esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions
### Discussion  
#### 4.4.1 Thermo-Mechanical Response Behavior of the FRPC Pile  
The phase change process of the PCM near the heat exchange pipe was completed within 0.1 h (see Figure 7). The liquid phase distribution at 0.5 h indicated that the phase change inside the pile was completed more quickly than that on the outer surface because the surface of the pile exchanged heat with the soil. At 1 h, the phase transition of the pile was almost complete, and the PCM existed in a liquid phase, which further reduced the FRPC pile's thermal conductivity and led to a further decrease in the heat power of the phase change pile in the later stage.  
Figure 8 shows that at the end of heat transfer, the boundary of the model box on the side of the FR pile intersected with the isotherm of 25 \({}^{\circ}\)C. The isotherm in the center of the two piles also overlapped, which suggested a boundary effect in the laboratory test, the same phenomenon was also observed in the study of [PERSON] et al. [42]. The thermal influence radius was about 0.5 times the pile diameter at 5 h of operation and about 3.5 times the pile diameter at the end of operation, producing strong thermal interference between piles and affecting the heat transfer capacity.  
The results of Figure 9d-f show that the increase in flow rate had a greater impact on the initial peak heat power. When the heat transfer was gradually stabilized, the benefit of flow increase was greatly reduced due to the decrease in temperature difference between the components. Taking the FR pile as an example, when the flow rate increased from 1500 to 3000 mL, the maximum and minimum heat power increased by 34.8% and 8.7%,  
Figure 12: Comparison of side friction between the FRPC pile and the FR pile after continuous heating for 140 h at a flow rate of 1500 mL/min.  
respectively. When the flow rate increased from 3000 mL to 4500 mL, the maximum and minimum values of heat power increase became 30.7% and 1.3%. Correspondingly, the quantity of heat increase caused by the increase of two flows was 12.9% and 2.0%, respectively. Therefore, there is an optimal economic flow rate to balance the system's energy consumption and heat power.  
Figure 10 shows that the expansion at the top of the FRPC pile was greater than that of the FR pile. However, it is worth noting that the numerical model used in this study did not consider the foundation consolidation but only the influence of the pile-side friction resistance. The addition of phase change materials had a positive effect on the mechanical properties of the pile, especially the tensile strength. As shown in Figure 11, it is worth noting that both types of piles experienced tensile stress at the end of the heat exchange. However, there were both tensile and compressive stresses in the FRPC pile, which indicated that the deformation of the FRPC pile was not uniform along the vertical direction during the heat exchange process. Therefore, engineering designs should pay attention to the bearing capacity of energy piles.
## 2 Methods  
### Theoretical Analysis of Wavefront Coding Systems  
The DoF in object space corresponds to the depth of focus in image space. Consequently, a wavefront coding system that extends DoF in the object space is insensitive to the defocus in the image space. A cubic phase mask is a classic example applied in wavefront coding systems to extend the DoF. The one-dimensional cubic phase mask \(f(x)\) is analyzed as an example for simplicity, which can be described as  
\[f(x)=\alpha x^{3}, \tag{1}\]  
where \(x\), in the range of \(-1\) to \(1\), is the coordinate of the phase mask and \(\alpha\) is the cubic parameter of the phase mask. The pupil function, which ignores all optical aberrations but defocus, can be described as  
\[p(x)=\exp[j(f(x)+\phi x^{2})] \tag{2}\]  
where \(\phi\) is the defocus aberration and \(j\) is the imaginary unit. The modulation transfer function (MTF), an image quality evaluation method, is used to evaluate the defocus insensitivity [9] of wavefront coding systems. MTF curves with different defocus aberrations will be coincident if the wavefront coding system is ideally defocus-insensitive. Consequently, the coincidence of MTF curves with different defocus aberrations is used as a merit function to evaluate the defocus insensitivity of the system.  
\[s(\alpha)=\min\iint\big{[}MTF(\alpha,u,\phi)-MTF(\alpha,u,\phi_{0})\big{]}^{2} dud\phi \tag{3}\]  
where \(u\) is the spatial frequency and \(\phi_{0}\) is zero. However, wavefront coding systems suffer from a trade-off between defocus insensitivity and imaging quality [10]. To ensure imaging quality, the system should satisfy  
\[t=\iint MTF(\alpha,u,\phi_{0})dud\phi\geq\mathrm{T} \tag{4}\]  
where the threshold T is 0.15 in this study [11].
## 2 Materials and Methods
### Multivariate Data Analysis and Model Evaluation  
The regression models were established using partial least squares regression (PLSR) and least squares support vector machine (LS-SVM) algorithms, respectively. PLSR is a multivariate statistics-based linear regression method that is widely used to establish regression models [34; 35]. The main principle of PLSR is to extract orthogonal factors of latent variables (LVs) and to establish regression relationships between the data set and the corresponding reference values. LVs are obtained by decomposing both independent and response variables. LS-SVM is a classical non-linear regression method for the computation of small sample data [36; 37]. It uses a radial basis function kernel to map the input features into a high-dimensional feature space, thus converting a linear non-differentiable problem into a constrained quadratic programming problem. LS-SVM can search for potential patterns in the data and use the found patterns to predict the unknown data. On the other hand, for the establishment of classification models, the present study did not use the PLSR algorithm but the partial least squares discriminant analysis (PLS-DA) algorithm. The PLS-DA algorithm encodes the dependent variables of PLSR by means of dummy variables, thus enabling the description of different categories. The reference values of the dependent variables were set to -1 and 1 for intact and impact-damaged strawberries, respectively. For the LS-SVM algorithm, it also used \(-\)1 and 1 to represent intact and impact-damaged strawberries, respectively, and then built the classification models. The specific needs for building regression and classification models can be achieved by choosing different functions for the LS-SVM calculation.  
For the accuracy of all regression models in this study, including the prediction of the extent of impact damage and the time of occurrence of the impact, they were evaluated mainly by the correlation coefficient of calibration (Rc), root-mean-square error of calibration (RMSEC), correlation coefficient of prediction (Rp), root-mean-square error of prediction (RMSEP), residual predictive deviation (RPD), and the absolute difference between RMSEC and RMSEP (AB_RMSE). Additionally, for the accuracy of the classification models, which was the identification of strawberries with impact damage, it was mainly evaluated by the correct rate, which was the ratio of the number of correctly identified samples to the total number of samples in the model. The best model and its corresponding best characteristic variable were determined based on the above indicators. All calculations for the multivariate data analysis were performed on MATLAB 2017b software (The MathWorks Inc., Natick, MA, USA).
## 3 Results
### Gravity Gradient Data Denoising Results  
The gravity gradient tensor contains multiple components, reflecting information about the shape and orientation of anomalous bodies. Based on the underground object and grid constructed in the previous section, three components of the gravity gradient were calculated. Different levels of Gaussian noise were added to this gradient data and denoising was performed using the U-Net network and traditional methods. The corresponding Figure 13 shows the results of different processing methods for the three-dimensional tensor components (Txx, Tyy, Tzz). Each row in the figure corresponds to one diagonal component (Txx, Tyy, Tzz) of the tensor, and each column corresponds to a denoising or data processing method, including U-Net, moving average, low-pass filter, and wavelet.  
Figure 12: Comparison of ablation experiment effects.  
Comparison results indicate that while the moving average and low-pass filter methods also achieve some noise suppression, they have poorer preservation of details and result in blurred boundaries. In contrast, the improved U-Net method effectively suppresses noise while better preserving the main structural information, resulting in smoother processed outcomes.  
Figure 14 presents a comparative analysis of the PSNR values achieved by different denoising methods under various noise levels. When the noise level is 5%, the PSNR of the U-Net reaches 59.13 dB, which is significantly higher than that of the unprocessed data (41.31 dB) and other methods. As the noise level increases, the advantage of the improved U-Net network becomes even more pronounced. At a noise level of 3%, the improved U-Net still maintains a PSNR of 48.82 dB, whereas the performances of the wavelet transform and low-pass filtering methods decline markedly. Although the moving average filter performs relatively well at lower noise levels, its PSNR also drops significantly as the noise intensity increases. Experimental results demonstrate that the improved U-Net architecture can effectively maintain image quality under various noise conditions, highlighting its robustness and denoising capability.  
Figure 13: Denoising performance of gravity gradient data using different methods.
# Low Temperature Chemoselective Hydrogenation of Aldehydes over a Magnetic Pd Catalyst
## 3 Results and Discussion
### Substrate Scope of the Reduction of Aldehydes to Alcohols
The results were shown in Figure 2. The hydrogenation rate of the substituted benzaldehyde showed a reasonable linear Hammett correlation and a Hammett value of 1.59 was obtained, indicating that the reduction of the aldehyde group to a hydroxy group was substituent-sensitive and proceeded via an anionic intermediate [30,31]. According to the results of Hammett equation, We predicted the reaction mechanism as follows: (1) hydrogen molecular cleaved by heterolysinin the presence of catalyst afford a H' and a H'; (2) the H' reacted with aldehydes and gave the anionic intermediate PhCH\({}_{2}\)O'; (3) PhCH\({}_{2}\)O' reacted with proton and give the product PhCH\({}_{2}\)OH. Figure 2: Correlation study for catalytic reduction of various aromatic aldehydes to the corresponding aromatic alcohols. Reaction conditions: substrate (1 mmol), \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)@HAP-Pd (10 mg)catalyst, 25 \({}^{\circ}\)C, H\({}_{2}\) (10 bar), H\({}_{2}\)O (10 mL).
Therefore, the current study aims to evaluate the potential of using oxygenated fuels, like OMEx, in combination with novel bowl designs, like radial lips, to reduce in-cylinder soot formation. For this purpose, several blends of OMEx and Diesel (0%, 30%, and 50% of OMEx) have been tested combined with a radial-lips bowl piston and a reentrant bowl piston. In addition, the reduction of oxygen content inside the combustion chamber (15% O2) was evaluated to simulate EGR conditions, typically to reduce NOx formation. The work applied 2-colour pyrometry and high-speed excited state hydroxyl (OH*) techniques in an optical single-cylinder CI engine to trace the soot formation and oxidation processes. The main result shows that at low in-cylinder oxygen conditions, the radial-lips piston combined with the OMEx blend significantly improves the soot reduction inside the combustion chamber for the whole event. Moreover, this effect is more noticeable with the increase of OMEx.
## 4 Proposed Trust-Based Optimized Reporting Scheme
### Trust Calculation Mechanism
#### 4.2.4 Forgetting Curve  
Unlike direct trust, the computation of indirect trust relies on the feedback provided by the recommenders to the source node. As illustrated in Figure 3, nodes C and D provide their feedback regarding node B to the trustor node A. Node A receives separate feedback based on \(\mathrm{T_{e}}\) and \(\mathrm{T_{u}}\). The trust value for \(\mathrm{T_{s}}\) is evaluated at node A once it receives all the feedback.  
A forgetting curve/time decay function is employed based on [28], prioritizing the consideration of recent trust scores for the specific node over old feedback received by the trustor. The forgetting function is crucial for authentic trust evaluation, as it gives greater prominence to the recent behavior of the targeted node.  
Figure 3: Indirect trust computation: nodes C and D contribute feedback about node B to node A.  
As depicted in Figure 3, node A receives numerous feedback values from nodes C and D concerning node B. Node A assesses the credibility of trustee node B using a fixed number of feedback values. When the number of feedback values equals the predefined threshold, the calculated trust values are filtered out. Node A continues to propagate the previously computed total trust for node B to the GLN. The operation of the forgetting function for both direct and indirect trust calculation is outlined in Algorithm 3.  
```
input:t--Direct trust or Indirect trust values,c--Counter,n--node\({}_{\text{id}}\) output: Setting the priority of recent test scores function forgettingCurve(t,c,n) : ifc\(|\)n == threshold then \(\text{t}_{\text{root}}(\text{t}[n])\) \(\text{t}[n]=0\) \(\text{c}[n]=0\) endif ifc\(|\)n \(<\) threshold then \(\text{t}_{\text{root}}(\text{t}[n])\) endif return
```  
**Algorithm 3**Forgetting Curve  
Algorithm 3 involves prioritizing recent trust scores in the system. It considers inputs like direct or indirect trust values, counters, and specific node identifiers. If the counter for a node reaches a defined threshold, it invokes a function to update the trust value at the root node. This process resets the trust and counter values for that node. Alternatively, if the counter is below the threshold, it still triggers the function to update the root node with the current trust value for the node under consideration. Finally, the algorithm ensures that trust updates are sent to the root node based on defined thresholds for ongoing network trust assessment.
## 4 Results  
### Thresholds Analysis in Cultivated Land Mapping  
We used the decision tree method to identify cultivated land from the Yuecheng Reservoir catchment area. By fully utilizing the advantages of each index, each level of the decision tree removes a particular type of ground object through index calculations. Finally, the distribution of cultivated land is obtained at the last level of the decision tree. First, NDWI was used for water identification. Vegetation was separated from water, impervious surface, and bare land by the NDVI. In this work, the challenge lies in distinguishing cultivated land from other vegetation. Therefore, we analyzed the differences between cultivated land and other vegetation under different vegetation indices. The results showed that the VIUPD value of shrubland and grassland was significantly different from the VIUPD values of forest land and cultivated land. The RVI separated forest land and cultivated area. To determine the thresholds, we selected 50 samples of each type of ground objects and evaluated the calculation results of their indices. Figure 9 shows good discrimination among the various ground objects under specific indices.  
Figure 9: Box diagram of remote sensing index of typical surface features. (**a**) NDWI can distinguish water from other surface features when the threshold is 0. (**b**) NDVI synthesized by the annual maximum  
value can distinguish impervious surface and bare land from vegetation when the threshold is 0.4. (**c**) VIUPD index (mainly including cultivated land, forest land, shrubland, and grassland) can distinguish shrubland and grassland from the other two types of land when the threshold is 0.6. (**d**) RVI statistical value of cultivated land and forest land samples demonstrates good separation when the threshold is 8.5. Finally, the distribution map of cultivated land is obtained (the yellow boxes show the indices values of identified ground features, and the green boxes show the indices values of other land use types).
# Multiparameter Determination of Thin Liquid Urea-Water Films
## 4 Results
### Temperature and Concentration Measurements  
In the case of the temperature and concentration measurements, the absorbances of the test set are required at all three wavenumbers, \(\
u_{1}\) = 4253 cm\({}^{-1}\), \(\
u_{2}\) = 4808 cm\({}^{-1}\) and \(\
u_{3}\) = 4501 cm\({}^{-1}\). These values are extracted and subsequently transformed into the \(\varphi\)-\(\theta\) space. Then, the respective gradients mT and mc are calculated based on the known intersection point coordinates PT for the temperature determination and Pc for the concentration. From the calibration performed beforehand with the calibration set, the linear fit coefficients are also known, and Equations (4a) and (4b) can be solved to gain the parameters of interest. Figure 12a shows the temperatures set in the calibration cell as red circles and the calculated values as black crosses, while Figure 12b depicts the true concentrations (red circles) and the respective evaluation results (black crosses). In both cases, good results can be achieved. The RMSE value for the temperature measurements amounts to 0.5 \({}^{\circ}\)C with a maximum absolute deviation of 1 \({}^{\circ}\)C. In terms of concentration, the RMSE value is 0.13%, and the maximum absolute deviation amounts to 0.18%.  
Figure 11: Film thickness of test set. Red circles: true, predefined film thickness. Black crosses: calculated film thickness with calibration at \(\
u_{1}\) = 4253 cm\({}^{-1}\).  
The uncertainty of the type K thermocouple that was used in the experiment amounts to \(\pm\)1.5 K, which can be increased by aging effects. With regard to the concentration of urea in water, the uncertainty of the mixing procedure that arises from the measurement of the liquid and solid components is within \(\pm\)0.1%, while the uncertainty of the commercially available product is within \(\pm\)0.7% such that an overall uncertainty can be estimated at well below \(\pm\)1%.
## 2 Materials and Methods
### Physionet MIMIC II Database for Statistical Analysis  
The raw ECG signals after conditioning had to be converted a format that was recognized by the WFDB tool from Physionet for further analysis [8; 43]. The WFDB library was installed on BBB, and software utilities from the library were used to convert the ECG waveforms to a WFDB format. The Multiparameter Intelligent Monitoring in Intensive Care (MIMIC II) Waveform Database is one of the two MIMIC II Databases. The waveform database contains several thousands of records of time series that have been digitized using physiological waveforms and simultaneously recorded Numerics (vital signs) signals of physiologic measurements. Some records also contain alarm annotations and signal quality indexes. The waveforms could be visualized online or by using a WAVE toolkit [44]. Physionet is a widely used resource for complex physiologic signals, which was created for the Research Resources of the National Institutes of Health (NIH), to instigate the study of cardiovascular and other complex biomedical signals. WFDB is a major component in the PhysioToolkit, and it has about 75 applications for signal processing and automated analysis [45]. For the current research experiment and data analysis, however the MIMIC II waveform and Numerics database was used. The database contains physiologic signals and vital signs in a time series format that have been captured from patient monitors for tens of thousands of ICU patients. Data were collected from a variety of ICU admissions varying from (medical, surgical, coronary care, and neonatal) related admissions. The MIMIC II Clinical Database contains clinical data from bedside monitors, and written notes taken by doctors and nurses. The MIMIC II Waveform Database includes records of high-resolution physiologic waveforms and minute-by-minute numeric (vital signs) time series (trends) of physiologic measurements. Waveform Database records are matched to the corresponding clinical database [44; 46]. The recorded waveforms and Numerics (vital signs) include one or more ECG signals, and often include arterial blood pressure (ABP) waveforms, fingertip PPG signals, and respiration rate signals, with additional waveforms depending on the patient condition that is being monitored. Numerics (vital signs + physiological signals) typically include heart and respiration rates, SpO2, and systolic, mean, and diastolic blood pressures. The recording lengths also vary from a few days in, to several weeks long. Waveform signals in MIMIC II/III were captured at 125 samples/s and consisted of ECG, PPG, RESP, and BP signals [44; 46], amongst others. For calculating the trauma and injury severity scores, HR, RR, SysBP, SpO2 scores were used along with age. The signals were read into the MATLAB environment and were sampled at a sampling interval of 1.024 s.
## 4 Conclusions  
This experiment mainly compares and investigates the effects of different solid active agents, liquid active agents, and solid-liquid mixing on the laser cleaning effect, ensuring that the laser parameters remain unchanged and only changing the height of the additive amount to explore the effect of the size of the additive amount on the cleaning result and the subsequent surface roughness as well as the depth of removal is analyzed. The conclusions are as follows:  
1. A 0.1 mm water layer height achieves the maximum removal depth of 37 \(\upmu\)m, and when the water layer is at a height of 0.2 mm, the laser cleaning mechanism under water medium is ablative, with a surface roughness of approximately 0.8 \(\upmu\)m.
2. SiO\({}_{2}\) active agent at a height of 0.1 mm obtains a cleaning depth of 37 \(\upmu\)m and surface roughness of 1.1 \(\upmu\)m, and SiO\({}_{2}\) plays an auxiliary role in the laser cleaning process.
3. The combination of solid and liquid mixtures can integrate the advantages of both, resulting in an improved surface roughness and increased depth of removal.
4. Under medium conditions, the microhardness values obtained from laser cleaning are lower than those from direct laser irradiation, with the surface hardness after SiO\({}_{2}\)-assisted cleaning being greater than that after water-assisted laser cleaning.  
Software, K.X.; validation, R.C.; investigation, L.L.; writing--original draft, K.C.; supervision, J.L. All authors have read and agreed to the published version of the manuscript. This research was funded by Class III Peak Discipline of Shanghai--Materials Science and Engineering (High-Energy Beam Intelligent Processing and Green Manufacturing.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.