex working conditions has not been explored. The effective application of this model to practical detection will be the key research content in future work.  
Conceptualization, Y.Z. and L.J.; methodology, Y.Z.; software, L.Z.; validation, C.W., J.H.; formal analysis, Y.Z.; investigation, Y.Z.; resources, L.J.; data curation, C.W.; writing--original draft preparation, Y.Z.; writing--review and editing, L.J.; visualization, J.H.; supervision, Y.Z.; project administration, L.Z.; funding acquisition, L.J. All authors have read and agreed to the published version of the manuscript.  
The research is supported by the Natural Science Foundation of Shandong Province, China (Grant No. ZR2020 QE158) and the Key Research and Development Project of Qingdao, China (Grant No. 21-38-04-0002) and University Cooperation Project, China (Grant No. HJ20191C070657).  
Not applicable.  
The authors declare no conflict of interest.
# Zinc-Decorated and Nitrogen-Functionalized Hierarchical Porous Carbons for Carbon Dioxide Capture
## 2 Materials and Methods
### CO\({}_{2}\) Adsorption Experiments  
A Micromerities ASAP 2020 system was used to acquire the CO\({}_{2}\) adsorption isotherms. The adsorption temperatures were set as 298, 313, and 328 K, which was controlled using an ISO controller (Micromertics). Based on our previous experience [42], the Freundlich equation [43] (Equation (1)) was used for adsorption data fitting. It is an empirical model that assumes heterogeneous adsorption due to the diversity of adsorption active sites, where \(q_{e}\) (mmol/g) is the equilibrium adsorption capacity, \(K_{F}\) [(mmol/g)(1/kPa)\({}^{1/n}\)] is the Freundlich adsorption coefficient, \(P\) (kPa) is the gas pressure, and \(n\) is a constant indicating the isotherm curvature.  
\[q_{e}=K_{F}P^{1/n}, \tag{1}\]  
The evaluation of the thermodynamic parameters for adsorption (mainly standard enthalpy change (\(\Delta H\)), standard entropy change (\(\Delta S\)), and standard Gibbs free energy change (\(\Delta G\))) provide valuable information for understanding the spontaneity of the adsorption mechanisms. The sign of \(\Delta H\) determines whether the adsorption process is exothermic (negative) or endothermic (positive). \(\Delta S\) is an indication of the orderliness during the adsorption process. If the adsorption process becomes less random, \(\Delta S\) is negative. Ifit is more random, the value is positive. \(\Delta G\) is a critical parameter for determining the spontaneity of the adsorption process. If \(\Delta G<0\), the adsorption process is defined as a spontaneous process. If \(\Delta G>0\), the process is not spontaneous and not favorable [44; 45]. The values of \(\Delta H\) and \(\Delta S\) were evaluated using Equation (2), where \(R\) (=8.314 J/mol/K) is the universal ideal gas constant and \(T\) (K) is the adsorption temperature. Specifically, \(\Delta H\) and \(\Delta S\) were obtained from the slope and the intercept of the regression line from the ln \(K_{F}\) versus \(1/T\) plot. Then, \(\Delta G\) was calculated using Equation (3).  
\[\ln K_{F}=\frac{\Delta S}{R}-\frac{\Delta H}{RT} \tag{2}\]  
\[\Delta G=\Delta H-T\Delta S \tag{3}\]  
The above thermodynamic properties were evaluated at zero surface coverage [44]. When the adsorbent's surface is covered with a certain amount of the adsorbate, the isosteric heat of adsorption (\(Q_{\text{st}}\)) can be used to measure the energy heterogeneity at the gas-solid interfaces [46]. It can also provide information about the interaction between the adsorbate molecules as well as the affinity between gas molecules and the lattice atoms of the adsorbent [44]. The value of \(Q_{\text{st}}\) (k/mol) was determined using the Clausius-Clapeyron equation (Equation (4)). The adsorption selectivity for CO\({}_{2}\) over N\({}_{2}\) was evaluated using Equation (5) [47], where \(q\) and \(p\) denote the adsorption amount and the partial pressure.  
\[-\frac{Q_{st}}{R}=\frac{d\ln P}{d\frac{1}{T}} \tag{4}\]  
\[Adsorption\ selectivity=\frac{q_{CO_{2}}/q_{N_{2}}}{P_{CO_{2}}/P_{N_{2}}} \tag{5}\]
## 2 Materials and Methods
### Lightweight Design of the Water Body Segmentation Network
#### 2.4.3 Feature-Based Knowledge Distillation Strategy  
Although the lightweight design significantly reduces the parameter scale and computational cost of the model, its feature expression ability is often limited, which leads to a decrease in segmentation accuracy in complex remote sensing scenarios. To improve the discriminative ability of the lightweight model, this paper proposed a feature-based knowledge distillation strategy [52] to guide the shallow model to learn richer intermediate semantic features from the deep model during the training phase, as shown in Figure 8.  
Specifically, we constructed a distillation structure in which a deep network and a lightweight network run in parallel. The deep network adopts an architecture with a large number of parameters to obtain high-quality semantic representations, while the lightweight network, based on a shallow structure LKF-DCANet, learns the discriminative features of the deep network through multi-layer feature alignment. We select the encoder outputs of the 1 st, 2 nd, 3 rd, and 4 th layers of the lightweight network and align them with the feature maps of the 1 st, 4 th, 7 th, and 12 th layers of the deep network in the spatial dimension. The feature differences between the two at each layer are measured by the Mean Squared Error (MSE) loss function.  
After performing forward propagation on the corresponding encoder downsampling parts of the deep model and the lightweight model, traverse the pairs of feature maps of the deep model and the lightweight model, extract the feature maps \(T_{f}^{i}\) and \(S_{f}^{i}\) of the two models at the \(i\)-th layer, respectively, and calculate the Mean Squared Error (MSE) between these two feature maps, as shown in Equation (11).  
\[L_{feat}=\frac{1}{N}\sum_{i=1}^{N}(S_{f}^{i}-T_{f}^{i})^{2} \tag{11}\]  
where \(N\) is the total number of feature map pairs, and \(S_{f}^{i}\) and \(T_{f}^{i}\) represent the feature maps of the lightweight network and the deep network at the \(i\)-th layer, respectively.  
Based on the feature distillation loss, we jointly model it with the water body segmentation task loss to construct the overall optimization objective function \(L\), as shown in Equation (12):  
\[L=\alpha\cdot L_{main}+\beta\cdot L_{feat} \tag{12}\]  
where \(L_{main}\) represents the loss term of the semantic segmentation task, and \(L_{feat}\) represents the feature alignment distillation loss term. In the distillation experiments, the weighting coefficients \(\alpha\) and \(\beta\) were set to 0.7 and 0.3, respectively, through multiple experiments. This setting consistently achieves the best trade-off between segmentation accuracy and distillation effectiveness.  
The optimization of the overall loss function enables the lightweight model to learn the discriminative ability of the deep model layer by layer. As a result, with an extremely low number of parameters and computational resource consumption, it can achieve accurate segmentation of the boundaries and shapes of water bodies in agricultural watersheds.
# Towards Autonomous Bridge Inspection: Sensor Mounting Using Aerial Manipulators
## 1 Introduction
### Concept  
We envision a team of robots working together to attach sensors to bridges and similar grade separation infrastructure. In theory, such a task could be accomplished with a single aerial robot, at the cost of a complex mechanical design. The proposed team shown in Figure 1 consists of two drones. One drone applies the adhesive material, and the other attaches sensors. We envision a two-stage process using two-component adhesives which form a solid bond from two separate reactive components: the \"resin\" and the \"hardener\". The first UAV applies the resin by spraying it onto the surface, while the second one attaches the sensor with the hardener already applied before the flight.  
It is important to follow the prescribed ratio of the resin and the hardener to achieve the desired physical properties of the adhesive. Only when mixed together do the two components form the adhesive. The reaction typically begins immediately after the two components are mixed and the bond strength depends both on maintaining the contact and the viscosity of the mixed adhesive during the process. Manufacters can control the cure rate to achieve various working times (worklife) until final bond strength is achieved, ranging from minutes to weeks. Resin bases are usually more viscous than their respective hardener and are generally applied by brush, roller, applicator or spray. In this work, we propose attaching a canister of pressurized resin to the UAV, and spray it through a nozzle onto the infrastructure surface. In this scenario, the spray needs to be softer and less turbulent to reduce the amount of material lost due to bouncing and it must be colored for the detection in the second stage. Spraying with drones is not a novel concept [2; 3], so without loss of generality, we will omit the details of this design and instead focus on detecting, navigating to, and sustaining contact with the sprayed surface.  
In typical applications, the assemblies are usually kept in contact until the sufficient strength of the bond is achieved. When fully cured, two-component adhesives are typically tough and rigid with good temperature and chemical resistance. We rely on the robotic arm attached to the second aerial vehicle to apply a controlled contact force between the sensor and the surface. Maintaining this fixed assembly contact through the impedance control system enables us to achieve a successful curing process and create a permanent bond between the sensor and the infrastructure. After the first UAV sprays the resin onto the surface, the second aerial robot finds the sprayed part and applies the contact with the sensor's surface. Before takeoff, the surface of the sensor is brushed with a hardener. Once contact is made, it is maintained for the prescribed curing time, after which the aerial robot disembarks and leaves the sensor attached to the surface.
# Lacase in Biorefinery of Lignocellulosic Biomass
## 3 Significance of Lacase in Biorefinery  
In recent years, there has been a growing interest in lignin biorefinery for the production of valuable products. Lignin is the most abundant aromatic polymer available on Earth for utilization by human beings. For economic biomass biorefinery, it is necessary to utilize lignin for value addition rather than burning it for fuels. Generally, it is produced in large quantities in the pulp and paper industry as a waste byproduct, and is typically used as a fuel or disposed of as waste. Lacases are versatile enzyme which can act on lignin to break it into its individual components of monolignols: p-counaryl, coniferyl, and sinapyl alcohols. Lignin has first emerged as a popular concept in biorefinery where lignin is extracted first and employed for various applications, as shown in Figure 2. The integration of lignin biorefinery in cellulose and hemicellulose biorefinery has several advantages over traditional biorefinery processes. It is the only way for making the overall process economic.  
By using lignin as a substrate for lacase, integrated biorefinery can reduce the cost of production and increase the sustainability of the process. Lignin can be deconstructed into its components via lignin-degrading enzymes, including lacase. Figure 3 shows the chemical composition of lignin, and its structure and hydrolyzed components as well.  
Figure 2: Figure showing the application of lacases in different industries and bioprocesses.
# Photoconductive TiO\({}_{2}\) Dielectrics Prepared by Plasma Spraying
## 2 Experimental Section
### Characterization Techniques
#### 2.2.3 Electrical Response to Visible Light  
Photoconductivity is divided into two categories [22]: (i) persistent photoconductivity (PPC) and (ii) transient photoconductivity (TPC). The PPC phenomenon is an electrical conductivity enhancement when the material absorbs electromagnetic radiation (light), and the enhancement in conductivity remains for a considerably long time even after the removal of the light source. On the other hand, in the case of TPC, the material regains its initial conductivity as soon as the light source is removed.  
The photoconductivity of the TiO\({}_{2}\) sample was tested using a standard 60 W bulb during the simultaneous measurement of electrical DC resistance. Light power of 20 mW \(\times\) cm\({}^{-2}\) was used, and DC bias was set to 50 V. Resistance values were recorded every 30 s. About 23 mW of radiation power impacted the naked surface of TiO\({}_{2}\). A graph showing voltampere characteristics (VAC) was created using voltage sweep from 0 to 100 V DC, with a step of 5 V and a time step of 10 s. The current is shown in microamperes.
# A Comparison of Deep Transfer Learning Methods for Land Use and Land Cover Classification
## 2 Materials and Methods
### Dataset
#### 2.1.2 Data Augmentation  
The term \"data augmentation\" refers to a group of methods performed to increase the amount and caliber of training datasets so that better deep-learning models may be created with their support [37; 38]. Data augmentations include geometric modifications, RGB color modifications, kernel filters, image blending, arbitrary cleaning, feature augmentation, and so on [37; 38]. The data augmentation techniques employed in this study for the EuroSAT dataset were horizontal/vertical flipping, random brightness, random saturation, random contrast, and random cropping.
## 3 Results and Discussion
### The Characterization of Geopolymer Composites
#### 3.3.3 Scanning Electron Microscope Structure Study  
The SEM images of the geopolymer composites containing different fractions of AGW1 and AGW2 (AGW1-Gs and AGW2-Gs, respectively) are presented in Figure 8 and Figure 9, respectively. The glass waste is more compact and homogeneous than the geopolymer matrix.  
Figure 8: The SEM images of the geopolymer composite with AGW1 particles (AGW1-G): The left side shows the images at lower magnification (2000\(\times\)), while the right side contains details of the images on the left at higher magnification (10,000\(\times\)). The images depict the geopolymer matrix (GP) and particles of automotive glass waste (AGW).  
In general, the geopolymer composites are very similar according to SEM investigations. No degradation of the glass is evident, as was the case of the glass after exposure to the alkaline solution (Figures 4 and 5). The composites are stable without significant signs of degradation. Only in the case of 0.40-0.63 mm AGW2, a slight surface distortion in the geopolymer is visible.  
In most cases, the geopolymer/AGW interface shows good adhesion of the geopolymer to the AGW. In certain images within Figure 8 (0.40-0.63 mm and 2-5 mm AGW1-G), it is evident that microcracks have developed, likely either during the geopolymer  
Figure 9: The SEM images of the geopolymer composite with AGW2 particles (AGW2-G): The left side shows the images at lower magnification (1000\(\times\)), while the right side contains details of the images on the left at higher magnification (10,000\(\times\)). The images depict the geopolymer matrix (GP) and particles of automotive glass waste (AGW).  
maturation process or during the preparation of the polished blocks. The crack runs through the AGW particles, indicating a relatively strong bond between the geopolymer and the glass waste. A similar phenomenon also occurs in the second type of glass waste (Figure 9, 2-5 mm AGW2-G). Microcracks are further visible in the geopolymer matrix even in areas with the predominance of AGW particles, where the geopolymer is almost closed between them, for example 0.40-0.63 mm and 1.25-2 mm AGW1-G. In some composites, there is a visible interlayer, particularly noticeable in the larger particles of both AGWs (1.25-2 mm and 2-5 mm), implying potential ion transfer between the materials. Interlayer formation has also been observed at interfaces between geopolymers and glass particles or other materials [36, 63, 64].
## 2 Seawater Channel Modelling Elements
### Scattering Phase Function Formats
#### 2.7.6 Sahu-Shanmugan Scattering Phase Function  
The SS-SPF, proposed by [PERSON] and [PERSON] [83], is commonly used in radar remote sensing to model the scattering behaviour of electromagnetic waves by hydrometeors like raindrops and ice particles in the atmosphere. It is particularly relevant for radar applications like precipitation estimation and cloud characterisation.  
SS-SPF is a quasi-analytical approach. This approach accurately depicts the scattering of photons by marine particles through the angular range between 0.1\({}^{\circ}\) and 90\({}^{\circ}\). The SS-SPF can be expressed as follows [84]:  
\[\log\biggl{(}\widetilde{\beta}(\theta_{s})\biggr{)}=\begin{cases}P_{1}(\ln( \theta_{s}))^{2}+P_{2}(\ln(\theta_{s}))+P_{3}&0.1^{\circ}\leq\theta_{s}\leq 5^{ \circ}\quad\text{with}\ m=1,2,3\\ P_{1}(\ln(\theta_{s}))^{3}+P_{2}(\ln(\theta_{s}))^{2}+P_{4}&5^{\circ}\leq \theta_{s}\leq 90^{\circ}\ \text{with}\ m=1,2,3,4\end{cases} \tag{40}\]  
where  
\[P_{m}=\alpha_{m}e^{-x}+b_{m}(x)+c_{m} \tag{41}\]  
The definition of the values of \(\alpha_{m},b_{m},c_{m},x\), and \(y\) are given in Table 4. It is presented via Equations (40) and (41) in Table 4, and it is obtained by a semi-analytical method using the bulk refractive index (\(n\)) and particle size distribution slope (\(\mu\)). Since this function seems very nonlinear with a slope shift at roughly 5\({}^{\circ}\), it was separated into two parts: 0.1 to 5\({}^{\circ}\) and 5 to 90\({}^{\circ}\).  
The following expression gives the SS-SPF [81; 83; 85; 86]:  
\[\widetilde{\beta}(\theta_{s},g,\ \lambda)=A.\ e^{-B\ \theta_{s}C} \tag{42}\]
# Progress and Summary of Photodarkening in Rare Earth Doped Fiber
## 7 Suppression Method of Photodarkening
### Avoid Clusters  
We showed that the phenomenon of PD was related to a multi-excitation of Yb clusters emitting light in the ultraviolet region [92]. It has been reported that fibers containing Yb clusters or Yb pairs are subject to enhanced light aging, while the high concentration of Al helps to reduce PD [73; 101]. Al co-doping can adjust the distribution of ytterbium ions and effectively avoid the cluster of ytterbium ions [102]. Other studies have shown that phosphorus can also induce a cage around a part of the rare earths, so as to avoid rare-earth ion clusters [103]. Some evidence shows that co-doping phosphorus in Yb-doped fiber can avoid clusters more effectively than co-doping Al [63; 104].