esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Thermal Conductivity of Alumina Nanofluids  
Nanofluid thermal conductivity is an important transport property for practical applications. As seen in Figure 3A, in this study, thermal conductivity was measured with respect to temperature by the lambda system. The LAMBDA system measures the thermal conductivity of fluids according to ASTM D 2717. The measurement principle is based on a resistance measurement using a hot-wire  
Figure 1: (**A**) Experimental apparatus and (**B**) schematic diagram.  
with a diameter of only 100 \(\upmu\)m. The sample temperature was controlled by a special double-jacket heating/cooling device which provided a homogeneous temperature distribution. It was calibrated with DW before measurements. For a better comparison, alumina nanofluids were measured and plotted. Therefore, as shown in Figure 3B, thermal conductivity increased with alumina concentration. Which indicates that increasing the concentration results in a higher thermal conductivity. The thermal conductivity was increased with a temperature due to the increased intermolecular collision. The measured thermal conductivities showed the traditional liquid characteristic, and the thermal conductivity was increased with the nanoparticle concentration.
## 3 Materials and Equipment
### Box Model Test Equipment  
The second part of the experiment was the box model test. The equipment used in this experiment was composed of a customized specimen box and a loading system assembly, as illustrated in Figure 6a. The mobile or detachable loading system was placed on the middle-top of the specimen box. The loading system had a maximum capacity of five tons and a minimum loading speed of 1.7 mm/s.  
Figure 6: The (**a**) box model test experimental set-up, and the actual photos of the model test equipment for (**b**) a combination of NMC clay and gravel, and (**c**) sand.  
The specimen box had internal, clear dimensions of 1.5 m in length, 0.5 m in width, and 1.0 m in height. The left- and right-side walls of the specimen box were made of 5 mm thick stainless-steel plates and reinforced with 40 by 5 mm thick flat steel bars, spaced at 175 mm on-center vertically, and 75 by 40 by 5 mm thick steel channels at the perimeters of the walls. Similarly, the rear wall of the specimen box was made of a 5 mm thick stainless-steel plate and reinforced with 40 by 5 mm thick flat steel bars, spaced at 175 mm on-center vertically, and 75 by 40 by 5 mm thick steel channels at the center and at the perimeter of the wall. The front wall was made of a 10 mm thick, transparent, polycarbonate sheet, through which the interior could be observed (see Figure 6b,c) and was reinforced horizontally with 40 by 5 mm thick, equal angle steel bars, placed at the center and vertically, with 75 by 40 by 5 mm thick steel channels at the center and the perimeter of the wall. The top of the specimen box was kept open for the operations, while two lengths of detachable, 40 by 5 mm thick, equal angle steel bars were placed as reinforcements during tests. The bottom plate was made of a 5 mm thick, stainless-steel plate and braced with 75 by 40 by 5 mm thick steel channels on-center. The wall reinforcements were designed to prevent the deformation of the specimen box during testing. The specimen box was placed on top of the supporting platform made of a 15 mm thick steel plate with rollers and stabilizing locks. Figure 6b,c show the actual photos of the box model experiment, including the material sample sets inside the specimen box.
# A New Click-Through Rates Prediction Model Based on Deep&Cross Network
## 3 The Proposed Model  
As we can see from the recent advancements, the key factors to boost the performance of CTR are more advanced and effective feature combinations, and more state-of-the-art techniques from DNN to better train and capture non-linearity. Therefore, based on Follow The Regularized Leader (FTRL) [11] and Deep&Cross Network (DCN) [18], this paper proposes an integrated model with feature optimization (FO), namely FO-FTRL-DCN, which will be empowered jointly by the embedding based on DCN, SMOTE on the imbalance of data and the FTRL optimization algorithm [11]. More specifically, the major contributions of the model are as follows.  
* First, in the embedding phase, the truncated normal distribution is introduced, which is a special normal distribution that limits the value range of variables. In the embedding stage, the truncated normal distribution can be used to replace the normal distribution to reduce some noise interference.
* Secondly, SMOTE is applied to tackle the imbalance of data, which can analyze the data of minority classes, synthesize new minority samples and add them to the original data, and balance the ratio of positive and negative samples.
* Thirdly, the advanced FTRL optimization algorithm is applied, which can greatly improve the optimization effect of CTR model in complex situations. FTRL is an efficient optimization algorithm and online learning algorithm in information retrieval and recommendation, combining the power of forward backward splitting (FOBOS) [12] and regularized dual averaging (RDA) [13]. It uses both \(L1\) and \(L2\) regularization terms in the iterative process, which greatly improves the prediction of the model. The latest version of FTRL-Proximal [8] is adopted in the proposed model and has gained substantial improvements in performance.  
The general structure of FO-FTRL-DCN is shown in Figure 1. The backbone of the FO-FTRL-DCN model can be divided into two parts: one is the feed forward deep network; the other is the cross network.  
The main ideas of FTR are detailed as follows.
## 2 Methodology
### Linearization Analysis and Evaluation Criteria  
From Equations (13) and (15), it can be seen that the mathematical model of the material weighing system inside process equipment contains two parts: one is a linear term composed of mass truth value, system inherent parameters, and the additional mass \(m_{D}\); the other is a power function term, which represents the measurement value variable caused by the connected piping. In order to ensure the weighing system accuracy, the value of the power function term should be controlled in a reasonable range. The relative change of the material mass caused by the connected piping is defined as the mass change coefficient \(m^{*}\):  
\[m^{*}=\frac{\Delta m-m_{D}}{m}=\frac{2}{\sqrt{\frac{2}{a^{2}b}m+1+1}} \tag{16}\]  
In engineering applications, when \(m^{*}\leq 5\%\), the connected piping can be regarded as a linear spring, with resistance exerted, and has little influence on the weighing result [36]. In this study, the high precision grade 1.0 of the industrial instrument is taken as a reference for theoretical analysis, which provides a basis for the accurate application of the weighing system in the following research. When \(m^{*}\leq 1\%\), the power function term can be ignored and the displayed value \(m_{1}\) has a linear relationship with the truth value \(m\). When \(m^{*}\geq 1\%\), it is considered that the influence of connected piping on weighing measurement resultscannot be ignored, and the power function term should be added to the linear part to correct it appropriately.
# A Novel Wavelet Selection Method for Seismic Signal Intelligent Processing
## 4 Discussion  
This paper proposes a novel wavelet selection method for the big seismic dataset based on decomposition stability \(w\). We tested our method in two field application experiments and achieved some valuable and positive results.  
These results show that the effect can be different by using different wavelets for the intelligent processing of automatic classification and P arrival picking of the seismic waveform. Moreover, the wavelets selected by our method can optimize the results when other influencing factors are excluded. In contrast to existing wavelet selection methods, our approach focuses for the first time on the selection criteria that optimize feature extraction for the entire big dataset. This provides a state-of-the-art quantitative wavelet selection method for many new techniques based on data-driven intelligent signal processing and analysis.  
Furthermore, by analyzing and comparing the results between the classification and P arrival picking, we find that the selected wavelets are both rbio3.1. There are three main reasons for this result. Firstly, although mining-induced microseismic and natural seismic events are different, they generally belong to the same broad class of signals. Secondly, in our experiment, the time length is ignored when the waveform is used as input, and the sampling point length is adopted, which makes the waveform of the two similar in time sequence. Finally, the amplitude units used by the two signals are different, so the amplitude differences between the two signals with significant magnitude differences are  
Figure 8: The automatic seismic P arrival picking results using wavelet packet decomposition and deep learning. **(a)** The first round of comparison experiments. **(b)** The second round of comparison experiments. **(c)** The third round of comparison experiments. Each round of comparison experiments contains the rbio3.1 wavelet selected by our method (blue) and five randomly selected wavelets (yellow).  
minor. However, although the selected wavelets are all rho3.1, the \(w\) obtained by calculation has an enormous difference, indicating that this method is differentiated. Additionally, we briefly discuss the effect of the number of wavelet packet decomposition levels on the results of wavelet selection. As shown in Figure 9, as the number of decomposition levels changes, the value of \(w\) changes, but the distribution of the results is still similar for each layer and does not affect the wavelet selection. Therefore, it can be assumed that the number of levels of wavelet packet decomposition has no effect on our method, and it can be disregarded while being used. In addition, this paper only tested the application of this method in seismology, and no experiments were conducted for other fields.
## 3 Results
### Key Findings  
The reviewed studies provided significant findings regarding PIP joint biomechanics, the effectiveness of different therapeutic strategies, and the factors influencing rehabilitation. From an anatomical perspective, it was determined that the PIP joint is primarily innervated by palmar digital branches, while dorsal branches were only identified in the little finger. In cases of fractures and dorsal dislocations, instability significantly increased when more than 50% of the joint surface was compromised. Regarding interventions, dynamic splints demonstrated greater efficacy in improving active PIP extension compared to static splints, whereas custom-made splints restricted flexion more than prefabricated models. The Edinburgh immobilization position showed variable effects on joint stiffness, while PIP blocking splints proved more effective than MCP blocking splints in reducing pain and improving function.  
When comparing surgical and conservative treatments, non-surgical therapy was generally preferred, with surgery reserved for cases where conservative treatment had failed. Surgical outcomes for PIP contractures were not encouraging, emphasizing the importance of early rehabilitation and the use of orthoses to prevent joint stiffness. For swan-neck deformities, splints were effective in mild cases, while severe deformities required surgical intervention. In the context of fractures and dislocations, dynamic external fixation improved both ROM and joint stability, achieving a mean post-treatment ROM of 89\({}^{\circ}\). Regarding PIP arthroplasty, silicone implants were the preferred option due to their lowerrevision rates, although post-traumatic arthritis increased the risk of reintervention. Higher revision rates were identified in patients under 60 years old (25%), while PIP fusion at angles between 20\({}^{\circ}\) and 40\({}^{\circ}\) resulted in better functionality compared to fusion at 0\({}^{\circ}\), which significantly impaired grip strength.  
In summary, the findings highlight the importance of selecting the most appropriate treatment based on the severity of the pathology, prioritizing dynamic orthoses and conservative strategies in most cases. However, the methodological variability among the reviewed studies limits the possibility of establishing a definitive consensus on the optimal approach for each specific clinical condition.
## 4 SNR Analysis of a QADA Receiver
P/S is parallel to serial and S/P is serial to parallel. to the channel gains to maximize the SNR. To do that, we use the well-known MRC algorithm [39]. The weighting factor using MRC is given by  
\[\alpha_{k}=h_{k}\Bigg{/}\sqrt{\sum\limits_{k=1}^{4}{{{{\left|{h_{k}}\right|}^{2}}}}} \tag{14}\]  
The SNR of the signal after MRC combining is given by  
\[\text{SNR}=\frac{\sum\limits_{k=1}^{4}{{{\left|{{h_{k}}}\right|}^{2}}E\left[{{ \left|{s\left(t\right)}\right|}^{2}}\right]}}{E\left[{{\left\|{\mathbf{w}\left( t\right)}\right\|}^{2}}\right]}} \tag{15}\]
# An Algorithm for Part Input Sequencing of Flexible Manufacturing Systems with Machine Disruption
## 2 Related Works
A hybrid approach was proposed for the FJS scheduling problem in dynamic environments for scheduling and rescheduling in disruption and experiments were performed for evaluation with the result of the competitiveness of the approach obtained [24]. Scheduling/rescheduling of flexible job shops were considered for machine recovery with an improved Jaya algorithm developed to minimize makespan in scheduling and to minimize both instability and makespan in rescheduling, generating the improved Jaya algorithm better than NSGAII and ISFLA in the non-dominated results [25]. Production scheduling and maintenance planning were considered in a flexible job shop in the situation of machine deterioration and a real-time system was proposed, which used a hybrid GA, an integrated model, and hybrid rescheduling policies [26]. A hybrid deep Q-network was built for dynamic FJS scheduling and for training to face disruption and experiments were performed to compare the method to scheduling rules, demonstrating the superiority of the proposed method [27]. A hierarchical-based deep reinforcement learning method was proposed for FJS scheduling and rescheduling and comparisons were made between the method and scheduling rules and other dynamic methods, demonstrating the superiority of the proposed method [28]. A flexible job shop scheduling method was proposed to consider machine breakdown and other dynamic events and to apply their dynamic event response strategy and their multi-objective model and to apply a multi-objective particle swam arithmetic optimization [29]. Even though disruption handling in FJS scheduling and in FMS scheduling has been investigated by researchers, the investigation of disruption handling in FMS part input sequencing is not seen. This paper investigates disruption handling in FMS part input sequencing. An algorithm for part input sequencing of FMSs with machine breakage is proposed.
# A Review of Recent Progress of Carbon Capture, Utilization, and Storage (CCUS) in China
## 4 Progress of CO\({}_{2}\) Utilization in China
### Development Status of CO\({}_{2}\) Geological Utilization in China
#### 4.3.2 CO\({}_{2}\)-Enhanced Natural/Shale Gas Recovery (CO\({}_{2}\)-EGR) in China
microscopic behavior of gas molecules in shale nanopores [147] as well as the macroscopic parameter optimization and technical improvements in the production process [146; 148]. [PERSON] et al. investigated the microscopic states and behaviors of CH\({}_{4}\) and CO\({}_{2}\) in typical shale nanopores using molecular dynamics (MD), grand canonical Monte Carlo (GCMC) simulations, and density functional theory (DFT). The result revealed the prevalence of competitive adsorption of CO\({}_{2}\) and CH\({}_{4}\) in nanopores of different compositions, that is, CO\({}_{2}\) adsorption ability is greater than CH\({}_{4}\) while its self-diffusion is usually weaker than CH\({}_{4}\) in the nanopores of kerogen, quartz, montmorillonite, calcite, and mixed components [147]. [PERSON] et al. determined the appropriate CO\({}_{2}\) injection parameters into shale oil reservoirs by employing numerical simulation, providing theoretical guidance for CO\({}_{2}\) injection to enhance shale gas recovery [146]. [PERSON] et al. proposed an integrated technology for the efficient development of shale gas and the geological storage of supercritical CO\({}_{2}\)[148]. With this technology, the shale was first fractured with supercritical CO\({}_{2}\) to promote the development of a more complex fracture grid, and then CO\({}_{2}\) was continuously injected to replace CH\({}_{4}\) in the reservoir to maximize shale gas recovery and geological storage of CO\({}_{2}\). Unlike the traditional shale gas production process that requires hydraulic fracturing to increase the porosity and permeability of shales, this approach's innovation lies in utilizing supercritical CO\({}_{2}\) instead of water for fracturing. According to the pilot test result of this technology in Yanchang Petroleum National Continental Shale Gas Demonstration Area, a 2.5-time increase in the daily average production from a single well was achieved, demonstrating a considerable rise in shale gas production. This innovative technology offers a new alternative for massive CO\({}_{2}\) storage while enhancing shale gas recovery and reducing water waste from the hydraulic fracturing of shales [149].
# Spatiotemporal Monitoring and Evaluation Method for Sand-Filling of Immersed Tube Tunnel Foundation
## 4 Application of Spatiotemporal Monitoring Model
### Monitoring Evaluation for Cushion Filling Results of P5 Pipe Section  
The evaluation results for the P5 pipe section from the trained neural network and the relationship between the diffusion radius of each sand-filling hole and time are shown in Figure 16. For the entire P5 pipe section, the sand-filling time required for side holes is longer, such as 12 h for sand-filling holes W1 and W2. The sand-filling time required for middle holes is shorter, such as 6 h for sand-filling hole W4. Whether it is a side hole or a middle hole, this model reflects the relationship between the diffusion radius and sand-filling time well. When the results of pressure gauge monitoring are compared with this model, the results of elastic wave monitoring are in good agreement with the pressure gauge values. The black circle in Figure 16 indicates the time when the pressure gauge suddenly increases for each sand-filling hole. It can be seen that the time when the pressure gauge suddenly increases shows a consistent trend with the results of spatiotemporal monitoring. The proposed model can evaluate the diffusion radius of the sand deposit very well, achieving the effect of time and space monitoring of sand-filling construction of immersed tunnels.  
Due to the proposed neural network model, the construction process of the P5 pipe section was smooth, and there were no abnormal phenomena, such as sand-filling hole plugging or uneven pipe section uplift. Moreover, the consecutive settlement of the pipe section was small (4 cm for six months), satisfying the design requirements and confirming the applicability of this method. Therefore, this model can provide guidance and real-time monitoring for construction.  
For researches on the sand-filling of immersed tube tunnels, model tests under different conditions were carried out to explore the law of sand deposit expansion [10]. In the study of the evaluation for the sand deposit state during construction, the P-wave impact imaging method was proposed to detect the radius and the distribution of relative compactness of sand deposits [12]. The sand deposits in both model testing and field monitoring are assumed to be with a free boundary. In the present study, a spatiotemporal monitoring method is proposed to evaluate the state of the sand deposit, and the influence of boundary conditions on the expansion of the sand deposit for different types of holes (side holes, middle holes) is analysed. Regarding the diffusion law of the sand deposit with free boundary, the analysis results of this paper show a high consistency with the related literature.  
Figure 14: Diffusion radius with the time of sand-filling hole W6.  
Figure 15: Diffusion model of sand-filling hole W6.
# A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery
## 3 Applications of FFD in Early Fault Diagnosis of Rotating Machinery
### Adaptive Decomposition Methods
#### 3.1.3 Lmd  
LMD decomposes a signal into a series of product functions (PFs). Each PF is the product of an envelope signal and a frequency modulated signal, which can be used to detect early faults. [PERSON] et al. [76] introduced LMD to decompose a signal and the instantaneous frequency was calculated from the frequency modulated signal for fault diagnosis of gearbox in wind turbines. [PERSON] et al. [77] utilized LMD to decompose a signal and the amplitude and frequency demodulation method was applied for fault diagnosis of planetary gearboxes. [PERSON] et al. [78] used LMD and instantaneous time-frequency spectrum for the early detection of gear local faults in a finishing rolling mill. [PERSON] et al. [79] utilized differential rational spline-based LMD to decompose the signal into a series of PFs, and then the selected main PF components were used to detect early faults in gears and rolling bearings. The abovementioned methods have been summarized in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Authors** & **Methodologies** \\ \hline [PERSON] et al. [71] & EEMD + similarity criterion \\ [PERSON] et al. [72] & Complementary EEMD \\ [PERSON] et al. [73] & Complementary EEMD \\ [PERSON] et al. [74] & Performance improved EEMD \\ [PERSON] et al. [68] & EEMD + tunable Q-factor wavelet transform \\ [PERSON] et al. [69] & Independent component analysis multivariate monitoring + EEMD \\ [PERSON] et al. [67] & EEMD + Hilbert Square Demodulation \\ [PERSON] et al. [70] & EEMD + adaptive stochastic resonance \\ [PERSON] et al. [75] & EEMD + multiwavelet packet \\ \hline \hline \end{tabular}
\end{table}
Table 2: Applications of ensemble empirical mode decomposition (EEMD) algorithm in early fault diagnosis of rotating machinery.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Authors** & **Methodologies** \\ \hline [PERSON] et al. [79] & Differential rational spline-based LMD \\ [PERSON] et al. [76] & LMD \\ [PERSON] et al. [77] & LMD \\ [PERSON] et al. [78] & LMD \\ \hline \hline \end{tabular}
\end{table}
Table 3: Applications of local mean decomposition (LMD) method in early fault diagnosis of rotating machinery.
# Predicting Shot Accuracy in Badminton Using Quiet Eye Metrics and Neural Networks
## 4 Methodology
### Data Collection
#### 4.3.3 Performance Measure  
Shot accuracy was measured using a point-based target system on the opponent's court. The court was divided into zones, with points assigned based on difficulty and strategic importance (e.g., 5 points for corner shots, 3 points for mid-court shots).