esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Mean Field Game (MFG) Framework
### Complexity Analysis  
In this work, the mean field represents a statistical distribution characterizing the state of a two-dimensional system at a specific time. It quantifies the likelihood of various states across the player set, as indicated by (13). The computational challenge lies in determining this mean field, which entails assessing the probability of \(S\) states across \(K\) players at any given time, \(t\in[0,T]\). The summation in the equation encompasses all players, indexed by \(i\), with \(K\) representing the number of players. Hence, the computational complexity scales proportionally with the player count. Additionally, for each time point \(t\), the mean field computation is necessary, leading to a scaling complexity with the number of time instances, \(T\). Moreover, as the mean field is computed for each achievable state \(s\) within the set \(\mathcal{S}\), the total possible states, \(S\), further contribute to the complexity. Taking these factors into consideration, the computational complexity of mean field determination, integrated into LEACH to formulate MFG-LEACH, can be represented as \(\mathcal{O}(K\cdot T\cdot S)\). This complexity arises from the necessity to iterate through all players, time instances, and states.
# Magnetic Nanomaterials for Magnetically-Aided Drug Delivery and Hyperthermia
## 9 Drug Delivery
By modifying the drugs with antibodies, proteins, or ligands, it is possible to improve the drug delivery _in vitro_. Chitosan coated Fe\({}_{2}\)O\({}_{3}\) magnetic nanoparticles were suggested by [PERSON] and co-workers [89]. These nanoparticles are safe to transport to the targeted organs and became one of the most useful tools in drug delivery [89]. [PERSON] and co-workers prepared magnetic targeting chitosan nanoparticles and were used as a drug delivery system [90]. This nanoparticle based drug delivery system was targeting photodynamic therapy [90]. In photodynamic therapy, a tumor localizing photosensitizing agent would be administered and activated by light of a particular wavelength [91]. In the study carried out by the [PERSON] and co-workers, they investigated the drug delivery and magnetic resonance imaging (MRI) properties of novel oleic acid-coated iron-oxide and pluronic-stabilized magnetic nanoparticles [92]. It has been recorded that the drug loading efficiency for doxorubicin and paclitaxel in magnetic nanoparticles was 74-95% [92]. The combination of drugs incorporated in magnetic nanoparticles showed a high antiproliferative activity in MCF-7 breast cancer cells [92]. [PERSON] and co-workers reported the development of a methotrexate-immobilized iron oxide nanoparticle drug carrier which is biostable, and that may be used for real-time monitoring of drug delivery through magnetic resonance imaging [93]. Methotrexate was attached to the nanoparticle via a poly(ethylene glycol) self-assembled monolayer. Cytotoxicity was studied by using 9L glioma cells. Experiments showed that the uptake of methotrexate attached nanoparticles by glioma cells higher compared to the control nanoparticles [93].
# Hyperspectral Molecular Orientation Mapping in Metamaterials
For IR astronomy, they are based on detection of absorption and scattering anisotropy induced by large volumes of low density materials with strongly expressed polarisation effects [14], while for the Earth observation, reflection/scattering from the surface is monitored for absorption and polarisation changes. By combining several wavelengths, more precise understanding about size and shape of scattered light can be gained. In a related area of remote sensing, IR imaging at the chemical finger printing region demonstrated strong polarisation dependence of reflected/scattered IR tunable laser radiation [15] and could be used for determination of material morphology and alignment on the surface and sub-surface down to \(\sim\)10-20 \(\upmu\)m (industrial, defence, environmental applications). In material science, molecular alignment as well as pattern formation (different mass density, out-off plane structures) can be detected beyond the spatial resolution as demonstrated for the IR spectral range [16]. This shows that the alignment of absorbers could be detected from a volume with a lateral cross section smaller than the diffraction limit of the optics used and even smaller than the wavelength. To detect absorbance changes from nanoscale volumes and films thinner than 100 nm is a challenge [17] but a light field enhancement by localised plasmonic resonance could be harnessed. This hypothesis was tested in this study and extended to anisotropy detection using light enhancement in metal-insulator-metal (MIM) metamaterials. Previously, silk micro-tome slices of \(\sim\)1 \(\upmu\)m thickness were mapped using IR transmission spectroscopy [18]; however, thinner polymers are challenging to characterise since thickness becomes \(\sim\)\(\lambda\)/10-\(\lambda\)/100. Here, we demonstrate a capability to detect anisotropy in dielectric nanofilms sandwiched in a metal-insulator-metal (MIM) metasurface (Figure 1a inset), engineered for plasmonic response over the IR spectral range of 4-20 \(\upmu\)m. The plasmonic resonance wavelength is tuned over this IR window by changing the diameter of the gold nanodisk pattern. The light field enhancement under the nanodisk couples with insulator (polyimide) IR absorption bands. The four polarisation method [19] was applied to demonstrate the capability to detect molecular alignment, if any, down to the IR wavelength scale (from sample areas of \(\sim\)6 \(\upmu\)m cross sections per pixel on the hyperspectral chemical image map). The top pattern (nanodisks layer) of the MIM is chosen to be isotropic with a triangular lattice arrangement. Therefore, the absorbance anisotropy of the polyimide nanolayer can be probed, rather than the nanodisk response. Nanofilms of polyimide are known to have anisotropy and where chosen for this study.
## 2 Related Work
### Non-Deep Learning Methods  
Edge-based segmentation methods: [PERSON] et al. [18] proposed an edge detection technique to fit line segments by calculating gradients and detecting changes in the direction of the unit average vector on the surface, while [PERSON] et al. [19] is based on grouping of scan lines to perform fast segmentation. The edge-based methods are faster, but accuracy is not guaranteed because the edge method is not very sensitive to inhomogeneous or sparse point clouds.  
Region-based segmentation: In the process of point clouds segmentation, when using a region-based segmentation method, it is necessary to classify the points with similar properties adjacent to each other, so that different segmentation regions of the point clouds can be obtained. The seed region approach was proposed initially by [PERSON] et al. [20], and when using this bottom-up segmentation method, it is mainly seed-based. The region segmentation is done by selecting multiple seed points. [PERSON] et al. [21] used a non-seed region approach to guide the process of planar clustering regions to reconstruct the complete shape of the building.  
Attribute-based segmentation method: The attributes of point clouds data are used as footholds for segmentation, and this method usually has better robustness. [PERSON] et al. [22] proposed a segmentation method based on feature space clustering. [PERSON] et al. proposed a road boundary enhancement point-column network BE-PCFCN, which uses point columns to extract point clouds features directly and integrates a road enhancement module to achieve accurate unstructured road segmentation [23]. [PERSON] et al. proposed a super voxel point clouds segmentation algorithm based on the principle of region growth [24]. [PERSON] et al. proposed a rod-shape segmentation method based on geometric structure constraints to segment road streetlights and other features [25]. [PERSON] et al. proposed a new super voxel segmentation algorithm framework for enhancing road boundaries from 3D point clouds to achieve road segmentation [26]. [PERSON] et al. [27] proposed a method for automatically extracting road lane markers based on fuzzy inference based on point attributes associated with MLS point clouds. In traditional non-deep learning methods, manual features require much prior knowledge, and the segmentation effect of MLS road point clouds data is not ideal.
## 2 Methodology
### Model of RTK Measurement
This classification helps in data quality control by filtering out unreliable observations, thereby enhancing the accuracy and continuity of the RTK positioning solution. This mechanism ensures that only high-quality data, free from significant errors or slips, is used in the positioning calculations. Following the data-checking method, we determined whether there was a correlation between the inter-epoch variation in the pseudorange and carrier phase and the Doppler measurements to assess the consistency of the observations. The following is a detailed description of the steps involved in the process:  
\[\left\{ \begin{array}{l}|D_{t+1}-D_{t}|<k_{3}\\ \left|(\phi_{t+1}-\phi_{t})+\overline{D}_{t+1,t}\cdot\Delta t\right|<k_{4}\\ \left|(P_{t+1}-P_{t})+\lambda\cdot\overline{D}_{t+1,t}\cdot\Delta t\right|<k_{5 }\\ \overline{D}_{t+1,t}=\frac{D_{t+1}+D_{t}}{2}\end{array} \right. \tag{6}\]  
where \(D\) represents the Doppler measurements in this context. Equation (6) also denotes the average Doppler of the adjacent epochs. In the consistency check, we first assessed the reliability by examining the Doppler epoch difference. It is reliable if this difference is less than 100 cycles per second. Next, we compared the carrier-phase epoch difference with the average Doppler. If the difference between the two is less than (\(k_{4}\) = 2.5 _cycles_), it indicates the absence of a significant cycle slip. We also verified the pseudorange against the threshold (\(k_{5}\) = \(\lambda\cdot k_{3}\)). The threshold values used in Equation (6) were empirically determined based on extensive testing and data analysis to ensure reliable performance in various conditions.
## 5 Case Study: An Actual Deployment at the City Campus of the University of Auckland
### Application Prospects Based on Case Study Insights
#### 5.4.2 Public Safety and Security  
This case study demonstrates that the system's real-time sound classification capabilities have promising public safety and security applications. The system could be adapted to detect anomalous or dangerous sounds, such as gunshots, alarms, or breaking glass, in high-risk areas, allowing for quicker emergency responses. Additionally, during large public gatherings or events, such as concerts or festivals, the system could monitor crowd noise levels to ensure safety and detect disturbances or potential hazards. This real-time monitoring would be particularly useful in urban environments where swift responses to public safety threats are critical.
## 3 System Model and Problem Modeling
### Problem Modeling  
Let \(\mathbf{x_{u}}=(x_{i,u}(t):\forall u\in\mathcal{U},i\in\mathcal{I},t\in \mathcal{T})\), \(\mathbf{x_{b}}=(x_{i,b}(t):\forall b\in\mathcal{B},i\in\mathcal{I},t\in \mathcal{T})\), and \(\mathbf{P}(t)=(P_{i}:\forall i\in\mathcal{I},t\in\mathcal{T})\), where \(\mathcal{U}\), \(\mathcal{B}\), \(\mathcal{I}\), and \(\mathcal{T}\) denote the sets of UAVs, ground 5G base stations, PDs, and network operation time, respectively. The objective of this paper is to maximize the total long-term system energy efficiency and jointly optimize the PDassociation strategy (\(\mathbf{x}_{\text{u}}\) and \(\mathbf{x}_{\text{b}}\)) and the power of PDs (\(\mathbf{P}\)) within each time slot, so that the optimization problem can be expressed as  
\[\mathbf{P1:} \max_{\mathbf{x}_{u},\mathbf{x}_{\text{b}},\mathbf{P}}U_{EE}\] (9) s.t. \[\qquad C_{1}\sim C_{7}.\]  
\[C_{1}:x_{i,u}(t)\in\{0,1\},\quad\forall u\in\mathcal{U},i\in \mathcal{I},t\in\mathcal{T},\] \[C_{2}:x_{i,b}(t)\in\{0,1\},\quad\forall b\in\mathcal{B},i\in \mathcal{I},t\in\mathcal{T},\] \[C_{3}:x_{i,u}(t)+x_{i,b}(t)\leq 1,\quad\forall u\in\mathcal{U},b \in\mathcal{B},i\in\mathcal{I},t\in\mathcal{T},\] \[C_{4}:\sum_{i=1}^{I}x_{i,u}(t)\leq l_{U},\quad\forall u\in \mathcal{U},i\in\mathcal{I},t\in\mathcal{T},\] \[C_{5}:\sum_{i=1}^{I}x_{i,b}(t)\leq I_{B},\quad\forall b\in \mathcal{B},i\in\mathcal{I},t\in\mathcal{T},\] \[C_{6}:0\leq P_{t}(t)\leq P_{\max},\quad\forall i\in\mathcal{I},t \in\mathcal{T},\] \[C_{7}:R_{i}(t)\geq R_{th},\quad\forall i\in\mathcal{I},t\in \mathcal{T}.\]  
\(C_{1}\) and \(C_{2}\) denote that the PD association strategy is a binary strategy. \(C_{3}\) indicates that at most one data transmission way is selected for each PD at each time slot, i.e., either the data are forwarded directly to the ground 5G base station or the data are uploaded to the UAV and then forwarded to the LEO satellite. \(C_{4}\) and \(C_{5}\) represent that there is an upper limit on the number of PDs associated to the UAV or ground 5G base station per time slot, \(l_{U}\) or \(I_{B}\), respectively. \(C_{6}\) shows that the transmission power of each PD is limited by the maximum power \(P_{\max}\). \(C_{7}\) means that the transmission rate of each PD needs to meet the minimum transmission rate requirement.
## 3 Simulations of System Measurement Method in Noise Conditions  
### Analysis of The Statistical Characteristics of CRN  
The OTDR trace fluctuation originates from the fading noise of Rayleigh scattering [21; 22; 23; 24], in which CRN is a very important component that cannot be eliminated. The statistical characteristics of the OTDR trace in Figure 2 are analyzed. Figure 3a shows the trace fitting results (the red line) from the original trace (the blue line), and by the subtraction between the original trace and the fitting trace, the fluctuation with average value \(\mu\) = 0.004 dB and standard deviation \(\sigma\) = 1.06 dB are obtained, as shown in Figure 3b. The probability density function of the CRN in Figure 3b is shown in Figure 4, which is very similar to the standard normal (Gaussian) distribution. It also can be inferred that at the same position in the sensing fiber, the power of Rayleigh scattering from frequency sweeping approximately obeys standard normal distribution. So, the Gaussian noise function can be adopted to simulate the 2D Rayleigh scattering spectra at each position along the sensing fiber.  
Figure 3: Statistical analysis of CRN in OTDR trace: (**a**) trace fitting; (**b**) mean and standard deviation of the CRN.
## 2 Gain Narrowing Pre-Compensating Method  
For the multi-order triple-wavelength WP method, because the Ti:sapphire crystal gain peak is around 800 nm, 795 nm was chosen as a half WP, and two wavelengths (for example, ~760 nm and ~820 nm) further away from 795 nm were chosen as full WPs. Firstly, we assumed that the input light was in a vertical polarization and that the propagation direction of the input beam was normal to the WP surface. Then, it went through a Glan laser polarizer, which allowed the pure vertical polarization to pass. Secondly, through calculation, the differently optimized thickness was adjusted to realize the WP as a triple-wavelength WP. Finally, after the Glan laser polarizer, the transmittance was calculated for different optimized thicknesses. All the calculations were based on fundamental WP theory and the index refraction of quartz [20,21]:  
\[d_{opt}=\frac{\left(m+\frac{1}{2}\right)\lambda_{795 nm}}{n_{e}-n_{o}}=\frac{n \lambda_{\sim 760 nm}}{n_{e}-n_{o}}=\frac{k\lambda_{\sim 820 nm}}{n_{e}-n_{o}}, \tag{1}\]  
\[T=1-\cos^{2}2\varphi\sin^{2}\frac{\pi d_{opt}}{\lambda(n_{e}-n_{o})}, \tag{2}\]  
where \(d_{opt}\) is the optimized quartz thickness, \(m\), \(n\) and \(k\) are all integers, \(n_{e}\) and \(n_{o}\) are refractive indices of the fast and slow axes, respectively, \(T\) is the transmittance and \(\varphi\) is the rotation angle between the input beam polarization and the fast axis of the WP.  
Using Equation (1), the calculated optimized quartz thicknesses were 1.117 mm, 1.027 mm, 0.938 mm, 0.849 mm, 0.759 mm, 0.67 mm, 0.58 mm, 0.491 mm, 0.402 mm and 0.312 mm, separately. When the thickness was more than 1.117 mm or less than 0.312 mm, we still could find some optimized thickness. With those optimized quartzes, the wavelength of 795 nm was a half waveplate, and two wavelengths further from 985 nm were both full waveplates. From Equation (2), we calculated the transmittance spectra of the input light with different optimized thicknesses and a rotation angle of 45\({}^{\circ}\), as shown in Figure 1. It indicates that, as the WP thickness decreases, the two wavelengths as full WPs are further away from 795 nm, and the bandwidth of the attenuation curve becomes broader.  
For a WP with a thickness of 1.117 mm, when the WP is rotated to different angles, the transmittance spectra are shown in Figure 2. It shows that, as the rotation angle becomes larger, the attenuation depth of the attenuation curve increases. In addition, we also calculated the transmittance spectra for different thicknesses of WPs around 1.117 mm and with a rotation angle of 45\({}^{\circ}\), as shown in Figure 3. Although the thickness of the WP only had a small increase, the central wavelength of the attenuation curve shifted to a longer wavelength. In order to reduce the beam pointing fluctuation effect and improve spectra stability, the WP was placed at the exit of the oscillator.
## 7 KAN-Based Tool Wear Modeling Using Simulated Turning Test Data  
### Design of Simulated Turning Tests Using DEFORM-3D  
To further investigate the modeling capabilities of KAN-A/B/C on tool wear, we conducted additional simulation turning tests using DEFORM-3D software (version 11.0). Compared with ABAQUS and ANSYS ([[https://www.ansys.com/](https://www.ansys.com/)]([https://www.ansys.com/](https://www.ansys.com/))), DEFORM-3D offers distinct advantages, including a comprehensive material library, rational mesh generation, and efficient data acquisition. The turning simulation was performed using TNMA332 tools with key parameters configured, as shown in Figure 8.  
AISI-1045 steel was selected as the workpiece material due to its excellent machinability, weldability, and broad applicability across mechanical manufacturing, automotive, and aerospace industries. Its physical and mechanical properties are presented in Table 10.  
The cutting parameters were set as follows: a cutting speed of 250 mm/s and a depth of cut of 0.5 mm along the lathe's Y-axis. The Johnson-Cook constitutive model was employed to characterize the workpiece material's dynamic behavior, while the normalized C&L fracture criterion served as the separation criterion. A shear friction model with a coefficient of 0.45 was applied to the tool-workpiece interface.  
After completing the simulated turning tests, the data on tool wear progression were obtained. Only the data samples corresponding to each observed change in tool wear were retained, as these points better represent the interaction characteristics between the tool and workpiece. For instance, during the stable wear phase, the wear amount of the cutting edge remains nearly constant, with minimal impact on part machining accuracy and surface quality.  
This data processing approach helps verify whether the proposed method can effectively learn wear progression patterns. After processing, 350 samples were obtained and divided equally into training and test sets for model development.  
The acquired data samples were further categorized into distinct features, with their definitions and explanations provided in Table 11.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Definition and Description** & **Category** \\ \hline \multirow{3}{*}{Step} & Time step (the number of times processing time and parameters are recorded) & \\ \hline Time (s) & Cumulative machining time & \\ \hline \multirow{2}{*}{Temperature (\({}^{\circ}\)C)} & Temperature at the chip-tool contact area during turning & \\ \hline \multirow{2}{*}{X/Y/Z-load (N)} & Cutting forces acting on the tool in the X/Y/Z-axis directions & \\ \hline \multirow{2}{*}{X/Y/Z-speed (mm/s)} & Movement speed of the tool in the X/Y/Z directions & \\ \hline \multirow{2}{*}{Wear depth (\(\upmu\)m)} & Tool wear depth & Modeling Target \\ \hline \hline \end{tabular}
\end{table}
Table 11: Features and descriptions of simulated turning data samples.  
Figure 11: Temperature variations in the tool–workpiece contact zone.
## 1 Introduction
Singular value decomposition as a data processing method has been successfully applied to signal noise reduction processing and has proven to be effective [16]. The key to noise reduction using the SVD method is to select the optimal noise reduction order [17]. For selecting the noise reduction order, commonly used methods include the trial-and-error method and the threshold method. Both these methods rely on user experience and lack theoretical basis. In order to rationally select the noise reduction order, a judgment basis needs to be defined. [PERSON] [18] and others used the principle of the unilateral maximum value of the singular value difference spectrum to determine the noise reduction order. [PERSON] [19] proposed a singular kurtosis difference spectrum method to determine the effective reconstruction order for signal noise reduction. [PERSON] [20] and others proposed an SVD noise reduction method using the singular value difference spectrum as a criterion, which achieved better results. [PERSON] et al. [21], and [PERSON] et al. [22] separately made some improvements on its basis, proposed the concept of energy difference spectrum, and normalized it with second-order moments of singular values. The methods above have achieved some results on certain feature sets, but the adaptability of the noise reduction methods is poor. In this paper, we take the difference between the singular value energy difference spectrum and the singular value difference spectrum as the discrimination threshold to avoid threshold screening errors caused by sudden changes in a certain order of singular values due to strong noise. In order to achieve better noise reduction and an accurate extraction of fault features, this paper combines the advantages of the improved CEEMDAN and SVD methods to study the vibration signals of combined harvester assembly quality problems Firstly, the vibration signal is decomposed using the principle of improved CEEMDAN. Then, the SVD algorithm is applied to denoise and reconstruct each order of IMF obtained from the improved CEEMDAN decomposition. This further separates the noise from the fault signal. Finally, the noise reduction effect and performance of this method are validated.