esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Miniaturization of Strontium Optical Lattice Clock for a Space Optical Clock  
Because space is a microgravity environment, an optical clock should achieve higher stability and accuracy than on Earth [29; 30]. An optical clock in space may be used in tests of fundamental physics, such as [PERSON]'s theory of relativity, positioning, time and frequency transfers, accurate geoid determination, and monitoring and deep space navigation.  
Making optical clocks compact and robust for transport is the first step towards applications of optical clocks in space [31; 32; 33]. We have produced a compact, robust laser source system operating at 461 nm along with a vacuum setup system (Figure 9). With these two systems, the first stage cooling and trapping of the \({}^{87}\)Sr atoms have been accomplished [34]. The ECDL 461-nm laser is locked to the \((5s^{2})^{1}S_{0}-(5s5p)^{1}P_{1}\) transition line of \({}^{88}\)Sr. After lock, the residual power of the 461-nm laser is 35 mW. More power is obtained from two other injection-locked slave lasers. The vacuum system includes an atomic oven, a two-dimensional collimating cavity, a Zeeman slower and a MOT cavity. We designed and constructed the Zeeman slower with permanent magnets [34]. With a MOT coil current of 2 A, an axial magnetic field gradient of 43 Gs/cm was obtained in the center of the MOT cavity and satisfies the requirements for the first-stage Doppler cooling very well.  
Figure 8: (a) \(\sigma^{\pm}\)-transition spectrum; (b) Measured values of \(2\
icefrac{{\sigma^{\pm}}}{{mF}}f_{d,mF}\) as a function of the magnetic field strength; (c) Measured values of \(\delta g\mu_{0}\) (Hz\(\cdot\)G\({}^{-1}\)) obtained from the \(\sigma^{\pm}\)-transition spectra.  
The internal diameter is 1.5 mm and the final temperature of the blue MOT is approximately 10.6 mK, which was determined by observing the expansion of the atoms in this MOT. The number of cold atoms finally trapped in the blue MOT is 1.6 \(\times\) 10\({}^{6}\) for \({}^{88}\)Sr and 1.5 \(\times\) 10\({}^{5}\) for \({}^{87}\)Sr. With two repumping lasers operating at 679 nm and 707 nm, the typical number of loaded atoms increased typically five-fold compared with before.
## 5 Fragility and Leakage Risk Assessment of the NCS
### Fragility Assessment Results
#### 5.2.3 HCLPF Capacity of the NCS  
The HCLPF capacity is considered to be the most conservative pressure capacity for evaluating the nuclear structures and facilities in a nuclear power plant, representing the pressure capacity corresponding to a failure probability of 5% on the fragility curve with a 95% confidence level [39]. The fragility curves at different confidence levels can be determined using the following equation [40]:  
\[F_{R}(p,\omega)=\Phi\left(\frac{\ln(p/P_{\text{m}})+\beta_{M}\cdot\Phi^{-1}( \omega)}{\beta_{R}}\right) \tag{12}\]  
where \(\omega\) is the confidence level and \(\Phi^{-1}\) is the inverse function of the standard normal distribution function.  
Therefore, the HCLPF capacity can be expressed as:  
\[\text{HCLPF}=P_{\text{m}}\cdot\exp[-1.645(\beta_{M}+\beta_{R})] \tag{13}\]  
Table 4 summarizes the pressure capacity of the NCS at different confidence levels. Compared with the uncorroded case, the HCLPF capacity with 10%, 20%, and 30% corrosion degrees of the liner is reduced by 6.79%, 12.83%, and 17.35%, respectively. Compared to Table 3, the reduction rate for HCLPF is slightly higher than the reduction rate calculated based on the median pressure capacity of the NCS.
## 2 Materials and Methods
### Study Data  
#### 2.2.1 ET Data  
The YRB has strong spatial heterogeneity and requires high--resolution data. The PML_V2 ET remote sensing product used in this study is currently the only ET and GPP coupling data product with 500 m resolution, and the data period is from 2001 to 2020. The temporal resolution is 8--day, and the ET data with two timescales of months and years are synthesized for subsequent analysis.  
The PML_V2 ET dataset was generated based on the Penman--Monteith--Leuning (PML) model by inputting leaf area index (MCD15A3H), albedo (MCD43A3), surface specific radiance (MOD11A2), and land use (MCD12Q1) data from MODIS satellite data and the air temperature, specific humidity, wind speed, surface pressure, precipitation, and shortwave radiation from the meteorological dataset of the CMA Land Data Assimilation System (CLDAS--V2.0) [37]. The dataset includes five components: gross primary production (GPP), vegetation transpiration (Ec), soil evaporation (Es), vaporization of intercepted rainfall (Ei), and water, ice, and snow evaporation (ET_water), coupling the two internal processes of GPP and vegetation evapotranspiration, making GPP and ET check and balance each other, making PML_V2 a great improvement in ET simulation accuracy compared with previous models.
## 3 Results and Discussion
### Comparison of the Experimental Results with Previous Researches
According to Table 9 and Figure 20, using LWC has decreased the energy absorption at both yield and ultimate points. Besides, this characteristic at yield and ultimate points was improved when steel fibres was employed approximately 8% and 27%, respectively, compared to LWC core with no steel fibres. Additionally, Figure 20a,b show that up to the yield point the energy absorption of CSC system slabs is substantially more than two other systems, while at the ultimate strength point the difference in energy absorption is significantly reduced for J-hook system slabs. In addition, compared with those produced with stud bolt connectors, using CSC connectors enhances the value of energy absorption at both yield and ultimate strength points, substantially. For further detail, the ductility ratio of specimens was also evaluated. This index is defined as a ratio of the displacement at ultimate strength \((\Delta_{u})\) to the displacement related to the yield point \(\left(\Delta_{y}\right)\) as follows:  
\[i=\frac{\Delta_{u}}{\Delta_{y}} \tag{1}\]  
The obtained results of the ductility of specimens are represented in Table 10. It should be mentioned that in order to show the performance of the proposed connector in this study, the ductility of specimens was compared to those manufactured by J-hook and stud bolt connectors. As it is seen from Table 10, using LWC resulted in raising the ductility ratio, especially when steel fibres were also utilized compared to the ones with NWC core except for NWC core with J-hook connectors. However, this issue will require further studies under the same conditions.
# Spindle Bearing Preload Control Using NI MyRIO
## 1 Introduction  
Modern machine tool spindle systems are very often equipped with angular contact ball bearings. These types of bearings allow for the transfer of loads simultaneously in the radial and axial directions in proportion to the angle of action of the bearing. More on this subject can be read in publications [1, 2]. For the proper operation of this type of bearings, it is necessary to introduce the so-called preload, i.e., applying an axial force on the bearing races to press them against the rolling elements. The introduction of the preload ensures the correct operation of the bearing, no slippage of the rolling elements, even distribution of the lubricant, and also has a positive effect on its temperature stability [3, 4]. In most cases for spindle systems, the preload value is determined during the assembly of a given spindle and remains a constant value throughout the spindle's use. Of course, it should be noted that the term constant value may be misleading because the preload value changes, e.g., due to bearing wear or during their operation in the HSC (High-Speed Cutting) speed range. However, these changes are very small, and bearing manufacturers guarantee that, provided that the maximum number of bearing operating hours is maintained and the maximum load is not exceeded, the preload value can be considered a constant value.  
The continuous development of control systems and active elements means that possibilities of their application have also emerged in the area of spindle systems. Various scientists are experimenting with the use of such elements for correction and sensors for controlling and monitoring the state of the preload bearings in spindle systems of machine tools. Many of these systems are prototypes, but the research presented by the authors in this area gives satisfactory results and allows for positive prognosis for their future. It is worth mentioning here the works of [5, 6, 7].
## 2 Description of the Proposed MR Brake
### Evaluating Braking Torque for the Proposed Pole Head Design  
A magnetorheological brake functions based on the magnetic flux interactions between the moving and immovable elements. The pole head is the part of the brake through which the magnetic flux exits from the core material and enters the MRF. The distribution and density of the magnetic flux are influenced by the size and shape of the pole head. In reaction to the magnetic flux generated, the moving rotor generates braking action. While smaller pole heads lower the surface area available for magnetic interaction, they can result in a decrease in brake torque; larger pole heads increase the surface area through which magnetic flux interacts with the rotor disc. The distribution of magnetic flux is also influenced by the shape of the pole head. So, an ideal distinct shape (i.e., circular sector shape) is chosen to maximize the flux flow in the contact region for a stronger magnetic field.  
In a magnetorheological brake, braking torque can be calculated by considering several factors, such as magnetic flux density, the geometry of the brake, the properties of the magnetorheological fluid, and the applied voltage/current. The strength of the magnetic field applied to the MR brake defines the flux density that passes through the pole head. Considering a circular sector pole head with trapezoidal dimensions, as shown in Figure 4, 'r' measures the radius of center of the pole head through which flux flows in a axial direction.  
Braking Torque Calculation for a Specified Pole Head Geometry  
The shear force (F) acting on the MR fluid is directly proportional to the effective area (A) exposed to the magnetic field and the magnetic flux density (B). It can be expressed as:  
\[\text{F}=\text{B}\times\text{A} \tag{1}\]  
To determine the effective area for the sector pole head geometry exposed to the magnetic field.  
\[\text{A}=\text{A}_{\text{sector}}-\text{A}_{\text{trapezoid}} \tag{2}\]  
Firstly, calculate the area of the sector pole head geometry (A\({}_{\text{sector}}\)):  
\[\text{A}_{\text{sector}}=\frac{\theta}{360}\times\pi\times\text{r}^{2} \tag{3}\]  
where  
The angle subtended by the sector is \(\theta\) (in degrees), the radius of the circle is r and \(\pi\) value = 3.14.  
Calculate the area of the trapezoidal pole head (A\({}_{\text{trapezoid}}\)):  
\[\text{A}_{\text{trapezoid}}=\frac{1}{2}\times(\text{b}_{1}+\text{b}_{2})\times \text{h} \tag{4}\]  
where  
Two lengths of the parallel sides of the trapezoid are denoted by b\({}_{1}\) and b\({}_{2}\), and the height of the trapezoid is denoted by h.  
Therefore, the effective area 'A' of the circular sector pole head can be obtained from Equations (3) and (4)  
From Equations (1) and (2), the torque exerted on the MR fluid due to the shear force can be calculated by multiplying the shear force by the radial distance (r) from the center of rotation. It can be denoted by the following expression:  
\[\text{T}=\text{F}\times\text{r}\]
## 4 Detection and Mitigation Mechanism
### Idbn
#### 4.1.3 Model Structure and Parameters Selection  
The determination of DBN structure for a particular system has not yet been fully supported by theory. It is necessary to confirm the relatively superior structure and parameters through the verification of theories and simulation experiments [34]. In order to determine the model structure and parameters selection of the proposed IDBN, a simulation experiment was conducted based on the NSL-KDD dataset. The NSL-KDD dataset contains 25,192 instances of training data. Each data in the dataset consists of an attribute label and 41 attribute characteristics. The attribute label divides the data into 4 types of attacks and 1 type of normal data. Moreover, the 4 types of attack data are divided into 39 subtypes. Furthermore, the 41 attribute characteristics are divided into 3 nominal types and 38 numeric types [35].  
The normalized training set and test set were obtained by adopting the methods that the data of nominal type is encoded by PMF and the data of numeric type is normalized. Since the dataset had 41 data characteristics and had divided into 1 type of normal data and 4 types of abnormal data, the IDBN input data was 41-dimensional and the output data was 5-dimensional.  
Considering the efficiency problem of intrusion detection, the number of DBN hidden layers was limited to 6 layers, which may sacrifice parts of the detection accuracy. The number of nodes in each hidden layer was the same and can be selected in {10, 20, 40, 60, 80, 100}. According to [36] and the iterative cross experiments, the other parameters of the IDBN model were set as follows: the learning rate in the pre-training and fine-tuning stages was set to 0.04; the number of iterations in the pre-training phase was 5, and after 20 iterations in fine-tuning stage, the results were stable in the area. The number of nodes and hidden layers were determined through simulation experiments.  
As shown in Figure 3a, the model obtains a relatively high detection rate in the detection process when the number of hidden layers is 2. In addition, increasing the depth of DBN is not a positive correlation with the ability of feature extraction. Instead, it may lead to the decrease of generalization ability and the problem of overfitting.  
In the condition where the number of hidden layers is fixed at 2 and the number of nodes in each hidden layer is changing, the change trend of the correct detection rate is shown in Figure 3b. When the number of nodes is 10, it is not enough to extract the feature set, which is suitable for classification, because of the less connected nodes to each other. Meanwhile, when the number of nodes is too large, there will be a problem of overfitting. Therefore, the correct detection rate is relatively highest when the number of hidden layer nodes is set to 20. Then, the design of IDBN is completed.  
Figure 3: (**a**) The correct detection rate changes with the number of hidden layers (20 nodes); (**b**) The correct detection rate varies with the number of hidden layer nodes (2 hidden layers nodes).
# Exploring CeO\({}_{2}\)-Doped Co/SBA-15 Catalysts for Acetic Acid Oxidative Steam Reforming
## 3 Results and Discussion
### Stability Test on Co/10 CeO\({}_{2}\)-SBA-15 and Co/20 CeO\({}_{2}\)-SBA-15 Catalysts
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Catalyst**} & \multirow{2}{*}{**T (\({}^{\circ}\)C**} & **S/C** & **O\({}_{2}\)/AcOH** & **TOS** & **GHSV** & **(mL/gcat.** & **X\({}_{\textbf{ACOH}}\)** & **YH\({}_{2}\)** \\  & & **Ratio** & **Ratio** & **(h)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline Ni/CeO\({}_{2}\)-m & 500 & 2 & 0.075 & 5 & 25,000 & 97 & 54 & [38] \\ Ni/La\({}_{2}\)O\({}_{3}\)-CeO\({}_{2}\)-m & 500 & 2 & 0.150 & 5 & 25,000 & 97 & 54 & [25] \\
10 Ni/Sm\({}_{2}\)O\({}_{3}\)-MnO & 700 & 4 & 0.280 & 10 & 51,000 & 100 & 62.5 & [59] \\ Ni\({}_{0.08}\)Mg\({}_{0.10}\)Ti\({}_{0.37}\)O\({}_{0.92\pm 8}\) & 700 & 4 & 0.280 & 10 & 50,700 & 100 & 68 & [60] \\
15 wt\% NiO-75 wt\% & 700 & 4 & 0.280 & 10 & 51,000 & 100 & 65.5 & [61] \\ TiO-10 wt\% YO\({}_{1.5}\) & & & & & & & & \\
17\%/CO/CeO\({}_{2}\) R & 600 & 4 & 0.280 & 10 & 37,260 & 100 & 62.5 & [62] \\ Ni\({}_{0.43}\)Mg\({}_{2.56}\)CrO\({}_{4.5\pm 8}\) & 700 & 4 & 0.280 & 10 & 25,550 & 100 & 62.5 & [63] \\ Co-Rh/CeO & 600 & 2 & 0.075 & 25 & 25,000 & 100 & 70 & [39] \\ Ni\({}_{0.8}\)La\({}_{1.35}\)Pr\({}_{0.73}\)O\({}_{3.92\pm 8}\) & 700 & 4 & 0.280 & 50 & 50,600 & 100 & 62.5 & [64] \\ Ni\({}_{0.80}\)Ce\({}_{1.76}\)Y\({}_{0.35}\)O\({}_{4.85\pm 8}\) & 700 & 4 & 0.280 & 50 & 50,600 & 100 & 65 & [65] \\ Ni-2 Sm-Al-O & 700 & 4 & 0.280 & 30 & 30,000 & 100 & 65 & [37] \\ Co/10 CeO\({}_{2}\)-SBA-15 & 550 & 2 & 0.075 & 50 & 25,000 & 85 & 62 & This \\ Co/20 CeO\({}_{2}\)-SBA-15 & 550 & 2 & 0.075 & 50 & 25,000 & 85 & 55 & Work \\ \hline \hline \end{tabular}  
* Standardized H\({}_{2}\) yield data by applying Equation (13).  
\end{table} 
Table 3: Catalytic performance of several catalysts in acetic acid oxidative steam reforming. conditions at 700 \({}^{\circ}\)C [37; 59; 60; 61; 63; 64; 65], reaching H\({}_{2}\) yields of 62-65%. Although a straightforward comparison becomes difficult due to differences in experimental conditions, the use of a standardized H\({}_{2}\) yield expression allows a more objective assessment. A competitive performance is demonstrated by Co/10 CeO\({}_{2}\)-SBA-15 and Co/20 CeO\({}_{2}\)-SBA-15 catalysts. Furthermore, a comparison with our studies performed under similar conditions [25; 38; 39] showed that both catalysts exhibited excellent long-term stability, maintaining their activity over 50 h time-on-stream.
# Microscopic Numerical Analysis of Sand Liquefaction Under Subway Train Load
## 7 Limitations and Future Work  
DEM simulations exhibit inherent limitations requiring methodological refinement. The spherical particle idealization inadequately represents natural sand angularity, compromising interlocking mechanisms and inducing shear strength underestimation. Simultaneously, linear contact formulations neglect rotational resistance and cohesive interactions, diminishing skeleton stiffness while restricting applicability to clay-bearing sands. Furthermore, the constant-volume approach provides only approximate undrained conditions, failing to resolve pore fluid dynamics achievable through fully coupled CFD-DEM frameworks. To advance predictive capabilities, subsequent research must implement non-spherical particle representations (clump-based or polyhedral models) for enhanced geometric fidelity, develop integrated fluid-particle coupling solutions for precise pore pressure quantification, and validate models against in situ metro monitoring datasets incorporating site-specific stratigraphy and vibration profiles, collectively enhancing liquefaction assessment reliability for underground infrastructure.  
[PERSON] conceptualized the research framework and revised the manuscript; [PERSON] performed data analysis and drafted the paper; [PERSON] and [PERSON] contributed to critical review and editing; [PERSON] were responsible for figure design and manuscript proofreading. [PERSON] provided guidance in academic writing and data analysis. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Shandong Provincial Natural Science Foundation, China (Grant No. ZR2021 MD005) and the Qingdao Natural Science Foundation Original Exploration Project 'Study on Long-Term Deformation Characteristics of Reinforced Soil Retaining Wall Embankment for High-Speed Railway in Seasonal Frozen Regions'.  
This study does not involve any ethical issues and has not required approval from an ethics committee or related documentation.  
This article does not involve human subjects as the focus of research.  
All data from this study are available upon request by contacting the corresponding author via email.  
The authors wish to acknowledge [PERSON] (formerly affiliated with Qingdao University of Technology) for his participation in preliminary conceptual discussions during the initial research phase. We extend sincere appreciation to [PERSON] (Beijing Urban Construction Design and Development Group Co., Ltd.) for her decade of metro engineering expertise, particularly in validating vibration frequency selection, and ensuring experimental parameters aligned with operational safety requirements for subway infrastructure.  
Author [PERSON] was employed by the company China Railway 16 th Bureau Group Electrification Engineering Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Acknowledgments  
The authors would like to thank the editors for their valuable comments and suggestions Author Contributions.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Routing** & **Energy** & **Mobility** & **Scalability** & **Data** & **Location** & **Query** & **Real-Time** & **QoS** \\
**Protocol** & **Aware** & **Mobility** & **Scalability** & **Aggregation** & **Awareness** & **Based** & **Multi-Media Support** & \\ \hline
**MMSPEED** & No & Yes & No & No & No & Yes & Yes & Yes \\
**ProHet** & Yes & No & No & Yes & No & Yes & No & Yes \\
**ECMP** & Yes & No & No & No & No & No & Yes \\
**CQARP** & Yes & No & No & No & No & Yes & No & Yes \\
**PT** & Yes & Yes & Yes & Yes & No & Yes & Yes \\
**QEMPAR** & Yes & No & No & No & Yes & No & Yes \\
**SPEED** & No & Yes & No & No & Yes & Yes & Yes \\
**MGR** & Yes & Yes & No & No & Yes & No & Yes \\
**SAR** & Yes & Yes & No & No & No & Yes & No & Yes \\
**QEMH** & Yes & No & No & No & No & No & Yes & Yes \\
**QRPWMSN** & No & No & Yes & Yes & Yes & No & Yes \\
**DCF** & No & No & No & Yes & Yes & Yes & No & Yes \\
**QBR** & No & No & No & Yes & Yes & No & Yes \\
**DGR** & Yes & Yes & No & No & Yes & Yes & No & Yes \\
**GRAB** & Yes & No & No & No & Yes & Yes & No & Yes \\
**SWSP** & No & No & No & Yes & No & Yes & No & Yes \\
**MQoSR** & No & No & No & No & Yes & No & Yes \\
**MIMO** & No & Yes & No & Yes & No & Yes & No & Yes \\
**Mobicast** & Yes & Yes & Yes & No & Yes & Yes & Yes & Yes \\
**EEQR** & Yes & No & No & No & Yes & No & No & Yes \\
**EQSR** & Yes & No & No & Yes & No & No & Yes & Yes \\
**MCBR** & Yes & No & No & No & No & No & Yes & Yes \\
**MCMP** & No & No & No & No & No & Yes & Yes \\
**PRTR** & No & Yes & No & No & Yes & No & Yes & Yes \\
**QoSR** & Yes & Yes & No & No & No & Yes & Yes & Yes \\
**QoSAM** & No & Yes & No & No & No & No & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation and comparison of QoS routing protocols over WMSNs.