esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Pool-Boiling Processes Based on Bubble-Dynamics Parameters
## 6 Boiling-Heat-Transfer Coefficient h and Heat Flux q  
Boiling-heat-transfer coefficient and boiling-heat flux are the most important two parameters to measure the heat-transfer effect of the boiling process. The former determines the wall superheat at equilibrium, the latter determines the heat taken away per unit time. The traditional methods of measuring the heat flux of pool boiling mainly include the Joule effect method, and temperature gradient method and thermoelectric effect method, etc. [108] The Joule effect method utilizes the voltage and current applied to the heating element to directly calculate the heat flux, and is suitable for systems where the boiling surface area is larger [109]. The gradient method determines the boiling surface temperature gradient by measuring the temperature difference between solid layers \(\
abla T\) and obtains a linear temperature distribution under steady-state conditions, and the heat flux is calculated by [PERSON]'s law \(q=-k\
abla T\)[110; 111; 112]. The principle of the thermoelectric effect method, on the other hand, is that materials with anisotropic thermal conductivity generate an electric field with a transverse component in the main axis of the material when heat passes through it due to the Seebeck effect, thus enabling the heat flux to be obtained by detecting the electrical signal, which allows for the ultra-fast response and is suitable for transient heat flux measurements [113]. With the continuous development of Machine Learning, the image [114; 115] and acoustic signals [116] of boiling are detected in order to develop a boiling heat flux measurement system with the aid of the Convolutional Neural Networks (CNNs) [117] and Multilayer Perceptron Neural Networks (MLPNNs) [118].  
Accurate prediction equations for boiling heat transfer coefficient and heat flux can help us to predict the experimental heat transfer intensity in advance, as well as to verify the experimental results. [PERSON] [119] reviewed bubble heat transfer models of pool boiling according to transient conduction, microlayer evaporation, enhanced convection, and three-phase line mechanisms, and a large number of correlation formulae for determining boiling heat transfer coefficients according to experimental results and theoretical analyses were compiled. [PERSON] found that most of the heat transfer models were developed based on a combination of factors such as surface structure, heat flux, liquid properties, pressure, and wall temperature. [PERSON] et al. [120] also suggested that the accuracy of the equations was highly dependent on a proper and accurate mathematical description of the bubble behavior in reviewing the prediction equations for boiling heat transfer coefficients and heat flux.
# Nondestructive Surface Crack Detection of Laser-Repaired Components by Laser Scanning Thermography
## 2 Detection Principle
Several commonly used materials are selected, and their thermodynamic parameters are shown in Table 1. The distance between the circular Gaussian heat source and the crack is set as \(d=1\) mm, the spot radius of the heat source is set as \(a=1\) mm (at \(1/e^{2}\) of the intensity), the total energy of the pulse heat source is set as \(Q_{0}=300\) mJ, and the excitation time is set as 0.3 s. The crack width is set as 40 \(\upmu\)m and the thermal resistance coefficient at the crack is \(R_{th}=L/\lambda_{air}=1.74\) mm\({}^{2}\).K/ mW. At 0.05 s after excitation, the surface temperature distribution and temperature gradient of the model along \(y\)-axis are shown in Figure 3a,b, where the surface temperature gradient \(T_{G}\) is defined as:  
\[T_{G}=\sqrt{\left(\frac{\partial T}{\partial x}\right)^{2}+\left(\frac{ \partial T}{\partial y}\right)^{2}} \tag{5}\]  
It can be seen from the results that there are significant temperature changes at the crack, so the temperature gradient can be used as the characteristic quantity to describe the shape and geometric distribution of the crack. The thermal conductivity has a significant effect on the temperature field amplitude and gradient. Note that the thermal gradient amplitude of titanium alloy material is the largest, and the crack characteristics are easier to be extracted. When studying the influence of laser power, a nickel-based alloy is selected as the model material and the total energy using the model is set as \(Q_{0}=100\)-\(600\) mJ, and other parameters remain unchanged. As shown in Figure 3d, the temperature abruptness at the crack is sharper with the increase in laser power. Figure 2: A theoretical model of heat conduction excited by a circular Gaussian heat source [22; 28]. We can see that the temperature gradient \(T_{G}\) can be used as an important indicator to evaluate the detection quality, which will be further analyzed by the temperature gradient results in the following section.
# Dynamic Modeling and Output Characteristics Analysis of the Hub-Drive Reduction System
## 2 Structure of the Hub Drive Reduction System  
The three-dimensional structure of the hub drive reduction system, as shown in Figure 1, primarily consists of a drive shaft, a two-stage planetary gear reducer, a wheel hub, and a hub frame. The hub frame is connected to the vehicle chassis, with one end of the wheel hub linked to the output of the two-stage planetary gear reducer and the other end connected to the brake input shaft. One side of the drive shaft connects to the hub motor, while the other side attaches to the input of the two-stage planetary gear reducer. The two-stage planetary gear reducer comprises a primary and secondary sun gear, planet carrier, ring gear, three primary planetary gears, and four secondary planetary gears, as depicted in Figure 2.  
Figure 1: Schematic of the hub drive reduction system.  
During normal vehicle operation, the hub motor serves as the driving component, rotating the drive shaft, which then transfers power to the first sun gear of the planetary gear system through its connection. The planetary gear system performs power transmission, achieving speed reduction and torque multiplication. The secondary planet carrier acts as the output of the planetary system, driving the hub frame and the wheels, thereby powering the entire vehicle.  
The hub transmission reduction system, as the core functional component of the vehicle, has a substantial impact on the smoothness and reliability of the vehicle's operation. However, due to the complex connections and interactions among the various components, involving significant friction and contact forces, conventional theoretical models cannot provide accurate dynamic performance analysis. Therefore, this study employed multi-body dynamics analysis using ADAMS software to build the model and perform dynamic calculations. The goal was to explore the system's dynamic output characteristics and identify influencing factors.
## 3 Experiments and Analysis
### Quantitative Comparison
#### 3.5.4 TVF  
The temporal-domain method TVF performs relatively well, achieving good results across Seq.1 to Seq.6. However, it struggles to identify false alarms caused by phase shifts in complex backgrounds, which limits its overall performance, particularly evident in Seq.3 where its D-statistic surges to 1.73 \(\times\) 10\({}^{-3}\) (over 10\(\times\) higher than other sequences), primarily due to TVF's insufficient temporal sensitivity in target perception and ineffective suppression of temporal clutter generated by image-shifting terrain structures adjacent to targets.
# A Reliable Publish-Subscribe Mechanism for Internet of Things-Enabled Smart Greenhouses
## 3 Research Methodology
### Proposed Anatomy of SGP MQTT Topic
Our proposed architecture establishes a standard configuration method in MQTT for sending and receiving crucial greenhouse data and events. The introduced approach classifies sensor data into topic-specific categories, facilitating the control of actuators and ultimately reducing the message load on the primary server. It is imperative to conduct thorough compatibility assessments with commonly used hardware and software configurations in order to guarantee seamless integration into existing IoT ecosystems within intelligent greenhouse environments. This includes designing and integrating hardware components like microcontrollers and wireless communication modules to support the SGP MQTT protocol, enabling the control of actuators and acquisition of sensor data through output and input controllers such as relays and analog-to-digital converters. Software configuration guidelines are crucial for configuring MQTT brokers or implementing new ones to accommodate the structured message format of SGP, detailing necessary software libraries, APIs, or configuration settings. Optimizing data flow involves explaining how sensor data travel through the SGP MQTT protocol to reach the primary server or cloud platform efficiently, ensuring timely transmission of critical greenhouse data. Furthermore, ensuring interoperability with diverse IoT devices and platforms is paramount, emphasizing adherence to standards and compatibility across various vendor solutions.
## 6 Experimental Evaluation
### Results on Data Reconstruction  
The \"Notice\" data contains 10,700 alarms related to malfunction and/or breakdown. By checking the technical site of each notice, it emerged that 10,567 assets had a missing or incomplete technical site. Consequently, it was necessary to define algorithms or completion rules. As already mentioned above, there are five of these rules, and for each of them, we report in Table 11 the number of technical sites that have been augmented. The reader notices that the same technical site may have been augmented by more than one strategy.  
The number of technical sites that have been augmented is 7204 over a total of 10,700. This corresponds to having reconstructed 68.2% of the input samples.  
The reconstruction results were manually validated in collaboration with the employees of ITALTELECO, who labeled as correct 100% of the performed augmentations. The reader notices that it has not been possible to automatically reconstruct a higher number of technical locations due to a lack of information necessary for their reconstruction. The lacking of a structured process when filling the notice reports led to several missing data.
# FEA for Optimizing Design and Fabrication of Frame Structure of Elevating Work Platforms
## 3 Results  
### Criteria Analisys for Evaluation  
#### 3.1.1 Comparative Analysis of Material Performance  
The Finite Element Analysis (FEA) revealed significant differences in structural performance among the four materials--Mild Steel (A36), High-Strength Low-Alloy (HSLA) Steel, Aluminum Alloy (6061-T6), and Fiber-Reinforced Polymer (FRP). Table 4 summarizes the results, including maximum von Mises stress, deformation, factor of safety (FOS), and estimated fatigue life under cyclic loading [15; 16; 17].  
The Aluminum Alloy arms were modeled with the same geometry and dimensions as the steel arms to ensure a consistent comparison of performance and stress distribution across materials, eliminating geometric variables that might otherwise affect the interpretation of material influence.  
These results provide a solid basis for targeted geometry optimization, highlighting materials that exhibit lower stress concentrations and deformations, and pointing toward potential refinements--such as increasing local thickness in high-stress areas or optimizing cross-sections--to balance weight and strength effectively. Importantly, the analysis was conducted at the fully extended length of the telescopic arm, which represents the worst-case scenario for bending moments and shear stresses. This configuration is critical for design evaluation. If geometry were modified to adapt to different materials, the stress distribution would indeed change, suggesting that future studies should investigate geometry optimization strategies that leverage the unique properties of each material, potentially leading to lighter yet equally strong designs.
## 5 Discussion
### Significance of Hydrocarbon Exploration  
#### 5.2.1 Physical Properties of the Reservoir Rock  
Carbonate rocks are considered to be high-quality reservoirs for oil and gas exploration [1]. In the study area, the reefs are mainly distributed in the western and southwestern slopes of the Xisha uplift, with a spatial distribution over 1000 km\({}^{2}\). The point reefs and platform-edge reefs flourished on east of the Huaguang Sag during the Middle Miocene and were regularly arranged in space to form a spectacular group of reefs, as shown in Figure 11A-C. The single-point reef is about 1 km\({}^{2}\), and its thickness is over 50 m, providing an ideal space for oil and gas accumulation, as shown in Figure 12. In addition, platform-edge reefs are also well developed north of the Zhongjiannan Basin. Most of those reefs show predominantly lateral growth, with a single reef over tens of square kilometers that primarily developed on the hanging wall of the fault.  
During the diagenetic process, these reefs experienced multiple cycles of growth-exposure-regrowth-submergence-death with the sea level fluctuation, further increasing the carbonate rocks' secondary porosity. Several drilling wells on Xisha Island provide valuable data on the reservoir properties. The porosity test results of Well XK-1 show that the average porosity of the Meishan Formation and Huangliu Formation carbonate rocks exceeds 25%, and the average permeability exceeds 500 \(\times\) 10\({}^{-3}\) um\({}^{2}\)[31], which is beneficial for hydrocarbon reservoirs.
## 2 Materials and Methods
### Signal Separation Metrics
#### 2.6.2 Calculation of the Accuracy of Signal Classification by Entropy Features  
A Support Vector Machine (SVM) was used to classify the signal using one and two entropy features.  
Two steps were involved in evaluating the classification metric.  
* In the first stage, hyperparameters were selected using Repeated K-Fold cross-validation (RKF). In order to accomplish this, the original dataset was divided into \(K=5\) folds in \(N=5\) ways. The K-folds of each of the \(N\) variants of partitions were filled differently with samples. In addition, the distribution of classes in each K-fold approximated the distribution in the original dataset. Next, the classifier hyperparameters were selected at which the average accuracy of the classifier on the validation set was maximized. As a result of using a large number of training and validation sets, repeated K-Fold cross-validation allows minimize the overfitting of the model.
* As a second step, we used the hyperparameter values obtained in the first stage and performed RKF cross-validation (\(K=5\)) in a similar manner to the first stage, but using different \(N=10\) partitions. As a result, the \(A_{\text{RKF}}\) is calculated by averaging \(N\) partitioned scores. We used \(A_{\text{RKF}}\) as a metric to assess signal separation.  
Figure 6: Examples of signals (\(x_{1},\ldots,x_{300}\)) for pair B (\(r=1.7161\) and \(r=1.7551\)).
# CDA-Net for Automatic Prostate Segmentation in MR Images
###### Abstract  
Automatic and accurate prostate segmentation is an essential prerequisite for assisting diagnosis and treatment, such as guiding biopsy procedures and radiation therapy. Therefore, this paper proposes a cascaded dual attention network (CDA-Net) for automatic prostate segmentation in MRI scans. The network includes two stages of RAS-FasterRCNN and RAU-Net. Firstly, RAS-FasterRCNN uses improved FasterRCNN and sequence correlation processing to extract regions of interest (ROI) of organs. This ROI extraction serves as a hard attention mechanism to focus the segmentation of the subsequent network on a certain area. Secondly, the addition of residual convolution block and self-attention mechanism in RAU-Net enables the network to gradually focus on the area where the organ exists while making full use of multiscale features. The algorithm was evaluated on the PROMISE12 and ASPS13 datasets and presents the dice similarity coefficient of 92.88% and 92.65%, respectively, surpassing the state-of-the-art algorithms. In a variety of complex slice images, especially for the base and apex of slice sequences, the algorithm also achieved credible segmentation performance.  
image segmentation; prostate MR image; CDA-Net; ROI extraction; sequence correlation processing; self-attention mechanism +
Footnote †: journal: Medical
## 5 Impact of the Scattering Coefficient on \(T_{20}\)  
Four spaces were selected to compare \(T_{20}\) values. The selected spaces included the perfect diffusion reverberation chamber, the rectangular space, a space with adjusted walls, and a space with adjustments to both walls and ceiling. In the four spaces, the surface scattering had three states (the surface scattering coefficient was 0.01, 0.3 and 0.7). Figure 6 shows the calculation results.  
Figure 6: Variations in \(T_{20}\) with a scattering coefficient in different spaces Note: A—rectangular space, B—single sidewall tilted, G—single sidewall and ceiling tilted, Re Chamber—reverberation chamber.  
As shown in Figure 6, the \(T_{20}\) varied in accordance with the scattering coefficient across the four spaces. As the scattering coefficient increased, the \(T_{20}\) decreased in three spaces (apart from the reverberation chamber); the \(T_{20}\) change range was different across the four spaces. Space A had the largest change range. The change ranges of Spaces B and G were smaller than Space A. When the scattering coefficient was low (0.01), the \(T_{20}\) differed most among the four spaces. Specifically, the \(T_{20}\) in Space A was approximately 1.23 times that in the reverberation chamber, while the \(T_{20}\) in Spaces B and G were approximately 1.17 times that in the reverberation chamber. As the scattering coefficient increased, the difference in \(T_{20}\) across the four spaces gradually decreased. When the scattering coefficient was 0.3, the \(T_{20}\) in Spaces A, B and G were approximately 1.05 times that in the reverberation chamber. When the scattering coefficient increased to 0.7, the \(T_{20}\) was basically the same across the four spaces.  
The above analysis shows that, under the same surface scattering coefficient, \(T_{20}\) varied in accordance with spatial shapes. When the surface scattering coefficient was low, the \(T_{20}\) varied significantly. The inherent diffusivity of each space obviously affected the \(T_{20}\). The more parallel surfaces existed within a space, the lower the diffusivity of that space, and the more significantly the \(T_{20}\) varied. As the surface scattering coefficient increased, the inherent diffusivity of each space affected the \(T_{20}\) less significantly, and the \(T_{20}\) in all other spaces tended to be consistent with that in the reverberation chamber.
## 2 Materials and Methods
### Test Data Processing Method for Pull-Out Test  
According to the experiment design, one characteristic pull-out load-slip curve of steel fiber was got from the four tested pull-out load-slip curves. The test data is processed according to the specification of China code CECS 13 [33], the procedure is presented in Figure 6.  
Take the pull-out test of HF6 as example. Four test curves are presented in Figure 7a with their peak loads and corresponding slips. As the load difference was within 15% of the average, all four test curves are valid. The average peak load is 1439.8 N with a corresponding slip of 1.246 mm. As presented in Figure 7b, the normalized curve of each curve is obtained with load and corresponding slip divided by the peak load \(P_{\text{max}}\) and corresponding slip \(s_{\text{p}}\), respectively. The slip ratio \(s/s_{\text{p}}\), corresponding to the same value of load ratio \(P/P_{\text{max}}\), is averaged to obtain the average normalized ascending segment of the valid curve. The load ratio \(P/P_{\text{max}}\), corresponding to the same slip ratio \(s/s_{\text{p}}\), is averaged to obtain the average normalized descending segment of the curve. Then, the average normalization curve is obtained. At last, as shown in Figure 7c, the characteristic pull-out  
Figure 5: Detailed test devices: (**a**) testing machine; (**b**) pull-out test devices.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Fiber Type**} & **Embedded** & **Anchored** & & **Embedded** & **Anchored** \\  & **Length** & **Length** & **Length** & **Length** & **Length** \\  & **\(l_{\text{fem}}\) (mm)** & \(l_{\text{ff}}\) (mm)** & & & \(l_{\text{fem}}\) (mm) & \(l_{\text{ff}}\) (mm) \\ \hline HFb & 11 & 17.8 & HF2 & 12 & 18 \\ HF3 & 12 & 37.6 & HF4 & 10 & 27 \\ HF5 & 15 & 35.0 & HF6 & 26 & 33 \\ CF & 12 & 25.8 & CF2 & 11 & 25.5 \\ IF1 & 11 & 20.3 & IF2 & 12 & 19 \\ IF3 & 10 & 25.5 & IF4 & 8 & 15.0 \\ MF & 10 & 20 & LF & 20 & 31.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The embedded length and anchored length of steel fibers in specimens.  
load-slip curve of HF6 is obtained by multiplying the average normalized curve with the average of the peak load and corresponding slip, respectively.  
Figure 6: The procedure of determining the characteristic pull-out load-slip curve.  
Figure 7: _Cont_.
## 2 Basic Notions and Preliminary Results
### Quantizer Description
#### 2.2.2 Hysteresis Quantizer  
The hysteresis quantizer employed is described in the following form, similar to [36].  
\[q_{h}(u)=\begin{cases}\begin{array}{ll}u_{i}sgn(u)&\frac{u_{i}}{1+\delta}<|u| <u_{i},\dot{u}<0,or\\ &u_{i}<|u|\leq\frac{u_{i}}{1-\delta},\dot{u}>0\\ \\ u_{i}(1+\delta)sgn(u)&u_{i}<|u|\leq\frac{u_{i}}{1-\delta},\dot{u}<0,or\\ &\frac{u_{i}}{1-\delta}<|u|\leq\frac{u_{i}}{1-\delta},\dot{u}>0\\ \\ 0&0\leq|u|<\frac{d}{1+\delta},\dot{u}<0,or\\ &\frac{d}{1+\delta}\leq|u|\leq d,\dot{u}>0\\ \\ q(u(t^{-}))&\dot{u}=0\end{array}\end{cases} \tag{3}\]  
where \(i=1,2,...,N\), the parameters \(d>0\) and \(0<\rho<1\) determine the quantization density of \(q(u)\), \(\delta=\frac{1-\rho}{1+\rho}\), \(u_{i}=\rho^{1-i}d\). \(q(u)\) is in the set \(U=\{0,\pm u_{i},\pm u_{i}(1+\delta)\}\) and \(q(u(t^{-}))\) denotes the status prior to \(q(u(t))\). For this kind of quantizer, some detailed discussions about its parameters and hysteresis mechanism can be found in [35].  
The hysteresis quantizer can be considered to be an extension of the logarithmic quantizer, it can be seen as a combination of two logarithmic quantizers with the same coarseness but different quantized values. Thus, control law, which adopts a logarithmic quantizer, can be also applied to the hysteresis quantizer, and vice versa. In the rest part of the paper, we only consider the logarithmic quantizer for simplicity. However, with the additional quantized values, a hysteresis quantizer can avoid chattering phenomenon. The detailed discussions can be found in [37].