esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Workpiece Fisturing Solutions for CNC Machining  
CNC machining requires precise and rigid workpiece fixture systems to ensure machining accuracy and surface quality [19]. Machining accuracy is directly influenced by the stability and precision of the workpiece's fixation, as any relative movement between the tool and the workpiece can lead to dimensional errors and form deviations [20]. The rigidity of the fixturing system is also important to withstand the cutting forces generated during the machining process, preventing vibrations and ensuring stable cutting [21].  
There is a variety of fixturing devices used in CNC machining, each with specific advantages and disadvantages. The choice of the appropriate fixturing device depends on a series of factors, including the workpiece's geometry, material, machining tolerances, and production volume [22].  
Fixturing devices can include vises, clamping plates, chucks, modular fixtures, and dedicated fixtures. Vises are general-purpose devices, suitable for clamping workpieces with simple shapes [23]. They offer a quick and flexible solution for clamping prismatic workpieces but may have limitations in terms of rigidity for heavy machining [24]. Clamping plates offer a larger fixturing surface and can be used for larger or more complex parts. These allow for fixing workpieces using clamping elements, such as screws, nuts, or clamps, providing increased flexibility in workpiece arrangement [25]. Chucks are mainly used for clamping cylindrical workpieces. They ensure high concentricity and are essential for turning and grinding operations.  
Modular fixtures offer flexibility and can be assembled in different configurations to adapt to various workpiece shapes [26]. These are built from standardized elements that can be combined to create complex fixtures, reducing design and manufacturing time and costs. Dedicated fixtures are designed specifically for a particular workpiece or machining operation and offer maximum precision and rigidity. Although they are more expensive and require design time, they are justified for high-volume production or high-precision machining [27].  
The design of fixturing devices is an essential aspect of the CNC machining process [28]. Correct design can significantly improve machining efficiency, reduce costs, and ensure part quality. Important factors to consider include the shape and dimensions of the workpiece, cutting forces, machining tolerances, and tool accessibility [29]. Cutting force analysis is essential to determine the forces and moments to which the fixture will be subjected, thus ensuring secure and stable clamping [30]. Tool accessibility must also be considered to allow the necessary machining operations without collisions or interference.  
For complex geometries, the design of fixturing devices can be a significant challenge, often requiring innovative solutions. Computer-aided design (CAD) and finite element analysis (FEA) methods are increasingly used to optimize the design of fixturing devices and to ensure their performance [31; 32].
# Adaptive Cruise Control for Intelligent City Bus Based on Vehicle Mass and Road Slope Estimation
## 2 Model of Intelligent City Bus
### Vehicle Longitudinal Dynamics  
When the bus is on a sloped road as shown in Figure 4, its longitudinal dynamic equation can be described as below:  
\[m\dot{v}=F_{t}-F_{air}-F_{slope}-F_{u} \tag{5}\]  
where \(m\), \(v\),\(F_{t}\), \(F_{air}\), \(F_{slope}\), and \(F_{\mu}\) are the bus mass, the longitudinal speed, the driving force acting on the wheels, the air resistance, the ramp resistance, and the rolling resistance, respectively. Therefore Equation (5) can be expanded as follows:  
\[m\dot{v}=\frac{T_{EM}\dot{i_{0}}\dot{i_{g}}\eta}{r}-\frac{1}{2}C_{D}A\rho v^{2} -mgf_{r}\cos\theta-mg\sin\theta \tag{6}\]  
where \(i_{0}\) is the ratio of finial drive, \(i_{g}\) is the ratio of AMT, \(r\) is the wheel radius, \(\eta\) is the transmission efficiency, \(C_{D}\) is the air resistance coefficient, \(A\) is the frontal area of the bus, \(\rho\) is the air density (25 \({}^{\circ}\)C), \(g\) is the acceleration of gravity, \(f_{r}\) is rolling resistance coefficient, and \(\theta\) is the road slope.  
Figure 4: The schematic diagram when the bus is running on ramp.
## 4 Indoor Dynamic Beam Deflection Measurement Experiment and Validation
### Cantilever Vertical Beam Vibration Experiment Setup  
In the indoor full-field dynamic deflection experiment, a Basler a2A5320-7 gmBAS industrial camera was used to measure the displacement of a 100 cm long cantilever vertical beam, and the location of the beam and the camera as well as the geometric size and connection are shown Figure 6a. The experimental equipment and the structure on site are shown in Figure 6b, and the equipment types and parameters are listed in Table 1. Vibration was stimulated by excitation (lateral impact with a hammer) at the top of the steel cantilever beam, and the camera captured the image sequence of the mid-span section at a distance of 1.5 m from the cantilever beam, with the camera lens parallel to the ground for orthogonal shooting. In the proposed method, the SIFT algorithm can rely solely on the texture information of the image to detect feature points, while speckles were prefabricated on the beam surface in the experiment to facilitate the operation of the DIC method for validation purposes, for which the proposed method works well.  
Figure 5: Flowchart of structural full-field dynamic deflection measurement of DFEM-DSMR.  
Figure 6: Schematic diagram and layout of indoor experiment. (**a**) Schematic diagram of the validation experiment. (**b**) Indoor experiment layout.  
In the experiment, the 4096 pixels \(\times\) 3072 pixels images were captured at the speed of 50 fps, and the image bit depth was 24. The thickness of the cantilever beam in the capture area was 2 mm, with 98 corresponding pixels in the image, and the orthogonal photogrammetric scale factor \(s\) can be calculated as:  
\[s=\frac{T}{t} \tag{10}\]  
where \(T\) is the thickness of the beam (mm) and \(t\) is the pixel number of the thickness in the image. The calculated scale factor was 0.02 mm/pixel. In the experiment, an image sequence comprising 100 frames was captured. The 20 th frame image is shown in Figure 7a, and the image without the background is illustrated in Figure 7b.
## 4 Conclusions  
In previous studies, normally distributed random noise was used for CMD. Therefore, in this study, an artificial dataset was created using other types of random noise distributions. To predict the type of motor imagery brainwave more accurately, a CNN classifier was trained with the dataset augmented via CMD based on GNDv1 and GNDv2. The PDF parameters of the random noise were varied, and their effects were investigated. The accuracy improved for larger \(\beta\) in GNDv1 and for small positive \(\kappa\) in GNDv2. In addition, the classification accuracy tended to increase when more artificial data were generated.  
It is worth noting that the optimal parameter values for \(\beta\) and \(\kappa\) can be changed if the proposed method is applied to other bio-signals. Thus, to determine the optimal parameters for random noise, the accuracy of the validation dataset needs to be obtained and compared with various parameter values.  
Figure 12: Classification accuracy of EMG data with GNDv2; the symbols and error bars represent the average accuracy and standard error, respectively.  
This study has proposed a generic method to release the constraints of classical CMD using GND. This method can be used to generate other types of bio-signals. However, GND might not be an optimal PDF for random noise for certain target signals. Other types of PDFs, such as multimodal distributions or Student's t-distributions, can be used for data augmentation in future work. Furthermore, a more general PDF for random noise, which is defined by many parameters, can provide higher-quality artificial data, the parameters of which can be trained to generate artificial data such that their distribution is similar to that of the original data.  
Conceptualization, W.N.; methodology, H.L.; software, H.L.; validation, H.L.; formal analysis, H.L.; investigation, H.L.; resources, W.Y.; data curation, H.L.; writing--original draft preparation, H.L.; writing--review and editing, W.N. and W.Y.; visualization, H.L.; supervision, W.N.; project administration, W.N.; funding acquisition, W.N. All authors have read and agreed to the published version of the manuscript.  
This research was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT; No. 2021R1 AA3030268), and the Chung-Ang University Research Grants in 2020.  
The authors declare no conflict of interest.
## 4 Community Radiative Transfer Model (CRTM)
The transmittance coefficients are obtained from the CRTM internal transmittance software [18] based on line-by-line calculations [33]. The internal surface reflectance is still under development. In this study, we use the reflectance option from the user inputs, and the surface is assumed to be Lambertian and unpolarized in the UV radiance simulations and assimilations. For OMPS NM, the surface reflectance can be estimated by fitting the measurements at two central wavelengths or at two channels. We believe that this study may be the first application of CRTM version 3.0 (to be publicly released) using OMPS UV radiance measurements.
## Appendix A PIV and PLIF Measurement  
In our study, the OH species profile and velocity fields were measured using planar laser-induced fluorescence (OH-PLIF) and particle imaging velocimetry (PIV), respectively. A schematic of the laser-based diagnostic system we used is shown in Figure 11. The OH-PLIF system is in a box surrounded by blue dotted lines, and the PIV system is within the box delineated by the red dotted lines. The OH-PLIF system was used to measure the flame structure. The source laser beam was generated at a wavelength of 532 nm, 10 Hz. The energy of each pulse was 15 mJ. The laser frequency was doubled, and the wavelength was transferred to 282.769 nm to excite the Q1(8) line of the A2E\(\leftarrow\)X2\(\Pi\)(1,0) transition. The fluorescence of OH was acquired by using an ICCD camera. The resolution of the ICCD was 1200 \(\times\) 900 pixels, and that of the visualized field was adjusted to 116 mm \(\times\) 87 mm (800 \(\times\) 600 pixels) to capture OH signals with a spatial resolution of 0.14 mm/pixel. The PIV system was used to measure the 2D velocity fields. A double-cavity Nd:YAG laser produced two 532 nm laser pulses with a 10 Hz repetition rate. A double-shutter CCD camera with a resolution of 1600 \(\times\) 1200 pixels and minimum 200 ns separation time was used to capture the dual frame, and TiO\({}_{2}\) with a particle size of 1 \(\upmu\)m was used as the seeding particle to visualize the velocity field. The visualized field was about 142 mm \(\times\) 107 mm. The experimental images were analyzed using Davis 7.2 software. Each OH-PLIF or PIV measurement was carried out for at least 100 realizations. Each operating condition was measured for four times. Further details about the OH-PLIF and PIV systems can be seen in our previous work [43].
## 1 Introduction  
Currently, two types of molten fluoride salt can be used as fuel solvent and coolant in a Molten Salt Reactor (MSR). These salts are FLiBe and FLiNaK. MSR refers to nuclear reactors of the IV generation, in which liquid fuel is used instead of solid fuel. The preparation of fuel for MSR involves the dissolving of fissile materials (\({}^{235}\)U, \({}^{233}\)U, \({}^{239}\)Pu) in molten salt, which must meet certain requirements, i.e., have the necessary physical and chemical properties. MSRs developed to date can use U, Pu, Th as fuel, and salt compositions for them can be chlorides, fluorides, or nitrides [1].  
A fast-spectrum breeder reactor (MSFR) has the possibility for actinide burning. Thanks to this reactor, the reduction of the reprocessing requirements and a better breeding ratio becomes possible [2]. The inclusion of MSFR in the nuclear power system for burning transuranium elements from spent nuclear fuel will solve the problem of closing the nuclear fuel cycle. It is known [3, 4] that CeF\({}_{3}\) and NdF\({}_{3}\) are chemically similar analogues of PuF\({}_{3}\) and AmF\({}_{3}\), respectively. It is more efficient to carry out experimental studies with safe analogues (Ce and Nd), rather than with the radioactive elements themselves (Pu and Am).  
It was found that molten fluoride mixtures FLiBe and FLiNaK form strong and relatively weak associates, respectively [5, 6]. On the basis of molecular dynamics (MD) calculations, it was shown that the value of the diffusion activation energy in the LiF-KF salt melt is commensurate with the strength of the anion-cation interaction in molten fluoride salts [7]. A stronger effect of temperature than the effect of the composition of the molten salt on the self-diffusion of anions and cations has been established. The thermokinetic properties (density, bulk modulus, thermal expansion coefficient, and self-diffusioncoefficient) of FLiBe and FLiNaK, including those with dissolved Cr, were calculated at a high temperature using ab initio MD [8]. The behavior of Mo solvated in the molten FLiNaK salt was studied by the method of molecular dynamics [9]. The dependence of the nature of complex formation of Mo on the degree of its oxidation is shown. With the use of molecular dynamics modeling, it becomes possible to create a scientific prediction of the behavior of solutes in molten salts and to predict the effect of their presence on the properties of the salt [10].  
The purpose of this work is to study the temperature dependence of ion diffusion in the molten FLiNaK salt with CeF\({}_{3}\) salt dissolved in it and to study in detail the structure of this melt under conditions close to those for an operating molten salt reactor. The study of the behavior of lanthanide (cerium) in the molten salt FLiNaK is important from the point of view of its use in the laboratory tests as a substitute for radioactive actinide, the specific type of which can be determined in future studies.
## 3 Results
### Optimization and Modeling in Energy Systems  
#### 3.1.1 Main Trends  
Recent advancements in the optimization and modeling of energy systems have been marked by the development of algorithms and models tailored to address the complexity and uncertainties inherent in DES. A prominent trend involves the application of stochastic and robust optimization techniques, which effectively handle uncertainties related to market prices, renewable generation, and variable loads [23]. For instance, a stochastic-robust model designed for 100% renewable energy communities integrates P2P transactions, collective assets like wind turbines and batteries, and coordinated scheduling among prosumers. This approach has demonstrated significant reductions in unserved energy and operational costs while maximizing resource utilization under uncertain conditions [1]. The adoption of hybrid approaches, combining robust optimization with risk management techniques such as CVaR, represents another key trend. These methods effectively model financial and operational risks associated with uncertain parameters, including limited historical data or unpredictable solar irradiation. A study employing this methodology illustrated that combining shared storage and P2P trading could lower operational costs by 21.49%, emphasizing the effectiveness of hybrid frameworks in addressing high-variability scenarios [16].  
Multi-objective optimization models have gained traction by simultaneously addressing economic, environmental, and technical goals. For example, a hierarchical energy management framework incorporates algorithms such as NSGA-II to optimize costs, carbon emissions, and wind energy spillage in DES. This approach aligns with modern demands for sustainability while enhancing decision making for resource allocation [24]. Moreover, decentralized models for energy communities have emerged as a solution to the challenges of resource coordination in complex systems. Techniques such as the Alternating Direction Method of Multipliers (ADMM) enable efficient energy management across multiple actors, reducing the computational time and improving operational flexibility under uncertain conditions [25; 26; 27].
## 5 Advanced MPPT Techniques
### Hybrid MPPT Techniques
#### 5.3.4 GA-P&O Algorithm  
The GA-P&O hybrid MPPT algorithm combines the global search capabilities of GA with the local optimization precision of P&O to enhance the tracking of the MPP in PV systems. GA initially explores a broad range of potential MPPs by mimicking natural selection, identifying a promising MPP region. Once this region is determined, the P&O algorithm fine-tunes the operating point for precise power tracking. This hybrid approach offers the advantage of avoiding local maxima and providing accurate MPP tracking, especially in complex or rapidly changing environmental conditions. However, it comes with the drawback of increased computational complexity, longer convergence times, and higher implementation costs due to the iterative nature of GA and the need for more sophisticated hardware and tuning. Despite these challenges, the GA-P&O hybrid is beneficial for systems requiring robust performance in dynamic conditions [100].
## 3 Results and Discussion  
### Structural Analysis of LTO  
In order to investigate the structural properties of LTO materials samples obtained via the salt-assisted solid state method, the material was characterized using XRD. Figure 1 presents the XRD patterns of the LTO powders with various additions of NaCl with 20 (deg.) range of 10-70\({}^{\circ}\). The XRD patterns of all samples show that all peaks of LTO are detected and corresponding to the JCPDS Card No. 42-0207 with the structure of cubic spinel and the space group of F3 dm [43; 44; 45]. Based on the XRD results, no slight impurities of NaCl were detected in the XRD pattern of LTO-2 wt % NaCl and LTO-4 wt % NaCl. However, with an addition of 6 wt % NaCl, tiny peaks of NaCl appear in the XRD pattern. The peaks of NaCl were easily detected at 20 of 31\({}^{\circ}\) and 45\({}^{\circ}\) (JCPDS No. 5-0628).  
Figure 1: The XRD patterns of Li\({}_{4}\)Ti\({}_{5}\)O\({}_{12}\) (LTO) powder in various NaCl additions.  
For further analysis of the XRD patterns, the calculation of lattice constants, Full Width at Half Maximum (FWHM), and crystallite size were calculated and are presented in Table 1. The values of lattice constants were approximately 8.384-8.362 A for the various samples of LTO, which correspond to those of the LTO sample reported in the literature (8.395-8.3538 A), [34; 46; 47; 48] and indicates that LTO is a zero-strain material with good structural stability [48; 49]. Based on these results, it can be predicted that the as-obtained LTO exhibits no volume expansion during the lithiation/delithiation process. The lattice constant values are decreased with NaCl additions ranging from 0-4 wt % but increased with an NaCl addition of 6 wt %. Since LTO with 4 wt % NaCl has the lowest lattice constant, it is predicted that the sample will have better electrode performance compared to the other samples during electrochemical testing, as reported by [PERSON] et al. [50].  
Surprisingly, the XRD results revealed the effect of NaCl addition on the crystallinity of LTO material. The crystallinity of LTO can be evaluated based on the FWHM (\(\beta\)) of (111), (311), and (400) peaks. Table 1 shows the FWHM values of LTO samples decrease from the range of 0 to 6 wt % NaCl addition. It means that the higher NaCl addition will result in the sharper peak on XRD and smaller values of FWHM, indicating the higher crystallinity of the material [51]. Therefore, it can be concluded that the increasing salt addition during the solid-state reaction had a good influence on LTO crystal formation. Salt exerts a medium reaction that accelerates the mass transfer of a precursor during the sintering process. During the reaction process, the NaCl (salt) enlarges and provides heat flux, which improves crystal growth [39]. Therefore, a highly crystalline product can be produced rapidly.  
The crystallite sizes of the samples with 0-6 wt % NaCl additions were calculated using the Debye-Scherrer equation [51; 52; 53]. The crystallite sizes of LTO on (111), (311), and (400) peaks increase as the NaCl additions increase, which is in agreement with a study reported by [PERSON] et al. [51].
## 3 Proposed Methods
### Improvement of Feature Fusion Network  
The channel attention module (CA) and the spatial attention module (SA) are embedded into the feature fusion network of the neck according to the needs, and the neck is lightweight to make it more focused on feature fusion. The feature fusion capability of the neck is preserved while reducing the computational overhead. The obtained feature fusion network is named FF-FPN (Feature Fusion-Feature Pyramid Networks), and its network structure is shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Input** & **Network Unit** & **Channel Number** & **Step** \\ \hline \(1^{2}\times 3\) & CBL & 32 & 2 \\ \(1/2^{2}\times 32\) & CBL & 64 & 2 \\ \(1/4^{2}\times 64\) & E-ELAN & 64 & 1 \\ \(1/4^{2}\times 64\) & Maxpool & 2 \\ \(1/8^{2}\times 64\) & E-ELAN & 64 & 1 \\ \(1/8^{2}\times 64\) & MVBlock & 64 & 1 \\ \(1/8^{2}\times 64\) & Maxpool & & 2 \\ \(1/16^{2}\times 64\) & E-ELAN & 128 & 1 \\ \(1/16^{2}\times 128\) & MVBlock & 128 & 1 \\ \(1/32^{2}\times 128\) & Maxpool & & 2 \\ \(1/32^{2}\times 128\) & E-ELAN & 128 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overall architecture of the feature extraction network GAE-ELAN.  
Figure 3: Structure of CBAM.  
The feature fusion process of FF-FPN is as follows:  
1. The P5 feature layer extracted from the backbone network undergoes a convolution downsampling to obtain the feature layer P6 with a higher level of semantic information.
2. The feature layer with higher semantic information guides the information fusion of the next feature layer with the help of the attention mechanism. The specific method is to obtain the spatial attention weight of P6, P5, and P4 feature layers through the SA module once, and then splice them with P5, P4, and P3 layers after upsampling. The spliced feature layers are perceived by the channel attention weight through the CA module once, and the feature channels are fused using convolution to obtain the preliminary fused feature maps P'5, P'4, and P'3.
3. Integrate the low-level feature map to the high level. The bottom feature maps P'4 and P'3 are spliced with the higher-level P'5 and P'4 through downsampling, the channel attention weight is obtained through a CA module, and then the feature channels are fused through convolution to obtain the further fused feature layers P\"5, P\"4, and P\"3.
4. According to the needs of the detection task, repeat steps (2) and (3) to obtain the final fused feature layers C5, C4, and C3.