esa-sceva/satcom-chunk-collection huggingface dataset:
# A Phase Correction Model for Fourier Transform Spectroscopy
## 4 All-Pass Filter and PCM-APF Model
### Filter Parameter Optimization Strategy
#### 4.3.3 Gold-SA and the GS-SABO Algorithm  
Similiar to SABO, the Gold-SA is also a meta-heuristic algorithm; it was proposed by [PERSON] in 2017 [16]. The design of the algorithm is inspired by the sine function in mathematics; the algorithm utilizes the sine function in mathematics to perform a computational iterative optimization search; its advantages are fast convergence, good robustness, ease of implementation, and having fewer parameters and operators to regulate.  
Based on the relationship between the sine function and the unit circle, Gold-SA can traverse all the values on the sine function, i.e., search for all the points on the unit circle, and at the same time, it introduces the golden section number in its position updating process to narrow down the space of the solution, so as to scan the area that may only produce good results, which largely improves the search speed and makes the search and development reach a good balance.  
For Gold-SA, its individual position update formula is  
\[X_{i}^{new}=X_{i}|\sin(r_{1})|+r_{2}\sin(r_{1})|x_{1}X_{best}-x_{2}X_{i}| \tag{16}\]  
where \(r_{1}\) and \(r_{2}\) are random numbers, \(r_{1}\in[0,2\pi]\) and \(r_{2}\in[0,\pi]\); \(x_{1}\) and \(x_{2}\) are coefficients obtained by introducing the golden section number:  
\[\begin{array}{l}x_{1}=a(1-\tau)+b\tau\\ x_{2}=a\tau+b(1-\tau)\end{array} \tag{17}\]  
The initial values of \(a\) and \(b\) are \(-\pi\) and \(\pi\), and \(\tau=(\sqrt{5}-1)/2\). Subsequently, the values of \(a\), \(b\), \(x_{1}\), and \(x_{2}\) vary with the target value. The scheme of parameter modification is detailed in [16].  
Since SABO does not utilize the global optimum at each iteration but updates the current individual using the positions of all individuals, it is easy to fall into a local optimum solution when the initialized individuals are poorly positioned, which we also verified by algorithmic replication. In order to solve this drawback, we introduce Gold-SA fused with SABO as a novel algorithm called GS-SABO: if the individual adaptation value under the current iteration does not change, the individual position is updated using Gold-SA. This will not increase the computation of the fitness value too much, but also helps the SABO algorithm to jump out the local optimal solution by using the advantage of Gold-SA in the global optimization search. The flowchart of GS-SABO is shown in Figure 5.
## 4 Experimental Results
### E-Rank Analysis  
In Figure 9, the graph illustrates the E-Rank values for the selected LWSCs. Salsa exhibits the highest E-Rank, with a notable lead over Sosemanuk. Contrarily, Grain-v1 displays the lowest E-Rank, with Mickey and Trivium following behind. Grain-128a shows a relatively moderate E-Rank, positioning it in the middle tier of overall software performance among the selected LWSCs.  
Figure 8: Energy consumed per frame by the selected LWSCs.  
Figure 6: Memory footprint of selected LWSCs.  
Figure 7: Code size of selected LWSCs.
## 6 Conclusions  
A novel inspection system for screen printing mobile phone back glass (MPBG) was proposed in this paper. High-quality images of MPBG were captured based on CBF and LABF line-scanning imaging systems. A modified segmentation deep convolutional network was constructed to detect the surface defects of MPBG. The network structure is partly symmetric. The performance of the proposed method was achieved by training from a glass surface defect dataset (GSDD), which was composed of 34,550 image samples. Verified by the test experiment, the average precision and recall of all kinds of defects are respectively more than 91% and 95%. The performance of the proposed method is significantly better than that of the traditional method. This paper demonstrated that the performance of the inspection system can satisfy the requirements of defect detection of a specific task (MPBG), and the system also shows great potential for other surface inspection tasks without much modification. For the future work, we will focus on achieving a good detection performance with less defect samples and on improving the computational efficiency. Meanwhile, how to annotate defect images with a higher precision and efficiency is also our destination.  
Conceptualization, Y.Y., P.C., and J.J.; methodology, J.J.; software, J.J., Z.L.; validation, J.J., P.C. and W.L.; formal analysis, J.J.; investigation, W.L.; resources, P.C.; data curation, Z.L.; writing--original draft preparation, J.J.; writing--review and editing, Y.Y.; visualization, P.C.; supervision, Y.Y.; project administration, P.C.; funding acquisition, Y.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by National Natural Science Foundation of China (NSFC, 61627825, 61875173).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Defect Type** & **Average Precision** & **Recall** & **Average Recall** \\ \hline \multirow{3}{*}{Traditional method} & Dent & & 87.8\% & \\  & Scratch & 85.2\% & 91.0\% & 90.7\% \\  & Discoloration & & 92.9\% & \\ \hline \multirow{3}{*}{Proposed deep learning method} & Dent & & 93.1\% & \\  & Scratch & 91.8\% & 95.5\% & 95.3\% \\ \cline{1-1}  & Discoloration & & 97.0\% & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Precision and recall results of the traditional machine vision method and proposed deep learning method.  
**Acknowledgments:** This work was supported by the State Key Laboratory of Modern Optical Instrumentation of Zhejiang University and Zernike Optics Co., Ltd.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Data Preprocessing for PARAFAC and CNN  
The pre-processing of 3D-EEMs data includes two parts. The first part is to process the original 3D-EEMs data to be suitable for the PARAFAC analysis model's construction, and the second part is to convert 3D-EEMs data into grayscale images for training and testing of the convolution neural network (CNN) model.  
Since these four kinds of data are obtained from different regions and measured with different instruments under different conditions and parameters, they have different excitation and emission wavelength ranges, as well as different excitation and emission intervals. Therefore, it is necessary to adjust the excitation wavelength range and emission wavelength range of the data and normalize the data by using the Raman normalization method [27]. The Pandas and Numpy tools in the Python toolkits were used in this study to unify the excitation spectra interval to 200-500 nm and the emission spectra interval to 250-610 nm (The reason is that this interval contains the common interval of all different datasets to ensure that the fluorescence characteristics of all samples are not lost). In addition, Raman normalization of the data after the interval unification is required. The reference formula for Raman normalization information is as follows:  
\[A_{rp}^{\lambda_{ex}}=\int_{\lambda_{em}^{1}}^{\lambda_{em}^{2}}I_{\lambda_{em} }d\lambda_{em} \tag{1}\]  
\[F_{\lambda_{ex},\lambda_{em}}(R.U.)=\frac{I_{\lambda_{ex},\lambda_{em}}(A.U.)} {A_{rp}} \tag{2}\]  
In the formula, \(I_{\lambda}\) is the spectral correction intensity of the Raman peak at the emission wavelength \(\lambda\). In this paper, \(A_{rp}\) is the integral value of the fluorescence intensity of emission ranges from 365-430 nm (excitation = 350 nm). \(F\) is the fluorescence intensity after Raman normalization. \(A.U.\) stands for the arbitrary unit, \(R.U.\) stands for Raman unit.  
The pre-processed 3D fluorescence data can be used for PARAFAC analysis, while the processed data and the results of the PARAFAC analysis are normalized and converted into grayscale images, as is shown in the following Figure 2.  
Figure 2: (**a**) Corrected data by Raman normalization; (**b**) Adjust the excitation ranges to: 200–550 nm, emission: 250–610 nm; (**c**) FU components (PARAFAC results); (**d**) F components; (**e**) P component; (**f**) PU components; (**g**) Grayscale image of 3D-EEMs components; and (**h**) Original 3D-EEMs grayscale image. This figure shows the data processing method in this paper, in which the grayscale images are generated by the samples EEMs and components EEMs, rather than the contour image, and pixels represent fluorescence intensity. In (**b**), the reason why the range is set as this range is to preserve the characteristic information of all 3D-EEMs. By using the bilinear interpolation method to interpolate 3D-EEMs, the size of the gray image is 60 \(\times\) 60.
# Broadband Flexible Microstrip Antenna Array with Conformal Load-Bearing Structure
## 4 Analysis of Simulation and Measurement Results
### Antenna Element  
The structure photographs of the fabricated antenna element are presented in Figure 11. Observed in Figure 12, the measured reflection coefficient results of the proposed antenna element are 11.56-13.95 GHz, 11.53-14.04 GHz, 11.58-13.94 GHz, and 11.60-14.01 GHz in flat and various bending with 30\({}^{\circ}\), 60\({}^{\circ}\), and 90\({}^{\circ}\) in the _xoz_ plane, respectively. As can be seen in Figure 13, the SLL is reduced by 13.4 dB in E-plane and 13.6 dB in H-plane under the reflection surface loaded, respectively. Figure 14 displays the simulated and measured gain radiation patterns and cross-polarization radiation patterns of the antenna element at 12.5 GHz. Whether in a flat or under a different bending state, the radiation direction of the proposed antenna element can be maintained as stable with low cross-polarization and their maximum radiation directions are kept in the direction perpendicular to the horizontal plane of the antenna. Its performance characteristics in flat state and under different states are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \(W_{1}\) & \(W_{2}\) & \(W_{3}\) & \(W_{4}\) & \(W_{5}\) & \(L_{1}\) & \(L_{2}\) & \(L_{3}\) \\ \hline
14.52 & 7.04 & 0.51 & 0.64 & 32.84 & 14.52 & 6.9 & 6.51 \\ \hline \(L_{4}\) & \(L_{5}\) & \(C_{1}\) & \(h_{1}\) & \(h_{2}\) & \(\varepsilon_{1}\) & \(\varepsilon_{2}\) & \(d_{1}\) \\
8.4 & 36.65 & 1.83 & 0.3 & 2 & 3.5 & 4 & 18.32 \\ \hline \end{tabular}
\end{table}
Table 1: Optimized dimensions of CLAS antenna (unit: mm).  
Figure 10: Various bending modal behaviors of the top substrate, radiation patch, and honeycomb equivalent layer, (**a**) bending states, and (**b**) modal significance.  
Figure 11: The photograph of the proposed antenna element: (**a**) top view, (**b**) top layer with radiation patch, (**c**) the ground with coupling aperture on top of the middle substrate layer, (**d**) microstrip feeding line on the back of middle layer, and (**e**) metallic reflection surface on the back surface of the lower substrate layer.  
Figure 12: Simulated and measured reflection coefficient results of proposed antenna element under various bending states and on the plane. The inset illustrated the bending state.  
Figure 13: The simulated radiation pattern of antenna elements with the comparison of the reflection surface loaded both before and after.
## 4 Experimental Study  
In this study, a comparative experiment was conducted to evaluate the performance of panoramic image segmentation in indoor construction environments. Three different methods were assessed, as follows: (1) using Panoplane360 alone for segmentation, (2) applying a single execution of SAM followed by label assignment from Panoplane360, and (3) implementing iterative SAM execution with filtering algorithms, after which labels were assigned using Panoplane360. To conduct this evaluation, a dataset was constructed using panoramic images collected from indoor construction sites in South Korea, and the segmentation results were analyzed using both quantitative and qualitative evaluation metrics. This experiment aims to demonstrate that the proposed method can achieve more precise object boundary segmentation and reliable label assignment, even in complex and dynamic indoor construction environments.
## 3 Results, Case Studies, and Applications
### Case Study: Public Sector and E-Governance Application  
In our case study, we will evaluate the effectiveness of the proposed RAG pipelines and conversational agents. All the results can be reproduced, as presented in the Python notebook example. In the proposed User Interface (UI) framework, the user can select a pipeline and model. For convenience, we will present the results of the conversational pipeline with agents since each tool's output is shown; however, the reasoning process of the virtual assistant is also examined in detail to provide a definitive answer.  
The questions asked for the presented case study are \"What is the Green Deal?\" and \"What is the EU4 DigitalUA?\". The results are only based on the retrieved documents that were indexed based on the sources. To leverage documents in the memory, we uploaded a document [35] that was highly correlated specifically with the Green Deal but not with the EU4 DigitalUA project. Therefore, the Q&A process with detailed reasoning processes and agents' usage is presented in Figures 3 and 4.  
With the below, a comprehensive reasoning process is overviewed since we can identify the selected tool in each step, the tool input, which can change (see Press Corner compared with the rest of the tools), the observation, which details the answer of each tool in each stage, and the thought process that the conversational virtual assistant follows.  
Moreover, in the second example related to EU4 DigitalUA (as presented in Figure 4), we can observe the Agents' behavior when all data sources do not cover the query topic. This example highlights the agents' ability to comprehend and retrieve relevant information from available sources (Press Corner and Web Search), even when the Research repository and in-memory documents do not contain additional details.
## 3 Model and Problem
### CCAA Models  
We show CCAA models in Figure 1. As can be seen, CCAAs consist of some concentric circular rings, and each ring contains a various number of antennas. Moreover, we assume that each antenna element of CCAA is radiation isotropic. In addition, all the antenna elements of CCAA are controlled by a centralized process and these antennas are synchronized in terms of time, frequency, and initial phase. Furthermore, all the antenna elements are fixed and the relative positions cannot be changed.  
In this case, the electromagnetic waves generated by all the array elements will be precisely superimposed at the receiver and offset in some directions. Intuitively, these antennas will generate a beam pattern that contains a mainlobe and some sidelobes [21]. Mathematically, we can use the array factor as the index to show the signal strength of the CCAA system. In CCAAs, the array factor is a complex function of the excitation current weights of array elements of CCAAs, which is shown as follows [22].  
\[F(\theta,\phi)=\sum_{m=1}^{M}\sum_{n=1}^{N_{m}}I_{mn}\cdot e^{[kr_{m}\sin( \theta)\cos(\phi-\phi_{mn})]+\gamma_{mn}}, \tag{1}\]  
where \(M\) is the number of concentric rings. Moreover, \(N_{m}\) is the number of antennas of the \(m\)th concentric rings, where \(m=1,2,\ldots,M\). Note that we use \(N_{T}\) to denote the total number of antennas in the CCAA, which can be calculated as \(N_{L}=\sum_{m=1}^{M}N_{m}\). In addition, \(k=2\pi/\lambda\)m, where \(\lambda\) is the wavelength. Additionally, \(r_{m}\) is the radius of the \(m\)th circle with the value of \(N_{m}d_{m}/2\pi\), wherein \(d_{m}\) is the spacing between the elements on the \(m\)th concentric rings. Furthermore, \(\theta\) and \(\phi\) are the zenith and azimuth angles, \(\theta_{0}\) and \(\varphi_{0}\) are the directions of the mainlobe, and \(\phi_{mn}\) is the central angle of the \(n\)th antenna element of the \(m\)th concentric rings. The most important parameters of Equation (1) are \(I_{mn}\) and \(\gamma_{mn}\), where \(I_{mn}\) is the excitation current weight of \(n\)th antenna element of the \(m\)th circle and \(\gamma_{mn}\) is the initial phase of \(n\)th antenna element of the \(m\)th circle. We assume that the initial phase of all the antennas are synchronized, thus \(\gamma_{mn}\) can be calculated as follows.  
\[\gamma_{mn}=-k\gamma_{m}\sin(\theta_{0})\cos(\varphi_{0}-\varphi_{mn}). \tag{2}\]
## Materials and Methods
### Image Processing and Spectral Data Extraction  
On the basis of the calibrated images, due to the distinctive spectral difference between the meat sample and background, the meat portion can be easily separated from the background. In this work, the entire meat portion was isolated by subtracting the image at a wavelength of 405 nm (low reflectance value) from that at a wavelength of 700 nm (high reflectance value), followed by setting a constant value of 0.25 as the threshold. With these procedures, a mask that excluded the background and the edge of the Petri dishes to avoid any undesired spectral information was produced. From the mask, ROI was identified and average reflectance spectrum was extracted by calculating the mean spectral data of all the pixels within the ROI. These treatments were repeated in all the images to obtain the spectrum representing each sample. The ENVI software (ENVI 5.1, Research Systems Inc., Solutions, Boulder, CO, USA, 2014) was adopted to carry out the image preprocessing and spectra extraction. The reflectance spectra were transformed into A and KM units according to Equations (1) and (2), respectively.  
\[\mathrm{A} =\mathrm{log}\ (\mathrm{1/R}) \tag{1}\] \[\mathrm{KM} =(\mathrm{1-R^{2}})/2\mathrm{R} \tag{2}\]  
Figure 1: Chemical structure of K-carrageenanan.  
where R is the reflectance spectra, A is the absorbance spectra, and KM is the Kubelka-Munck spectra.
###### Abstract
Moreover, accessing information related to fine dust monitoring systems is difficult for the public. Since the current monitoring system is a centralized system, there is no responsibility to share information on fine dust sensors at construction sites, and manipulation of the information provided to the public may occur [13; 18]. Therefore, it is difficult to remotely read the fine dust measurements at construction sites and determine whether the contractor carried out the fine dust reduction measures. The problems of real-time monitoring of fine dust in current construction sites through IoT can be summarized into two main categories. First, due to the centralized server, fine dust concentration information is hidden, and the information provided by the system can be tampered with. Second, a specific method to verify whether fine dust reduction measures are implemented is needed. Therefore, this study presents a blockchain-based network concept model for the development of a reliable and accessible fine dust management system at construction sites. Because blockchain can fundamentally prevent data forgery and share information through a distributed ledger technology and decentralization, it is a suitable solution for the current monitoring system. The proposed method stores fine dust concentration data and pictures of fine dust reduction measures in the blockchain. Users can freely participate in the fine dust management network and transparently view the workflow information. Blockchain and decentralization are expected to solve the information imbalance problem and form a trust protocol.
# Hierarchical Synchronization Control Strategy of Active Rear Axle Independent Steering System
###### Abstract  
The synchronization error of the left and right steering-wheel-angles and the disturbances rejection of the synchronization controller are of great significance for the active rear axle independent steering (ARIS) system under complex driving conditions and uncertain disturbances. In order to reduce synchronization error, a novel hierarchical synchronization control strategy based on virtual synchronization control and linear active disturbance rejection control (LADRC) is proposed. The upper controller adopts the virtual synchronization controller based on the dynamic model of the virtual rear axle steering mechanism to reduce the synchronization error between the rear wheel steering angles of the ARIS system; the lower controller is designed based on an LADRC algorithm to realize an accurate tracking control of the steering angle for each wheels. Experiments based on a prototype vehicle are conducted to prove that the proposed hierarchical synchronization control strategy for the ARIS system can improve the control accuracy significantly and has the properties of better disturbances rejection and stronger robustness.  
active rear axle independent steering (ARIS) system; hierarchical synchronization control; virtual coupling control; linear active disturbance rejection control (LADRC) +
Footnote †: journal: Applied Sciences
## 4 Discussion
### The Influence of Immersion Time in Mine Water on the Mechanical Properties of Coal Fracture
Concurrently, the pores transition from smaller to larger sizes (micropores \(\rightarrow\) small pores \(\rightarrow\) mesopores \(\rightarrow\) large pores) [17], while the connections and cementation between particles weaken. Furthermore, the ability of coal samples to resist deformation diminishes, the energy required for deformation and failure decreases, and the overall strength of the coal samples declines. For soft and fissured coal bodies, there exists a critical moisture content. When the moisture content is below this critical threshold, the strength of the coal increases with rising moisture content; however, when the moisture content exceeds this critical level, the strength decreases with further increases in moisture [50]. The coal samples used in this experiment are relatively weak, and the minimum immersion time in mine water is 30 days, significantly exceeding the critical duration. Additionally, compared to pure water, mine water contains a higher concentration of ions and various components, resulting in complex chemical reactions and interactions between the weak coal and mine water. Consequently, as the immersion time in mine water increases, the mechanical properties of the coal samples gradually deteriorate. Notably, studies [51; 52] have demonstrated that the fracture toughness of rocks decreases after exposure to high temperatures and carbonation corrosion. Scanning electron microscopy (SEM) observations corroborated the accumulation of pores and cracks, as well as the formation of fractured structures. In this experiment with coal, similar fragmentation structures were also identified. These findings indicate that both immersion in mine water and the effects of high temperature may relate to changes in the material's skeleton, driven by the evolution of pores and cracks, thereby affecting the macroscopic mechanical properties of the material.