esa-sceva/satcom-chunk-collection huggingface dataset:
# A Real-Time Numerical Decoupling Method for Multi-DoF Magnetic Levitation Rotary Table
## 1 Introduction
In general, for large-stroke magnetic levitation positioning systems, the following three methods are often considered:  
1. Harmonic analysis method by solving Maxwell equations [27; 28]. 2. Look-up table or curving fitting model using the measurements and results from finite-element method (FEM) software [29]. 3. Numerical method based on the basic law of electromagnetism [30; 31; 32]. The former two methods are mainstream in the literature because they are suitable for real-time computation, but their disadvantages are obvious. The harmonic analytical model cannot concurrently consider all motions and rotations, so large-range multi-DoF motion is hard to achieve. The look-up table and fitting model are built on the obtained force and torque dates, so generality is bad, and the multiaxis motion increases the stored data amount and decreases the accuracy of curving fitting. Furthermore, the numerical method is an alternative with its inherent generality, but it needs an advanced computation engineer to improve computation speed [33]. For a maglev system with a definite structure, a reasonable modeling method should be selected and studied. In this work, a magnetic levitation rotary actuator with a novel topology structure is proposed that is able to synchronously realize translation and rotation. In order to model the electromagnetic actuator with both the translational and rotational position taken into account, magnetic force and torque are solved via the numerical method, as the magnetic charge model solves the magnetic flux density, and the Gaussian quadrature calculates the Lorenz integral. The magnetic field computation is also reasonably simplified to decrease the total computation burden. Additionally, because the proposed wrench model is required to realize motion decoupling, the real-time solution is realized via the field programmable gate array (FPGA) with a pipelined computation architecture. The computation delay for each sampling point is lower enough for the typical control system with 5 kHz control frequency. In the experiment section, real-time solutions are given on an oscilloscope and stored in an SD card. Modeling accuracy is evaluated due to a unified boundary element method (BEM) program package. Because the selected sampling points are located in a large region, the proposed numerical decoupling method is proved to be available to decouple the multiaxis motion in centimeters and amn unlimited rotating range, which is suitable for micromachining CNC devices used in the mesoscale.
# Experimental Study on Mechanical Properties of Anisotropic Slate under Different Water Contents
## 4 Analysis of Anisotropic Properties of Slate Parameters
### Compressive Strength Anisotropy
Based on the \(R^{2}\) value, the data suggest that, aside from the relatively dispersed test data of dry slate leading to moderate prediction accuracy, the uniaxial and triaxial prediction results for the slate in natural and saturated states from Equation (5) align well with the experimental outcomes. This indicates the formula's suitability for describing uniaxial and triaxial compressive strength. \begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{\(\beta\)} & \multirow{2}{*}{\(\sigma_{3}\)/MPa} & **Compressive Strength of Dried Samples/MPa** & **Compressive Strength of Natural Samples/MPa** \\  & & **Sampling/MPa** & **Sampling/MPa** & **Sampling/MPa** \\ \hline  & 0 & 134.12 & 102.50 & 84.46 \\  & 5 & 168.30 & 125.01 & 96.37 \\
0\({}^{\circ}\) & 10 & 173.90 & 14  
Based on the research in the literature [28; 29], the degree of anisotropy in compressive strength for this study was defined as follows:  
\[R_{c}=\frac{\sigma_{\text{cmax}}}{\sigma_{\text{cmin}}} \tag{6}\]  
where \(R_{c}\) is the degree of anisotropy of compressive strength. [PERSON] [30] believes that \(R_{c}\) is the ratio of the uniaxial compressive strength of the rock at \(\beta\) = 90\({}^{\circ}\) to its minimum at other bedding angles. In contrast, in this study, the maximum uniaxial compressive strength appears in both 0\({}^{\circ}\) and 90\({}^{\circ}\) samples, so \(R_{c}\) should be defined as the ratio of the maximum value to the minimum value of uniaxial compressive strength at a bedding angle of 0\({}^{\circ}\)-90\({}^{\circ}\). Therefore, \(\sigma_{\text{cmax}}\) and \(\sigma_{\text{cmin}}\) are the maximum and minimum compressive strength values under the same water content and confining pressure. \(R_{\text{cg}}\), \(R_{\text{cz}}\), and \(R_{\text{cb}}\) are the anisotropy degrees in dry, natural, and saturated states. Figure 11 indicates that the fracture joints along the bedding direction in slate become compacted under confining pressure, diminishing the displayed anisotropy. Thus, as confining pressure increases, the anisotropy degree gradually decreases. Under varying water contents, the order of anisotropy degrees is \(R_{\text{cb}}\) > \(R_{\text{cg}}\) > \(R_{\text{cz}}\). This phenomenon may stem from the increased likelihood of water infiltration along fractures and pores between rock beddings in a saturated state, leading to the dissolution of some soluble minerals, an increase in microfractures, and pore enlargement, thereby enhancing the slate's anisotropic properties. In line with the classification standard of anisotropy degrees for compressive strength of rock materials, the anisotropy degree of slate's compressive strength in this study ranges from 2 to 5, indicating medium to high anisotropy. Figure 10: Compressive strength of slate at different bedding angles: (**a**) dry state, (**b**) natural state, and (**c**) saturated state.
## 3 Methodology
### Accuracy Assessment  
In this study, the forest fire burned area detection based on machine learning models was conducted by classifying into three classes, i.e., forest, burned area, and non-forest.  
However, the purpose of this study is to detect the burned area. Therefore, two classes (forest and non-forest) were combined and expressed as a binary classification image (i.e., burned area and unburned area) for an intuitive understanding of the areas affected by the forest fire burned area. Classification performance was then assessed using five accuracy evaluation metrics: Overall Accuracy (OA), recall, precision, F1-score, and kappa. The OA represents the ratio of the number of correctly predicted samples among all samples (Equation (3)). The recall, also known as sensitivity, is calculated in Equation (4). It represents the ratio between the predicted true value and the actual true value. The  
Figure 4: Visualization of sample points extraction for each class. (**a**) Sample points extraction of Site A, (**b**) Sample point extraction of Site B.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scheme** & **Num. Spectral Bands** & **Num. Indices** & **Total** \\ \hline \multirow{3}{*}{Scheme 1} & 9 images & 2 images & 11 variables \\  & (post fire image, 9 bands) & (PostNDVI, PostNBR) & \\ \cline{1-1}  & 18 images & - & 18 variables \\ \cline{1-1}  & (pre and post fire images, 9 bands each) & 6 images & \\ \cline{1-1}  & 18 images & (PreNDVI, PreNBR, PostNDVI, PostNBR, & 24 variables \\ \cline{1-1}  & (pre and post fire images, 9 bands each) & dNDVI, dNBR) & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Components of dataset by scheme.  
precision (Equation (5)) can be used to assess the accuracy of the recall value because there is a trade-off relationship between these two accuracy assessment metrics. Even though all three metrics are useful, there are limitations to effectively representing the algorithm's performance under the unbalanced dataset. For this reason, quantitative analysis was conducted using both F1-Score and Kappa, which are accuracy evaluation metrics that can sufficiently maintain reliability in an unbalanced dataset. F1-Score is the harmonic average of recall and precision values (Equation (6)), and [PERSON]'s Kappa can evaluate the degree of agreement between the classification results and reference data (Equation (7)).  
\[OA=\frac{TP+TN}{TP+TN+FP+FN} \tag{3}\]  
where _TP, TN, FP_, and _FN_ stand for true positive, true negative, false positive, and false negative, respectively.  
\[Recall=\frac{TP}{TP+FN} \tag{4}\]  
\[Precision=\frac{TP}{TP+FP} \tag{5}\]  
\[F1\ Score=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{6}\]  
\[Kappa=\frac{P_{0}-P_{E}}{1-P_{e}}\]  
\[P_{0}=\frac{TP+TN}{TP+TN+FP+FN}=\text{OA} \tag{7}\]  
\[P_{e}=\frac{(TP+FN)\times(TP+FP)}{(TP+TN+FP+FN)^{2}}+\frac{(FN+TN)\times(FP+ TN)}{(TP+TN+FP+FN)^{2}}\]
## 4 Conclusions  
We theoretically and numerically investigated the entangled photon-pair generation in five kinds of non-poled KTP isomorphs: KTP, RTP, RTA, KTA, and CTA. The SPDC properties under Type II EPM were theoretically investigated in the mm2 biaxial crystal to which the KTP isomorphs belong, including PM, GV matchings, effective nonlinearities, and spatial walk-offs between the interacting photons. The heralded-state spectral purities of SPDC output were quantified via JSA analyses, and the results show that the purities calculated for Type II EPM with a proper pump filtering are as high as 0.995-0.997. The spectral position of photon-pairs produced by KTP isomorphs can be chosen over the broad range of 1883.8-2068.1 nm, which is still within the silica fiber transparency. Therefore, the spectral position of the photon pair can be selectively determined or tuned in the range of interest while maintaining the high spectral purity of the photon pairs generated via Type II EPM. In addition, the use of non-poled crystals is expected to increase the SPDC efficiency by 29% to 77% compared to the PPKTP. Since KTP isomorphs have been commercialized as high-level crystal growth techniques, quantum light sources based on non-poled KTP isomorphs have good potential for fiber-optic and free-space quantum communication and sensing.  
Conceptualization, K.J.L.; Simulation, D.L.; writing--original draft preparation, I.K.; writing--review and editing, K.J.L.; supervision, K.J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Research Foundation of Korea (Grant NRF-2019R1F1A1063937) and by the Korea Institute of Science and Technology (KIST) (Grant 2E29580-19-147).  
Not applicable.  
The data presented in this study are available in this article.  
The authors declare no conflict of interest.
## 2 Methodology
### Satellite Structures and Self-Shadow Impact  
The BDS-3 satellite can be simplified as a box-shaped body structure with integrated solar panels. It was pointed out that the body of the CAST satellite is T-shaped [19]. Nonetheless, we inferred that this T-shaped structure is not evident based on the released surface area as well as the length, width, and height of the satellite body. Consequently, we simplified it as a rectangular structure and overlooked the resulting self-shadowing. The optical parameters of BDS-3 MEO satellites are listed in Table 1[18].  
In addition to the self-shadowing of the T-shaped structure, six BDS-3 MEO satellites are equipped with the SAR antenna to provide international SAR service [21]. These satellites are listed in the table as satellites labeled as type B. The SAR panel increases the illuminated area of the satellite \(\pm\)Z panels and changes the SRP acceleration. Moreover, the satellite body would shadow the surface of the SAR antenna when the \(\beta\) angle is large (refer to Figure 1). To clarify whether SAR has an impact on the SRP modeling, this paper considers the self-shadowing of the SAR antenna.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sat. Type** & **Sat.** & **Panel** & **Area (m\({}^{2}\))** & **\(\mathbf{\alpha+\delta}\)** & \(\mathbf{\rho}\) \\ \hline \multirow{4}{*}{CAST-A} & C19, C20, C21, C22, C23, C24, C36, C37, C41, C42 & +Z & 2.59 & 0.920 & 0.080 \\ \cline{3-6}  & & \(-\)Z & 2.59 & 0.350 & 0.650 \\ \hline \multirow{4}{*}{CAST-B} & C32, C33, C45, C46 & +X & 1.25 & 0.350 & 0.650 \\ \cline{2-6}  & & +Z & 2.59 & 0.920 & 0.080 \\ \cline{3-6}  & & \(-\)Z & 2.59 & 0.350 & 0.650 \\ \hline \multirow{4}{*}{SECM-A} & C25, C26, C27, C28, C29, C30, C34, C35 & +X & 1.25 & 0.200 & 0.800 \\ \cline{3-6}  & & +Z & 2.59 & 0.200 & 0.800 \\ \cline{3-6}  & & \(-\)Z & 2.59 & 0.200 & 0.800 \\ \hline \multirow{4}{*}{SECM-B} & C43, C44 & +X & 1.24 & 0.200 & 0.800 \\ \cline{3-6}  & & +Z & 2.57 & 0.200 & 0.800 \\ \cline{1-1} \cline{2-6}  & & \(-\)Z & 2.57 & 0.200 & 0.800 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geometric and optical information of BDS-3 MEO satellites.  
Following the normal yaw mode, only the \(\pm\)Z and +X panels of the satellite are irradiated. It can be inferred from Figure 1 that the +Z side of the SAR antenna would not be shaded by the satellite body, while the \(-\)Z side may be shaded by the satellite body. When the sunlight incidence angle satisfies the conditions (\(\alpha>21.45^{\circ}\)), the \(-\)Z side of the SAR antenna is completely shaded. The increased irradiated area at any moment is computed as  
\[\left\{\begin{array}{l}A_{\mathrm{SAR},+Z}=1.105\\ A_{\mathrm{SAR},-Z}=1.105-2.16\cdot tan\alpha\end{array}\right. \tag{9}\]  
where \(\alpha\) is the angle between the +Z direction and the Sun-satellite direction. For the SECM-B satellites, the size and location of the SAR antenna are not publicly available. Therefore, the satellite body was just regarded as a rectangular structure, and the effects of the SAR antenna would be reduced by adjusting the optical parameters.
## 4 Conclusions  
We empirically analysed the evolving trend of the Ganges-Padma River by compiling a time series of satellite pictures to record the historical morphological changes. Bangladesh is home to numerous rivers, including the mighty Ganges-Padma. The sinuosity of the Ganges-Padma River has grown throughout time. The sinuosity ratio of bank lines increased from 1.31 in 1973 to 1.33 that same year. In 1996, the sinuosity ratio was raised from 1.33 to 1.37. The sinuosity ratio rose from 1.37 in 1973 to 1.43 in 2006 and 1.47 in 2016 when compared to the corresponding values in 1973 and 1996. As expected, the outcome  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Year** & **Total Length of Bars (Km)** & **Length of Rivers (Km)** & **Braiding Index** \\ \hline
1973 & 136.08 & 209.18 & 1.3 \\
1984 & 150.74 & 209.72 & 1.43 \\
1996 & 169.11 & 208.63 & 1.62 \\
2006 & 200.81 & 208.98 & 1.92 \\
2016 & 225.41 & 209.10 & 2.15 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Measurement of Braiding Index of Ganges-Padma River during 1973–2016.  
demonstrates a rise in the braiding index with passing years. In comparison to 1973 and 1984, when the number of bars was reduced, the length of the bars was raised. However, in 1984, the braiding index was raised from 1.3 to 1.43. The total number of bars has increased since 1973 and 1984 but dropped since 1996. In 1973, 1984, and 1996, the bar lengths were extended. However, the index of braiding rose after 1973, 1984, and 1996. In 1973, the braiding index for the Ganges-Padma River was 1.3, which means it was not braided at that time. As of 1984, it was 1.43, which likewise indicated that it was non-braided, but subsequent decades showed a growth in the braiding index, which is now greater than 1.5. The braiding index was 1.62 in 1996, 1.92 in 2006, and 2.15 in 2016. The braiding index is more than the cut-off value of 1.5 at this point. Since the river is expected to be braided if the braiding index is more than 1.5, we may safely assume that the river is braided at the present time.  
Conceptualisation, A.Y.; Methodology and Analysis, M.W. and A.Y.; Writing--Original Draft Preparation, M.W.; Writing--Review and Editing, M.W. and A.Y. All authors have read and agreed to the published version of the manuscript. This research was funded by Nanjing University of Information Science & Technology Start-Up fund. Not applicable. Not applicable. Not applicable. Data Availability Statement: Data will be provided upon the reasonable request from the corresponding author. The authors would like to acknowledge [PERSON] for his guidance. The authors declare no conflict of interest.
### Current Issues  
Stakeholders in the maritime industry have agreed to develop MASSs--the epitome of advanced technologies--and expand their commercial operations. However, they are concerned about the risks of infringing on the rights and benefits of individuals or organizations, related to data transactions [4]. Thus, to prepare for an era of commercially operated autonomous vessels, we must first review the existence and protection of ownership, providing a stable trading framework that can be roughly summarized as follows: \"raw data \(\rightarrow\) information \(\rightarrow\) knowledge \(\rightarrow\) convergence of content on heterogeneous information = data of a higher integrated concept\".  
The US and European maritime industries anticipate an increase in the economic value of the data required for commercially operating autonomous vessels and managing smart ports. Recently, [PERSON] et al. identified six research fields and considerations, as well as directions of improvement relating to technological issues [5]. Similarly, Korea has focused on developing innovative technologies that reduce the costs related to autonomous vessels. Overall, the focus has been on developing the MASS technology rather than on the legal ramifications of the smart technologies on which it relies. Thus, it is necessary to conduct an adequate legal review of the foundation to ensure the stability of data collection and transactions. Therefore, this study examined the concept, transaction structure, and application scope of data activating digital platforms to support data transactions and aid in advancing the new business sector for commercial operations of MASSs.  
This study focused on the following research questions. First, what types of maritime data and concepts are produced by a MASS? Second, what is the structure of the maritime data transactions required for the commercial operation of an autonomous vessel? Third, what is the legal framework relative to MASS data, and the right to ownership that is necessary to ensure the stability of an autonomous vessel's commercial operation? Fourth, how can the rights to maritime data ownership for the commercial operation of an autonomous vessel be protected?
## 5 Key 6G Technologies  
To effectively accommodate groundbreaking use cases and applications, the development and implementation of advanced transmission, networking, and computing technologies will be essential for the 6G system. This section offers an exhaustive overview of potential technological drivers for 6G, organized into various categories: innovative spectrum technologies including THz communications, mmWave, optical wireless communication (OWC), visible light communication (VLC), and dynamic spectrum management (DSM); novel networking approaches such as software-based networking and virtualization, open radio access network (O-RAN), network slicing in radio access network (RAN), and post-quantum cybersecurity; new air interfaces featuring intelligent reflecting surfaces (IRSs), coordinated multi-point (CoMP), extensive MIMO, fresh modulation strategies and cell-free massive MIMO; revolutionary architecture ensuring three-dimensional coverage through the integration of expansive satellite constellations, HAPs, and UAVs with conventional terrestrial networks; and a new approach fostered by merging communication, computation, and storage capabilities, along with incorporating AI, blockchain, digital twins, and mobile networks. For each technology identified, its principles, benefits, challenges, and areas requiring further research are discussed [25].
# Speech Enhancement Algorithms: A Systematic Literature Review
## 3 Method  
The field of speech enhancement is of great interest due to its highly multidisciplinary nature and its relevance to many human-centric applications, including medical applications. Therefore, in this review we implement the idea of building a body of evidence and information on various SEA approaches. The survey conducted in this paper is divided into several stages, with each stage including specific elements of planning, conducting, and reporting to form the methodological framework. This systematic literature review is conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol [44; 45], which is useful for visualizing the relationship between the stages of the literature review process. Figure 3 presents the PRISMA flowchart, which details the outcomes at each stage of the systematic review and visually summarizes the screening process while improving the quality and transparency of the SEA literature. First, the number of identified papers from the electronic database is recorded. The selec  
Figure 2: Degradation and **denoising of a speech** signal.  
tion process of the systematic review is then carried out transparently by reporting the decisions made at each stage, including the number of studies. Reasons for including or excluding papers at the full-text stage are documented. The PRISMA flowchart, shown in Figure 3, enhances the reliability and reproducibility of reviews by enabling researchers to understand and evaluate the methodology and findings. The workflow of the methodology is initiated by identifying research questions, followed by formulating and outlining the search strategy and developing criteria for the inclusion or exclusion of studies. Lastly, the procedures for quality assessment and data extraction are implemented. Data are extracted to obtain essential information from the selected papers included in the review. In this review, the extraction process was performed independently by two reviewers to ensure greater accuracy and reduce bias. All stages of the review are detailed in the following sections.  
This work reviews the technologies used for speech enhancement processing. It explores the existing methods and techniques currently used in the field in addition to the potential challenges and limitations, providing insights about how methods for enhancing the quality of degraded speech can be improved. The following sections present the methodological steps and resources used to conduct this systematic review in comprehensive detail.  
Figure 3: PRISMA flowchart for study selection.