esa-sceva/satcom-chunk-collection huggingface dataset:
# A Cross-Level Iterative Subtraction Network for Camouflaged Object Detection
## 2 Related Work  
### Camouflaged Object Detection  
Traditional COD methods extract a variety of hand-crafted features between the foreground and the background to segment the camouflaged object [23; 24; 25]. However, these methods are limited in their applicability to simple scenes and exhibit inefficiency in complex scenes.  
In recent years, CNN-based COD methods have made significant development, and they can be broadly categorized as follows: (1) multi-scale feature fusion: ZoomNet [26] designs a mixed-scale triplet network that takes images as input at three scales, learning their discriminative mixed-scale semantics and mining the subtle differences between the target and background. C2 FNet [15] incorporates an attention-induced cross-layer fusion module and a two-branch context-aware module to obtain richer global context information; (2) multi-stage strategy: SINet [9] mimics the predation process of natural organisms and designs a strategy of searching before recognizing. PFNet [17] proposes a position-then-focus approach and introduces the strategy of distraction mining. LSR [10] models the visual perception behavior and achieves effective edge prior and cross-comparison between potential camouflaged regions and background. MRRNet [27] proposes a match-recognize-refine strategy to recognize camouflaged objects by matching appropriate receptive fields for the camouflaged objects of different sizes and shapes. SegMaR [28] proposes the strategy of segment-magnify-reiterate to obtain detailed distraction information through the magnification process in multi-stage training to have the ability to segment complex shaped camouflaged targets accurately; (3) comparative learning: UJSC [29] introduces an uncertainty-aware adversarial training strategy that combines SOD and COD to improve SOD and COD accuracy simultaneously. UGTR [30] combines a convolutional neural network and transformer and uses a probabilistic representation model to learn the uncertainty of the camouflaged object, allowing the model to focus more on the uncertain region, leading to more accurate segmentation.  
Due to the utilization of convolutional layers, CNNs excel at extracting local features from images. These convolutional layers can effectively capture spatial hierarchies of features, which is beneficial in scenarios where local details are critical for object detection. Based on this, we adopt the multi-scale feature fusion strategy to combine information from different scales, enhancing feature representation ability. Different from the above methods, CISNet integrates cross-level features and enables the full interaction of information through an iterative approach, an approach which more fully makes use of the complementarity between different-level features.
## 4 Conclusions  
In this work, by comparing the reduction of nitrogen oxides by three biochar and analyzing their physical and chemical morphology, we found that:  
1. The structure of biochar will affect the process of O2 adsorption on the surface and its participation in the reaction. The larger the specific surface area, the lower the O2 concentration that allows to reach the optimal denitration rate of the biochar.
2. The elemental composition and type of surface chemical bonds play an important role in promoting or inhibiting the reduction of C-NO. The C element in the biochar promotes the reduction of C-NO. The denitration rate of biochar decreases faster as the O/C ratio increases, and the higher the relative content of N element, the larger the production of NFGs which catalyze C-NO reduction. The C-C bond directly affects the rate of denitration, and the (NCO)x bond and C-O bond have a positive effect on the reduction of nitrogen oxides by biochar.
3. The denitration rates and reaction conditions of the three types of biochar are different. Their specific surface area and surface morphology affect the reaction conditions, and the chemical elements and functional groups present in each one of them have an important effect on the denitration rate.  
Over the past decade, environmental and air pollution issues have attracted more and more attention. Due to its unique structure and richness in surface chemical bonds, biochar, a reusable product of agricultural waste, has important applications in the adsorption and reduction of pollutants. The research in this paper shows that biochar is very effective in the reduction of nitrogen oxides. Therefore, the application of biochar has great potential for the elimination of harmful industrial gases and environmental protection.  
Figure 7: Mechanism of C-NO reduction.  
**Author Contributions:** Conceptualization, Y.W.; data curation, N.Q., S.P.; formal analysis, N.Q.; funding acquisition, Y.W., S.C.; methodology, N.Q.; project administration, Y.W., S.C.; supervision, Y.W., X.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Acknowledgments:** This work was financially supported by the National Nature Science Foundation of China (Grant No. 51702007).  
**Conflicts of Interest:** There are no conflicts to declare.
## 3 Test Results and Discussion
### Compressive Strength  
Figure 5 shows the effect of the MWCNT and GNP content and functionalization on the compressive strength. Overall, the compressive strength tended to increase as the content of CBNs increased up to the level of 0.30% to 0.60%, whereas at contents higher than 0.60%, the strength decreased. However, the amount of change in compressive strength in all specimens was within 6%, and its level did not appear to be substantial. When compared with the MWCNT or GNP mixed specimens, the MWCNT showed an overall higher strength than the GNP, regardless of functionalization. Therefore, the shape of the MWCNTs more effectively bridged cracks than the GNPs [31]. The compressive strength of the nonfunctionalized and functionalized specimens was at a similar level, and no apparent tendency was observed for the effect on compressive strength through functionalization. Although some studies showed that excessive functionalization treatment adversely affected the strength of cementitious composites by causing physical damage to the surface of the CBNs, no such defects were found in this study [36]. This may have been because the MWCNTs and GNPs were noncovalently functionalized through the aromatic ring in melamine via \(\pi\)-\(\pi\) interactions that did not change the surfaces, unlike covalent functionalization [30].  
Figure 4: Effect of hybridization of MWCNTs and GNPs on electrical resistivity: (**a**) nonfunctionalization and (**b**) functionalization  
Figure 6 shows the effect of the hybridizations of MWCNTs and GNPs on the compressive strength. The hybrid did not improve the compressive strength, and no substantial change was observed in the compressive strength with an increase in the hybrid content. However, some studies reported that the hybrid incorporation of CBNs improves mechanical properties, including the compressive strength of cementitious composites [37, 38, 39, 40]. Further studies on the effect of hybrid incorporation of CBNs on electrical conductivity and mechanical properties are necessary.
# Bridge Damping Extraction Method from Vehicle-Bridge Interaction System Using Double-Beam Model
## 4 Bridge Damping Ratio Extraction Method
### Equivalent Traffic Flow Stiffness  
The estimation of the model parameters is an inverse problem compared to the frequency solution process for the simplified VBI model. The frequency \(\omega\) and the damping ratio \(\xi\) of a VBI system are known after solving for the undamped VBI simplified model frequencies \(\omega_{0}\) using Equation (16), and then subsequent estimation can be performed in the corresponding undamped simplified VBI model.  
The undamped VBI system frequency \(\omega_{0}=f(\omega_{b},nk_{v}/L,nm_{v}/L)\) is a function of bridge frequency \(\omega_{b}\), the number of vehicles \(n\), average vehicle stiffness \(k_{v}\), and mass \(m_{v}\). Here, the number of vehicles \(n\) and the average vehicle mass \(m_{v}\) are considered known, and the frequency of the bridge \(\omega_{b}\) can be determined when \(\omega_{0}\) and \(k_{v}\) are given. However, the function \(\omega_{0}=f(\omega_{b},nk_{v}/L,nm_{v}/L)\) does not have an analytic formula, so only numerical algorithms (such as the optimization methods) can be used to calculate \(\omega_{b}\).  
Based on the above analysis, when any of the \(k_{v}\), is combined with the \(\omega_{0}\) obtained using the measured response, an estimated bridge frequency \(\omega_{b}\) can be obtained. Therefore, in order to obtain an estimate of the bridge frequency as accurate as possible, a suitable method needs to be selected to determine \(k_{v}\). Since the frequency of a bridge does not vary with the number of vehicles \(n\), the absolute value of the slope of the line obtained by linear least squares of \(\omega_{b}\) and \(n\) can be used as the selected evaluation index, as follows:  
\[R=\left|\frac{\sum_{i=1}^{m}(\omega_{bi}-\overline{\omega}_{b})(n_{i}-\overline {n})}{\sum_{i=1}^{m}(n_{i}-\overline{n})^{2}}\right| \tag{21}\]  
where \(m\) is the number of samples, i.e., the number of samples of bridge frequencies and damping ratios obtained at different vehicle number \(n\); \(\widehat{\omega}_{bi}\) is the \(i\)-th bridge frequency sample; \(\overline{\omega}_{b}\) is the bridge frequency average; \(n_{i}\) is the \(i\)-th vehicle number samples; and \(\overline{n}\) is the average of the number of vehicle samples.  
Using the parametric optimization algorithm, Equation (21) can be used as the objective function, and the value \(k_{v}\) that minimizes the objective function can be selected. Commonly used methods for parameter optimization include genetic algorithms (GAs) [32; 33] and particle swarm optimization (PSO) algorithms [34]. The authors of this paper used a PSO algorithm to optimize the objective function. The algorithm was not the focus of this study, and the specific details of the algorithm can be found in the related literature.
# Cooperative Downloading for LEO Satellite Networks: A DRL-Based Approach
## 2 System Model  
Figure 1 shows a system model considered in this paper. LEO satellites collect data while traveling along their assigned orbits and contact GSs in a specific area. During the contact time, satellites can download data to GSs through SGLs. Since all satellites have different amounts of data and contact times according to their trajectories, there could be (1) over-burdened satellites that retain excessive amounts of data and (2) under-utilized satellites that have spare contact time to GS. In these situations, ISL can be utilized eventually download more data. For example, an over-burdened satellite A can offload its data to an under-utilized satellite B through ISL. After that, the under-utilized satellite B can deliver the received data to the GS during its contact time.  
We consider a set of GSs, \(\mathrm{K}=\{k_{1},k_{2},...,k_{K}\}\), and define the two-dimensional Cartesian coordinates (i.e., xy-coordinate) of GS \(k\in\mathrm{K}\) with altitude 0 as \(\mathbf{p}_{k}=(x_{k},y_{k})\). We also consider a set of LEO satellites, \(\mathrm{L}=\{l_{1},l_{2},...,l_{L}\}\). We assume that all satellites have the same and unvarying altitudes \(h\) during an episode \(\mathrm{T}=\{1,2,...,T\}\), and define the xy-coordinate of satellite \(l\in\mathrm{L}\) at time slot \(t\in\mathrm{T}\) as \(\mathbf{p}_{l}(t)=(x_{l}(t),y_{l}(t))\).  
For simplicity, we assume that SGLs/ISLs can be established when GSs and satellites are within a certain distance each other. Specifically, satellite \(l\) can deliver data to GS \(k\) through SGL if their Euclidean distance \(d_{lk}(t)=|\mathbf{p}_{l}(t)-\mathbf{p}_{k}|_{2}\leq d_{\mathrm{th}}^{\mathrm{SGL}}\), where \(|\cdot|_{2}\) denotes L2 norm and \(d_{\mathrm{th}}^{\mathrm{SGL}}\) is the maximal distance for SGL. Similarly, satellite \(l\) can offload data to another satellite \(l^{\prime}\) through ISL when the Euclidean distance \(d_{ll^{\prime}}(t)=|\mathbf{p}_{l}(t)-\mathbf{p}_{l^{\prime}}(t)|_{2}\leq d_{\mathrm{th} }^{\mathrm{ISL}}\), where \(d_{\mathrm{th}}^{\mathrm{ISL}}\) is the maximal distance for ISL. We also assume that satellites can transmit one data unit at each time slot, and SGL/ISL have the same data rates, as in [22].
## 5 Conclusions  
The dislocation pileup-based TMW model is further modified to include two factors: (i) the surface finish factor \(R_{s}\), and (ii) the microstructural factor M, which reflect the effects of surface roughness and microstructure pertinent to AM materials. Through analyses of the fatigue data on wrought and AM forms of 17-4 PH steel, Ti6 Al4V and AlSi10 Mg, the following conclusions can be made:  
* The AM microstructure has an effect of M\(-\)1.5-2 times of reduction in fatigue strength on average, relative to their wrought forms, which can be attributed to the presence of porosities in AM materials. HIPing can reduce the value of the microstructural factor relative to the non-HIPed state, but does not fully recover its original microstructural state, i.e., M = 1. It should also be noted that the microstructural factor for plastic strain concentration is not necessarily the same as that for stress concentration. More plastic strain-related data are needed to confirm this effect on AM materials;
* The surface roughness generally contributes to the fatigue life scatter at high stresses (at low stresses, a significant part of life scatter is due to variations in fatigue endurance limit per coupon), but a definite relationship with the arithmetic average surface roughness \(R_{a}\) is not found;
* The modified TMW model can be used to evaluate the distribution of fatigue endurance limit from the whole set of test data, which can facilitate the determination of fatigue strength allowables.  
Conceptualization, X.W.; methodology, X.W.; validation, X.W.; formal analysis, P.K., H.M., J.M., P.S., J.M.T.; data curation, P.K., H.M., J.M., P.S., J.M.T.; writing--original draft  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material** & **Distribution Function** & \(\mu\) & \(\sigma\) \\ \hline Wrought AA6061 & \(f(x)=\frac{1}{av\sqrt{2\pi}}\exp\left[-\frac{(\ln x-\mu)^{2}}{2\sigma^{2}}\right]\) & 4.828 & 0.15 \\ SLM AlSi10 Mg, hor. & \(f(x)=\frac{1}{\sigma\sqrt{2\pi}}\exp\left[-\frac{(x-\mu)^{2}}{2\sigma^{2}}\right]\) & 77 & 14 \\ SLM AlSi10 Mg, vert. & \(f(x)=\frac{1}{\sigma\sqrt{2\pi}}\exp\left[-\frac{(x-\mu)^{2}}{2\sigma^{2}}\right]\) & 70 & 13 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Distribution function of fatigue endurance limit of AlSi10 Mg.  
Figure 8: Approach to design allowable determination, after [4].  
preparation, P.K., H.M., J.M., P.S., J.M.T.; writing--review and editing, X.W.; supervision, X.W. All authors have read and agreed to the published version of the manuscript.  
The APC was funded by NSERC CRD Grant#:CRDPJ500913-16.  
We would like to acknowledge that his activity related to this study was conducted within the Aeronautical Product Development and Certification Program of the National Research Council Canada.  
The authors declare no conflict of interest.
## 4 Experimental Measurements  
To supplement the simulation results with experimental measurements, we constructed the hybrid CPV module analyzed above, and show the results in Figure 6 and Table 3. We used a solar simulator (WACOM, WXS-450S-65), xenon lamp (6500 W), Fresnel lens (PMMA, 254 \(\times\) 254 \(\times\) 3 mm\({}^{3}\)), GaInP/GaInAs/Ge triple-junction solar cell (Spectrolab, CDO-100-C3 MJ, 10 \(\times\) 10 mm\({}^{2}\)), polycrystalline silicon solar cells (Zhiwang, 80 \(\times\) 55 mm\({}^{2}\)), and a heat sink in the setup for this measurement. Figure 6g shows the I-V and P-V curves of the TJ solar cells and the hybrid CPV module, demonstrating that the pyramidal SOE had the highest maximum output power of 212.8 W/m\({}^{2}\). The hybrid CPV module without an SOE had the next highest power (181.5 W/m\({}^{2}\)), followed by the perpendicular SOE (149.0 W/m\({}^{2}\)). The perpendicular SOE had the lowest \(P_{MAX}\) due to its narrow entrance, but it was expected to act as a homogenizer well. Although these values are slightly lower than the results calculated above, they show a similar relationship. The I-V and P-V curves for the polycrystalline silicon solar cells in Figure 6h demonstrate that the sum of maximum output power for the pyramidal SOE was the lowest at 5.14 W/m\({}^{2}\). The perpendicular SOE and the no SOE cases attained the sum of maximum output power values of 5.67 W/m\({}^{2}\) and 5.99 W/m\({}^{2}\), respectively. From Figure 4, the \(P_{MAX}\) of the polycrystalline silicon solar cells with an 18% conversion efficiency were found to be 1.98 W/m\({}^{2}\), 2.5 W/m\({}^{2}\), 1.96 W/m\({}^{2}\), 1.66 W/m\({}^{2}\), 1.67 W/m\({}^{2}\), and 1.67 W/m\({}^{2}\), respectively. On the other hand, the experimental measurements obtained \(P_{MAX}\) of 0.42 W/m\({}^{2}\), 0.76 W/m\({}^{2}\), 0.4 W/m\({}^{2}\), 0.33 W/m\({}^{2}\)  
Figure 5: Irradiance distributions of the triple-junction and 12 silicon solar cells with three SOE configurations: (**a**,**b**) perpendicular (\(\theta\) = 0\({}^{\circ}\), h = 15 mm), (**c**,**d**) no SOE (h = 0), and (**e**,**f**) pyramidal (\(\theta\) = 30\({}^{\circ}\), h = 15 mm).  
0.32 W/m\({}^{2}\), and 0.33 W/m\({}^{2}\), respectively, which were lower than expected. These values are lower than the simulation results, due to the lower proportion of diffuse light in the measurements using the solar simulator. Since the measured output power values were smaller than those predicted by the simulation, we need to include diffuse light in the experimental measurement setup for modest-DNI conditions in future tests. Overall, the experimental measurements show similar results to those we calculated using the raytracing simulations, and the hybrid CPV module with the optimized SOE design performed better than the other cases we tested.
# Research on Reliability Assessment of Mechanical Equipment Based on the Performance-Feature Model
## 3 Method  
### Performance Degradation Path Modeling and Model Parametric Estimating  
For some obvious traits of performance degradation products, degradation mechanisms are easy to understand, one can use the relationship between the degradation characteristics and time to calculate the reliability of the product. For products with performance degradation characteristics are not obvious, the quantitative relationship of the degradation model cannot be directly expressed, and analysis methods and techniques such as regression analysis are needed. Therefore, the reliability evaluation method based on the degraded path is proposed, as described in Reference [15].  
Firstly, the performance degradation data of the products at different times are collected, which do not have to reach the failure threshold, and the features associated with performance degradation from monitored signals are extracted and used to replace time. Then, the appropriate degradation model according to the performance degradation curve trend is selected. Different distributions, as the alternatives, are chosen to match the relationship between the feature value and performance degradation value. Linear, exponential, and Gaussian distribution are usually chosen as the degradation model, as outlined in Reference [19]. Different distributions will also be discussed depending on the features extracted.  
Linear distribution can be expressed by Equation (1).  
\[f_{Linear}(x)=ax+b \tag{1}\]  
Exponential distribution can be expressed by Equation (2).  
\[f_{Exponent}(x)=a\cdot e^{bx} \tag{2}\]  
Gaussian distribution can be expressed by Equation (3).  
\[f_{Gaussian}(x)=a\cdot e^{\left(-\left(\frac{x-b}{c}\right)^{2}\right)} \tag{3}\]  
where \(a\), \(b\), and \(c\) are estimators of the distribution parameters and are different in different sample modeling. So, the parameters of each performance degradation model are estimated according to the recorded performance degradation data.
# Optimized Design of Flexible Quick-Change System Based on Genetic Algorithm and Monte Carlo Method
## 2 Material and Methods
### Layout Structure Design Optimization of a Flexible Quick-Change Process System
#### 2.2.1 Zero Point Clamping Unit Error Model  
The zero point clamping unit in the flexible quick-change process system uses a SCHUNK supplier's NSE3-138 type with a repetitive positioning accuracy of 0.005 mm for a single zero point clamping unit. Figure 4 depicts the posture deviation error model of the flexible quick-change process system brought on by the zero point clamping unit's manufacturing precision. When the repetitive positioning error of the zero point clamping unit is \(\delta\), the errors in the X, Y, and Z directions are \(\delta\)cos\(\alpha\), \(\delta\)sin\(\alpha\), and \(\delta/2\), respectively. The workpiece's rotation angle deviation around the X and Y axes will be affected by the error in the Z direction and will be approximately equal to L, where L is the distance between the zero point clamping pin and the one-way positioning pin.  
Assume that the positioning pin on the fixture plate and the positioning hole in the base of the zero point clamping unit on the foundation plate of the flexible quick-change process system are out of alignment in both position and posture. The position deviation caused by the zero point clamping unit is then represented by the flexible quick-change process system using the following model [20].  
\[\mathrm{d}_{\mathrm{w}}^{0}=\left[\Delta\mathrm{R}_{\mathrm{w}}^{0},\Delta \mathrm{\theta}_{\mathrm{w}}^{0}\right]^{\mathrm{T}}=\left[\delta\cos\alpha, \delta\sin\alpha,\delta/2,\tan\theta,\tan\theta,0\right]^{\mathrm{T}} \tag{9}\]  
Figure 4: Error model of a flexible quick-change process system.  
Figure 3: Flexible quick-change process system-workpiece and error source. (**a**) Flexible quick-change process systems and workpieces. (**b**) The system error source.  
where \(\mathrm{q}_{\mathrm{w}}^{0}\), \(\mathrm{\Delta R}_{\mathrm{w}}^{0}\), and \(\mathrm{\Delta\theta}_{\mathrm{w}}^{0}\) are the total error, total position deviation, and total attitude deviation caused by the zero point clamping unit, respectively. Additionally, \(\delta\) refers to the repetitive positioning accuracy of the zero point clamping unit; \(\alpha\) indicates the angle with the X-axis; and \(\theta\) indicates the angle deviation of the workpieces around the X- and Y-axes.