esa-sceva/satcom-chunk-collection huggingface dataset:
# Harmonic Detection Method Based on Parameter Optimization VMD-IWT Combined Noise Reduction
## 1 Introduction
However, this algorithm is easily subject to the local optimal solution. And the computational efficiency needs to be further improved. In [18], the grasshopper optimization algorithm (GOA) was used to achieve adaptive selection of VMD parameters. However, the global search ability of the GOA algorithm declined in the late iteration period, resulting in low convergence accuracy of the algorithm. In [19], a whale optimization algorithm (WOA) was applied to optimize VMD decomposition signals. Due to its few control parameters and powerful optimization ability, WOA accurately extracted effective information of each frequency band of signals and achieved good performance. However, the heuristic optimization algorithms mentioned above have some inherent defects in the process of solving problems, such as low convergence accuracy and slow convergence speed, and it is easy to fall into local optimal solutions. In view of the difference in characteristics of signal and noise in different practical application scenarios, the presence of noise may cause the decomposed IMF to no longer purely represent the different frequency components of the signal, but may contain the characteristics of noise [20]. In the case of low SNR, noise components may be amplified in the decomposition, thus confusing the real signal components, and the performance of VMD may be more challenging [21]. Therefore, when the heuristic algorithm optimizes VMD, how to reasonably configure the fitness function in these fields, improve the search efficiency of the algorithm, and avoid falling into the dilemma of local optimization has become a key issue to be solved [22]. Building on the analysis presented, this paper addresses the problem of traditional harmonic detection methods being susceptible to noise interference which reduces their detection accuracy. It introduces a novel approach that combines VMD optimized through parameter tuning with wavelet threshold denoising. This method utilizes the minimal sample entropy of the signal as the objective function for parameter optimization via the prime optimization algorithm (RIME). Once the optimal parameters are identified, the signal undergoes VMD decomposition. Subsequently, the Pearson correlation coefficient method is employed to adaptively distinguish between effective and ineffective components, followed by denoising the effective components using improved wavelet thresholding (IWT). The denoised components are then reconstructed to obtain the final denoised signal. The efficacy and accuracy of the proposed RIME-VMD-IWT method are validated through simulations, real signal measurements, and comparative analyses with other methods.
# Trust and Mobility-Based Protocol for Secure Routing in Internet of Things
## 5 Simulation Study  
The proposed trust and mobility-based routing protocol is tested via simulation study by embedding in RPL routing protocol. This is carried out by using ContikiOS/Cooja simulator with InstantContiki2.7. SMTrust is tested under three scenarios: Scenario I of static nodes, Scenario II of mobile sender nodes, and Scenario III of the mobile sink and sender nodes. The simulation performance of SMTrust and default ContikiRPL, along with existing systems, under routing attacks is determined and analysed for parameters, including node rank changes, packet loss rate, throughput, and power consumption. Figure 5 shows the simulation workflow of our experiments.  
Contiki [67] is an open source, wireless sensor network operating system consisting of a kernel, program loader, and several libraries and processes. Contiki OS is the most commonly used platform [68], which contains distinct modules for different tasks, and all tasks are divided logically into directories. This research work focuses on the routing module, that is ContikiRPL, in Contiki OS. Hence the coding is mainly carried out in the _rpl_ directory, located at..._/contiki/core/net/rpl_, to implement and integrate the proposed trust-based secure routing system. So, the Contiki OS is chosen because it provides enough flexibility of code modification in the required directory and supporting mobility scenarios.  
The implementation of ContikiRPL is according to the specification in RFC 6550 [12]. Two objective functions are implemented in ContikiRPL, Objective Function Zero (OF0) and the Minimum Rank with Hysteresis Objective Function (MRHOF). This research work defines its own objective function, named as SMTrustOF, and manipulates ContikiRPL according to the SMTrust model and process flow as depicted in [29]. In the proposed secure routing protocol, a comparison is made with MRHOF because OF0 is already proved to have lower performance in RPL implementation [69; 70]. Using SMTrustOF, the routing decisions rely on the trust among nodes, thus detecting and isolating the malicious nodes in the network. All nodes calculate trust values for the neighbouring nodes to choose only the trustworthy nodes for routing. The trust values evolve over time according to the weightage assigned to each trust metric.
# Abstract Text Summarization with a Convolutional Seq2 seq Model
## 5 Conclusions  
In this work, we introduce a seq2 seq generative model based on convolutional framework to produce abstract text summarization. Our proposed model can represent a sequence with a hierarchical structure which promotes its efficiency a lot. A copying mechanism is also adopted to further improve the model performance as it can handle the OOV words. Moreover, our model is able to model the keywords and key sentences simultaneously with the hierarchical attention mechanism. We conduct comprehensive experiments on two real-life datasets, and prove the effectiveness and efficiency of our model for generating abstract text summarization.  
**Author Contributions:** [PERSON] realized the conceptualization, formal analysis and put forward a methodology, he also conducted the experiments and wrote the paper; [PERSON] helped data curation and coding; [PERSON] reviewed and edited the paper, also helped results validation; [PERSON] helped with the project administration; [PERSON] was the supervisor and helped the funding acquisition.  
**Funding:** This research was funded by National Nature Science Foundation of China (NSFC) grant number 61872446 and 71690233, and NSF of Hunan province under grant No. 2019 JJ20024.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Model** & **Times** \\ \hline ABS & 3211 \\ ABS+ & 3865 \\ RASELman & 4573 \\ GAN & 6587 \\ BERT & 7432 \\ RNN-Isent & 7753 \\ CNN-Isent & 1681 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The Computation Cost.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Metric** & **Rouge-1** & **Rouge-2** & **Rouge-L** \\ \hline ABS & 35.68 & 16.47 & 34.14 \\ ABS+ & 36.74 & 16.89 & 35.01 \\ RASELman & 39.65 & 18.25 & 37.24 \\ CNN-2 sent-hieco & 41.87 & 19.64 & 39.35 \\ CNN-2 sent-hieco-RBM & * **42.04** & * **19.77** & * **39.42** \\ GAN & 37.87 & 15.71 & 39.20 \\ BERT & 41.71 & 19.49 & 38.79 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The experimental results on CNN/Daily Mail testing datasets. * indicates the statistical significant improvement.
## 1 Introduction
This method uses membership functions to transform input features into the fuzzy domain and reduces the uncertainty of each pixel in the image through uncertainty mapping functions and fuzzy fusion functions, thereby improving the accuracy of the segmentation model. 2. FuzzyTransNet is a new deep learning model that can be applied to polyp and skin segmentation. It accomplishes feature extraction by applying multi-correlated convolution, multi-feature aggregation, and attention mechanisms. Innovatively, it adopts the Swin Transformer V2 network for feature extraction and effectively combines it with ResNet50 through the fusion module. As a result, the network can extract global semantic information without sacrificing underlying contextual features. 3. Experiments are conducted on three skin segmentation benchmark datasets (ISIC2017 and ISIC2018) and polyp segmentation datasets (Kvasir, CVC-ClinicDB, and CVC-ColonDB). Compared with some current advanced algorithms, our proposed method demonstrates advantages in evaluation metrics such as mDice, mIoU, and accuracy. Furthermore, by using class activation maps (Cams) for visualization, the effectiveness of the proposed algorithm is demonstrated more intuitively.
# Blasting Vibration Control and Signal Analysis of Adjacent Existing Deterioration Tunnels
## 4 Field Experiment of Blasting Vibration Control
### Blasting Scheme
#### 4.2.2 Mechanical Cutting Blasting  
The mechanical cutting blasting method refers to using a hydraulic sandblaster to cut an area first (1.2 m \(\times\) 1.2 m \(\times\) 1.6 m). Normal detonators are then used for blasting. It is only necessary to lay the charge number of auxiliary holes and peripheral holes; the circulation footage is 1.2 m, the blasting hole length of auxiliary holes and peripheral holes is 1.4 m, and the hole distance is designed to be 40 cm. The layout and spacing of auxiliary holes and peripheral holes are shown in Figure 12, and the specific design parameters are shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \begin{tabular}{c} **Hole Class** \\ \end{tabular} & \begin{tabular}{c} **Number** \\ **(mm)** \\ \end{tabular} & \begin{tabular}{c} **Hole Diameter** \\ **(mm)** \\ \end{tabular} & \begin{tabular}{c} **Segment** \\ **(m)** \\ \end{tabular} & \
\begin{tabular}{c} **Hole Depth** \\ **Hole (kg)** \\ \end{tabular} &
\begin{tabular}{c} **Charge in a Single** \\ **(kg)** \\ \end{tabular} \\ \hline Cutting hole & 8 & 42 & 1 & 1.6 & 0.9 & 7.2 \\ \hline Auxiliary hole & 10 & 42 & 3 & 1.3 & 0.3 & 3.0 \\ \hline Peripheral hole & 15 & 42 & 7 & 1.3 & 0.3 & 4.5 \\ \hline Bottom hole & 11 & 42 & 11 & 1.3 & 0.3 & 3.3 \\ Total & 74 & & & & 27.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Normal detonator blasting scheme.  
Figure 11: Normal detonator layout. (The numbers represent the segment).  
Figure 10: Site arrangement of measuring points. (**a**) Location of blasting vibrometer in tunnel, (**b**) The setting mode of sensor.
## 2 Materials and Methods
### Data Sources
#### 2.2.1 NPP Data  
The vegetation NPP data were obtained from the NASA EOS/MODIS dataset of MOD17 NPP products for 2001-2020 ([[https://lpdaac.usgs.gov/](https://lpdaac.usgs.gov/)]([https://lpdaac.usgs.gov/](https://lpdaac.usgs.gov/)) (accessed on 7 August 2023)). The temporal resolution is 8 days and annual synthetics, and the spatial resolution is 500 m. MOD17A3 HGF version 6.0 is an improvement of MOD17A3 based on the BIOMEBGC and light energy utilization models [53; 27; 54; 55]. NPP values were simulated using the new Biome specified Parameters Look-Up Table (BPLUT) and the latest version of the Global Modeling and Assimilation Office (GMAO) daily meteorological data, further improving \(NPP\) estimation accuracy. It integrates a light use efficiency logic to estimate the gross primary productivity equation using remotely sensed vegetation information and a maintenance and growth respiration module to derive the \(NPP\) equation.  
\[GPP=LUE_{\text{max}}\times f_{T\text{min}}\times f_{vpd}\times 0.45\times SW_{rad }\times FPAR \tag{1}\]  
\[NPP=GPP-R_{M}-R_{G} \tag{2}\]  
where \(GPP\) denotes the gross primary productivity, \(LUE_{\text{max}}\) is the maximum light use efficiency, regulated by \(f_{T_{\text{min}}}\) and \(f_{vpd}\), to address water stress (\(T_{\text{min}}\)) and vapor pressure deficit (VPD) caused by low temperature. \(SW_{rad}\) stands for the short-wave solar radiation load, 45% of which is for photosynthetic activity. \(FPAR\) represents the percentage of absorbed photosynthetically active radiation. \(R_{M}\) is used by vegetation to maintain its respiration and is estimated from the leaf area index (LAI) [56; 57], climate data, and biome-specific parameters. \(R_{G}\) indicates the energy cost of organic compounds fixed by vegetation for its growth respiration, calculated as a function of the maximum annual LAI. According to the calculation principle, the land types with low vegetation cover lack the conditions to form NPP, and those with vegetation cover were selected as the study object in combination with the land use situation of the Songnen Plain.
## 2 Literature Review  
In many recent studies, including those conducted by the authors, specific parts were automatically detected by combining AI with image-processing technology. For example, the authors have conducted research on detecting slope failure regions from aerial photographs taken during landslides [2], detecting buried pipes from penetrating radar measurements [3], and detecting bridge damage and integrating it into three-dimensional data using Structure from Motion (SMM) [4; 5]. In the field of civil engineering, there have been numerous studies on infrastructure, especially on bridges, many of which involved crack detection. For example, [PERSON] et al. [6] conducted crack detection for concrete bridges with semantic segmentation using an extended convolutional layer called ASPP, and [PERSON] et al. [7; 8] conducted crack detection for concrete structures using LightBGM, which is a machine-learning method, and crack detection for roads using ResNet [9], which is a convolutional neural network (CNN). There have been many other studies on deformation detection in concrete [10; 11; 12; 13; 14], but relatively few studies have been performed on steel damage. Nonetheless, there has been research [15; 16; 17; 18] on the detection of corrosion points in steel using fully convolutional networks [19], including the study of [PERSON] et al. As mentioned above, various methods have been discussed and actively researched for deterioration/damage detection in bridges. However, for headrace tunnels, which were the focus of the present study, effective maintenance and management methods have not been developed, despite their importance, and little research has been performed on the subject [20]. As a method for conducting inspection surveys, [PERSON] et al. constructed a tunnel inspection system that can accurately capture piping facilities that are difficult to check visually, such as those hidden in walls, as visual information by using a Mixed Reality device and a model of the channel shape that is created by attaching a plan view recorded by past inspections to a three-dimensional model generated from design and construction data of the channel tunnel [21]. [PERSON] et al., developed a float-type image-capturing device equipped with a CCD camera for agricultural canal tunnels and detected cracks in the captured images [22]. In addition, they constructed a functional diagnosis system using electromagnetic radar that can be used when water is cut off [23]. [PERSON] et al., conducted a safety risk assessment in tunnels using a robot that can operate in underwater environments [24]. Although inspection and survey systems have been developed, there have been few studies on comprehensive headrace-tunnel maintenance and management systems, e.g., the construction of databases of inspection and survey results and analyses based on the obtained data. Thus, both the image-acquisition method and the analysis method are still in early stages.
## 4 Conclusions  
This paper presents a study concerning the laser in situ alloying of Ti-Al-Nb using the LENS technique. The objectives were to elucidate the effects of niobium on the structure of gamma Ti-48 Al alloy. From the results and discussion of this study, the following was concluded:  
* Niobium increases and decreases the aluminium solubility in the dual (\(\alpha_{2}+\gamma\)) and pure \(\gamma\) phase alloys, respectively.  
* Niobium had a direct microstructural transformation effect on the Ti-48 Al alloy. This microstructural transformation was attributed to the resulting exothermic reaction, which is also known as a self-propagating high-temperature synthesis or combustion synthesis.
* Niobium 6 (at. %) promotes twinning in \(\alpha_{2}\) phase for Ti-48 Al alloy, while anneal twinning in the gamma (\(\gamma\)) phase can be achieved for the sample containing 8 (at. %) niobium content if heat treated to 1400 \({}^{\circ}\)C and oven cooled.
# The Multiband over Spatial Division Multiplexing Sliceable Transceiver for Future Optical Networks
## 1 Introduction
Then, Section 3 presents the MB(oSDM) S-BVT main architecture and key building blocks. Section 4 includes a numerical analysis of the optical signal crosstalk (XT) to evaluate the impact in different bands, such as O-, E-, S-, C-, L-, and U-bands. Then, the proof-of-concept of the proposed transceiver is explained in Section 5. The experimental assessment is performed in Section 6, where MB and MBoSDM transmission systems are evaluated. Additionally, in-band and out-band XT evaluation is also performed. Finally, in Section 7 the conclusions are drawn.
# Quality Assessment of HDR/WCG Images Using HDR Uniform Color Spaces
## Appendix B Performance Indexes
** \\ \hline PU-PSNR & 15.87 & 17.09 & 20.43 & 16.00 & 17.08 \\ PSNR\({}_{HDR\text{-}Lab_{100}}\) & 16.44 & 17.45 & 24.95 & 15.97 & 17.66 \\ PSNR\({}_{HDR\text{-}Lab_{100}}\) & 19.52 & 19.79 & 21.27 & 18.77 & 21.14 \\ PSNR\({}_{ICCp}\) & 15.32 & 16.81 & 20.05 & 15.46 & 15.49 \\ PSNR\({}_{J_{\mu}a_{\mu}b_{\mu}}\) & 18.66 & 20.02 & 20.92 & 18.79 & 21.21 \\ PSNR\({}_{XYZ}\) & 21.80 & 21.24 & 22.36 & 22.31 & 24.58 \\ PSNR\({}_{Y^{\prime}C^{\prime}C\backslash P^{\prime}}\) & 21.38 & 20.42 & 21.79 & 20.23 & 22.75 \\ \hline PU-SSIM & 15.92 & 14.48 & 16.44 & 11.75 & 16.69 \\ SSIM\({}_{HDR\text{-}Lab_{100}}\) & 15.91 & 14.81 & 15.73 & 11.86 & 17.78 \\ SSIM\({}_{HDR\text{-}Lab_{1000}}\) & 17.70 & 17.08 & 16.12 & 13.52 & 22.41 \\ SSIM\({}_{ICICP}\) & 15.91 & 15.16 & 16.43 & 11.54 & 15.28 \\ SSIM\({}_{J_{\mu}a_{\mu}b_{\mu}}\) & 16.70 & 17.14 & 15.13 & 12.46 & 21.20 \\ SSIM\({}_{XYZ}\) & 21.32 & 21.79 & 19.80 & 19.65 & 25.95 \\ SSIM\({}_{Y^{\prime}C^{\prime}C\backslash P^{\prime}}\) & 20.89 & 20.49 & 18.63 & 16.88 & 25.11 \\ \hline PU-MS-SSIM & 11.76 & 12.73 & 12.15 & 8.43 & 10.73 \\ MS-SSIM\({}_{HDR\text{-}Lab_{100}}\) & 11.86 & 12.62 & 13.46 & 8.71 & 11.26 \\ MS-SSIM\({}_{HDR\text{-}Lab_{1000}}\) & 13.62 & 15.03 & 12.49 & 8.12 & 19.18 \\ MS-SSIM\({}_{ICICP}\) & 12.10 & 13.33 & 11.73 & 9.04 & 10.53 \\ MS-SSIM\({}_{J_{\mu}a_{\mu}b_{\mu}}\) & 12.25 & 14.11 & 10.11 & 7.69 & 17.88 \\ MS-SSIM\({}_{XYZ}\) & 19.95 & 21.23 & 19.65 & 15.88 & 23.17 \\ MS-SSIM\({}_{Y^{\prime}C^{\prime}C\backslash P^{\prime}}\) & 18.66 & 19.61 & 17.74 & 12.81 & 22.71 \\ \hline PU-FSIM & 9.67 & 14.84 & 12.07 & 9.11 & 13.02 \\ FSIM\({}_{HDR\text{-}Lab_{100}}\) & 9.89 & 14.70 & 12.29 & 9.39 & 12.18 \\ FSIM\({}_{HDR\text{-}Lab_{1000}}\) & 11.98 & 12.50 & 14.41 & 7.19 & 12.09 \\ FSIM\({}_{ICICP}\) & 9.70 & 15.70 & 12.60 & 10.05 & 13.64 \\ FSIM\({}_{J_{\mu}a_{\mu}b_{\mu}}\) & 10.42 & 10.94 & 13.39 & 7.31 & 12.58 \\ FSIM\({}_{XYZ}\) & 18.04 & 18.71 & 19.01 & 13.55 & 19.97 \\ FSIM\({}_{Y^{\prime}C^{\prime}C\backslash P^{\prime}}\) & 15.72 & 16.37 & 16.79 & 11.60 & 17.22 \\ \hline PU-PSNR-HVS-M & 12.80 & 16.75 & 19.95 & 15.39 & 12.19 \\ PSNR-HVS-M\({}_{HDR\text{-}Lab_{100}}\) & 12.70 & 16.53 & 20.23 & 12.14 & 14.20 \\ PSNR-HVS-M\({}_{HDR\text{-}Lab_{1000}}\) & 16.52 & 19.54 & 20.36 & 11.88 & 15.21 \\ PSNR-HVS-M\({}_{IJ_{\mu}a_{\mu}b_{\mu}}\) & 12.74 & 17.45 & 19.60 & 11.54 & 11.45 \\ PSNR-HVS-M\({}_{J_{\mu}a_{\mu}b_{\mu}}\) & 15.12 & 20.75 & 20.48 & 12.85 & 16.52 \\ PSNR-HVS-M\({}_{XYZ}\) & 21.15 & 21.46 & 22.44 & 18.36 & 22.01 \\ PSNR-HVS-M\({}_{Y^{\prime}C^{\prime}C\backslash P^{\prime}}\) & 20.12 & 20.95 & 21.77 & 13.83 & 18.15 \\ \hline PU-PSNR-HMA & 12.55 & 17.07 & 16.28 & 10.99 & 13.60 \\ PSNR-HMA\({}_{HDR\text{-}Lab_{100}}\) & 12.92 & 17.70 & 16.21 & 11.10 & 14.10 \\ PSNR-HMA\({}_{HDR\text{-}Lab_{1000}}\) & 16.51 & 18.20 & 17.40 & 10.96 & 15.62 \\ PSNR-HMA\({}_{ICICP}\) & 12.00 & 16.0
## 1 Introduction
This mainly involves improving the average electric field enhancement factor and enhancing bistability performance. Specifically, we propose a metal-dielectric hybrid metasurface for enhanced optical bistability, achieving the switch thresholds of \(I_{\text{ON-OFF}}\) and \(I_{\text{OFF-ON}}\) of as low as 8.5 MW/cm\({}^{2}\) and 7.1 MW/cm\({}^{2}\), respectively. The MAS resonances supported by the metasurface are manipulated by the PEC mirror effect and asymmetrical optimization, and the electric field is significantly enhanced and localized within the Si nanostrip, which is exactly the Kerr medium. This paper is organized as follows. In Materials and Methods, the enhancement of the proposed metasurface is demonstrated, and the materials, mechanism, and process are elucidated. In Section 3, the bistable performance of the proposed metasurface and the influence of the incident angle of light on the threshold are investigated and discussed. Finally, conclusions are drawn in Section 4.
###### Abstract  
As a kind of common phenomenon in practical engineering, misalignment error considerably changes the working performance of bearing. However, it has rarely attracted attention in the study of duplex angular contact ball bearings (DACBBs). To evaluate the influence of misalignment on DACBB, an analytical model of DACBB considering combined loads and angular misalignment is established for back-to-back, face-to-face, and tandem configurations. According to the geometrical relationship inside the bearing, the deformations caused by external load and angular misalignment are systematically analyzed. On this basis, the effects of external load, axial preload, and angular misalignment on the displacement, maximum contact load, contact load distribution, contact angle, and fatigue life of DACBB are analyzed. The results demonstrate the high dependence of the contact load and contact angle on the above factors. Angular misalignment causes the fluctuation of contact load and contact angle distributions along the azimuth angle of a rolling ball. Nonlinear relationships between fatigue life of DACBB, angular misalignment, and axial preload are obtained. The occurrence of angular misalignment considerably decreases the fatigue life of DACBB. The results suggest the necessity of angular misalignment analysis for DACBB.  
angular contact ball bearing; angular misalignment; fatigue life; contact load; contact angle