esa-sceva/satcom-chunk-collection huggingface dataset:
# Explainable Machine Learning Model for Chronic Kidney Disease Prediction
## 4 Results
### Performance Evaluation  
In this study, we propose an ML model for the effective prediction of CKD. The dataset that we utilized was taken from the UCI ML repository, and it has a considerable number of values that are missing. KNN imputation was applied for numerical values, and mode imputation was used for categorical values. Subsequently, we scaled the data using min-max scaling. The dataset comprises 24 predictive features, and we used the SelectKBest method with mutual information score to select the 12 most important features, ensuring that our model is well-formed and easy to use. Our model was trained using a two-hidden layer MLP model. Two hidden layers are present: one with 59 neurons and the other with 94 neurons. We used the Adam solver and the ReLU activation function. The data were divided into 75% for training and 25% for testing. Accuracy, precision, recall, and F1-score were among the metrics used to evaluate the model.  
To compare our proposed model, we also implemented other ML models such as ridge classifier, SGD Classifier, Bernoulli NB, logistic regression, Gaussian NB, random forest, and decision tree using the Scikit-learn library. The accuracy, precision, recall, and F1-score, among other performance indicators, are displayed in Table 4 for both our proposed model and the other models. We achieved 100% accuracy, precision, recall, and F1 score with our proposed model, which is superior to others. Figure 6 visually illustrates the performance comparison between the proposed model and other models.  
To further evaluate the performance of our model, we calculated the area under the ROC curve (Figure 7) and the precision-recall curve (Figure 8). Our proposed model outperformed the others, achieving a score of 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Ridge classifier & 95 & 89.47 & 97.14 & 93.15 \\ SGD Classifier & 99 & 100 & 97.14 & 98.55 \\ Bernoulli NB & 93 & 86.84 & 94.38 & 90.41 \\ Logistic Regression & 98 & 97.14 & 97.14 & 97.14 \\ Gaussian NB & 96 & 89.74 & 100 & 94.59 \\ Random Forest & 98 & 100 & 97.28 & 97.05 \\ Decision Tree & 96 & 94.28 & 94.28 & 94.28 \\ The Proposed Model & 100 & 100 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The performance metrics of various models.  
Figure 6: The performance comparison of various models.
## 4 Simulation Experiment and Results
### Case 1: Simple Scenario
#### 4.2.1 Simulation Results
From Figure 9A, it can be seen that the velocity variation during the motion of the cabin pushing in the simple scenario can be divided into three stages: acceleration, maximum velocity motion, and deceleration, conforming to the actual motion situation and more stable than the results of RPM planning. From Figure 9B, the 0-25 s orientation angle changes rapidly to adjust the movement direction, fine-tune the angle to avoid obstacles during the maximum  
Figure 8: Path planning results derived using the simple scenario. (A), (B) The collision of the path planned by RPM. Figure 7: Lengths of paths planned by different algorithms in the simple scenario. velocity motion, and gradually adjust to the specified direction to meet the target pose requirements when approaching the target position, which is more in compliance with the actual situation compared with the RPM planning results.
## 4 Results
### Data of Bands with a Spatial Resolution of 20 m  
#### 4.2.1 Multivariate Analyses  
As in the previous subsection, the results of the multivariate analyses performed with the StatGraphics Centurion XVIII [23] are presented in Figure 6. It is important to note that the pixel value of bands included in the previous subsection does not match the values in this subsection. For these analyses, we have used bands characterized the pixel value with a resolution of 20 m; there are more available bands for this spatial resolution. As in Figure 3, in Figure 6 the dispersion and the correlation matrix can be seen. Nonetheless, in this case, we have included only the bands that correlate with the moisture since the complete table (with all the bands) is too big and provides too much useless information. As in the previous subsection, we can identify in Figure 6a the results of including all soil types, in Figure 6b the results for sandy soil, and in Figure 6c the results for clay soil. No correlations are obtained for sandy-clay soil.  
The most interesting results are linked to clay soil. The information contained in the satellite bands 4, 5, 11, and 12 presents a strong negative correlation with soil moisture, while; band 7 has a strong positive correlation. Other bands present correlation but with lower significance. For the other types of soil, no strong correlation (with high significance) is found.
# Acceptance of Medical Treatment Regimens Provided by AI vs. Human
### Results  
A manipulation check of the regimen content was first performed. A full-model ANOVA was done with the degree of surgical pain and the degree of effectiveness, using the treatment subject and the regimen content group as fixed factors. The results showed a significant effect of regimen content manipulation on the degree of surgical pain (\(M_{\text{utilitarian}}=5.82\), \(SD_{\text{utilitarian}}=0.89\); \(M_{\text{humane}}=3.11\), \(SD_{\text{humane}}=1.03\); \(t=10.96\), \(p<0.001\), \(d=2.82\)), as well as a significant effect of regimen content on surgical effectiveness (\(M_{\text{utilitarian}}=6.13\), \(SD_{\text{utilitarian}}=0.77\); \(M_{\text{humane}}=5.24\), \(SD_{\text{humane}}=0.63\); \(t=8.22\), \(p<0.001\), \(d=1.27\)). Therefore, the manipulation of the content of the surgical regimen was successful.  
Next, we examined the interaction effects of treatment subject and regimen content on acceptance. A full-model ANOVA was conducted with treatment subject and regimen content groups as fixed factors and acceptance as the dependent variable. The results showed a significant main effect for the treatment subject group (\(M_{\text{human physicians}}=4.17\), \(SD_{\text{human physicians}}=0.87\); \(M_{\text{AI}}=3.89\), \(SD_{\text{AI}}=0.96\), \(F(1,\,205)=5.752\), \(p=0.017\), \(d=0.31\)), while the main effect for the regimen content manipulation was not significant (\(F=0.542\), \(p=0.462\)). More importantly, there was an interaction effect of treatment subject and regimen content group on acceptance (\(F=4.110\), \(p=0.044\), \(d=0.24\)), with participants being more accepting of human physicians than AI in the humane regimen (\(M_{\text{human physicians}}=4.26\), \(SD_{\text{human physicians}}=0.71\); \(M_{\text{AI}}=3.70\), \(SD_{\text{AI}}=0.91\); \(t(96)=3.34\), \(p<0.001\), \(d=0.69\)). However, in the utilitarian regimen, there was no significant difference between participants' acceptance of human physicians and AI (\(M_{\text{human physicians}}=4.10\), \(SD_{\text{human physicians}}=0.99\); \(M_{\text{AI}}=4.05\), \(SD_{\text{AI}}=0.97\); \(t(109)=0.23\), \(p=0.818\), \(d=0.05\); Figure 2).  
Figure 2: Interaction of Human vs. AI and the type of regimen on acceptance.
## 2 Wind Speed Prediction Research Methodology
### Fully Adaptive Noise Ensemble Empirical Modal Decomposition  
CEEMDAN, proposed by [PERSON], is a method used for signal processing and data analysis, and is an improvement on and optimization of the empirical mode decomposition (EMD) method. CEEMDAN is suitable for the decomposition of non-smooth and nonlinear signals, and it can better control the magnitude and distribution of noise [33]. Traditional EMD decomposes a complex signal into a series of eigenmode functions, thereby revealing various features and components in the signal. However, EMD is sensitive to noise, which leads to unstable decomposition results when there is noise in the signal [34]. The main idea of CEEMDAN is to introduce positive and negative noise estimation and adaptive parameter tuning based on EMD. This improvement eliminates redundant auxiliary noise and reduces the number of iterations, increases the randomness of the data to mitigate  
Figure 1: Steps of CEEMDAN-RIME-MHSA-BiLSTM wind speed combination prediction algorithm.  
endpoint effects and modal aliasing, and reduces the error after signal reconstruction [35]. The main steps of CEEMDAN are as follows:  
Step 1: Let the original signal be \(x(t)\) and add Gaussian white noise (\(G^{i}(t)\)) to the original signal to form a noise signal [36] to obtain \(x^{i}(t)=x(t)+G^{i}(t)\), including among these \(i\) = 1, 2,..., \(n\). The EMD of the \(x^{i}(t)\) signal is decomposed into an intrinsic mode function (IMF) and a residual component (residual (R)) (\(r_{1}(t)\)), with the first component (\(I_{IMF1}\)) as follows:  
\[I_{IMF1}=\frac{1}{n}\sum\limits_{i=1}^{n}l_{IMF1}^{i}(t) \tag{1}\]  
\[r_{1}(t)=x(t)-I_{IMF1} \tag{2}\]  
where \(I_{IMF1}\) is the first component, \(I_{IMF1}^{i}(t)\) is the first decomposition mode function, and \(n\) is the number of signals;  
Step 2: A new component (\(r_{1}^{i}(t)=r(t)+w^{i}(t)\)) is obtained by adding the standard normally distributed Gaussian white noise (\(w^{i}(t)\)) to the first residual component (\(r_{1}(t)\)). The EMD decomposition of the \(r_{1}^{i}(t)\) is computed as follows:  
\[I_{IMF2}=\frac{1}{n}\sum\limits_{i=1}^{n}EMD\Big{(}r(t)+G^{i}(t)\Big{)} \tag{3}\]  
\[r_{2}(t)=r_{1}(t)-I_{IMF2}(t) \tag{4}\]  
Step 3: The decomposition is repeated until there are only two residual signal extreme points at most. Finally, \(I_{IMF1}\), \(I_{IMF2}\),..., \(I_{IMFn}\) can be obtained in turn for the corresponding residual components. The original signal can be expressed as follows:  
\[x(t)=\sum\limits_{j=1}^{z}I_{IMFj}+r_{z}(t) \tag{5}\]
# An Effective Simulation Scheme for Predicting the Aerodynamic Heat of a Scramjet-Propelled Vehicle
###### Abstract  
Currently, aerothermal research into scramjet-propelled vehicles characterized by a wedge-shaped section is relatively sparse. Based on the Mach number, grid strategy, and numerical method, an effective simulation scheme for predicting the aerodynamic heat of a scramjet-propelled vehicle during flight is proposed in this paper. At different Mach numbers, the appropriate grid strategy and numerical method were determined by validation tests. Two-dimensional external flow field models based on wedge sections were established and, unlike in blunt bodies, the tests showed that at the high supersonic stage, the ideal cell Reynolds number should be no larger than 16. At the hypersonic stage, the ideal cell Reynolds number and aspect ratio of wall cells near the shock should be no larger than 40, and the AUSM+ flux type performs better than [PERSON]'s FDS flux type at the above stages. The aerothermal prediction indicates that during a flight time of about 34 s, the temperature change reaches about 1913.35 \({}^{\circ}\)C, and the maximum average temperature change rate reaches 115 \({}^{\circ}\)C/s.  
a +
Footnote †: journal: Applied Sciences  
arcedynamic heat prediction; scramjet propelled vehicle; simulation scheme
# Automatic Restoration of Dunhuang Murals and Process Visualization Method Based on Deep Learning
###### Abstract  
Dunhuang murals are an important part of human cultural heritage with extraordinary literary and historical value. However, with the influences of time and the environment, many murals have suffered large defects and require complete reconstructions of their historical information, which is difficult. Moreover, the manual restoration of murals is complicated and slow. We attempted to use deep learning techniques to restore two types of damaged murals, including central regular defects and random irregular defects. First, we created a dataset of 26,240 Dunhuang murals for model training. Second, we built an image restoration model based on U-net to automatically restore murals. We show that mapping global information to defective areas can effectively restore murals with random irregular defects. Compared to manual restoration, the proposed model significantly reduces the time and cost of restoration work. In addition, we propose a method for constructing and preserving the contour maps of mural images and present the model's training process. Using the deep learning model, professional restorers can provide visual feedback on the restoration process to directly compare the restoration patterns of different rounds. This method implements observations of the filling-in process in the defective areas. Finally, we built a Dunhuang mural restoration system for professional restorers to restore, select, and share murals on an online platform.  
Dunhuang murals; mural restoration; deep learning; process visualization; restoration systems Article
## 3 Results
### Correlation Matrix  
In the analysis of complex data, especially when dealing with multiple variables, it is important to identify patterns and relationships that may influence the desired outcome. In the case of this research, there is an interest in seeing how different variables affect tool wear. To facilitate this task, a correlation matrix can be used to quantify the linear relationship between each pair of variables in the dataset. This allows for the evaluation of how variables such as axial force, spindle speed, and vibrations correlate with tool wear. Each value in the matrix represents the Pearson correlation coefficient, which ranges from \(-\)1 to 1; values close to these limits indicate strong positive or negative correlations, respectively, while values close to 0 suggest a weak relationship. By visualizing the matrix (Figure 8) with a heat map, significant relationships and emerging patterns can be quickly identified, allowing the analysis and modeling to focus on the variables that have the greatest impact on wear [21] or even consider others that may have been overlooked.  
Figure 8: Correlation matrix of the NUAA dataset.  
When analyzing the correlation matrix of the NUAA dataset (Figure 8), relationships emerge that align with the initial expectations of this study. In particular, the last row highlights a strong positive correlation between wear and time, which is consistent with the notion that wear tends to increase as time passes. Moreover, a moderate negative correlation is identified between wear and both cutting depth and spindle speed, suggesting that increasing these variables significantly reduces wear. The lack of data could imply that this might be due to factors such as energy dissipation or the distribution of stresses during the cutting process, though it could also be attributed to a decrease in cutting depth. A strong direct relationship is observed between vibrations on the \(X\)-axis and those on the \(Y\)-axis, as well as a total correlation between axial force and force \(Z\), probably indicating that they have the same data with different variable names. Lastly, the feed per tooth shows a positive correlation with wear, although it is not very high, thus establishing that as the feed per tooth increases, wear also increases.
## Materials and Methods
### JSBACH Simulations
#### 2.4.5 Model-Derived Indicators for the Start of Season  
The start of season in ENF for the regional JSBACH run (\(\text{ENF\_SOS}_{\text{mod}}\)) as well as for the local site-level runs (\(\text{ENF\_SOS}_{\text{mod,loc}}\)) was calculated from the canopy GPP of the respective PFT fraction. The method of calculating the \(\text{ENF\_SOS}_{\text{mod}}\) was similar to that for \(\text{ENF\_SOS}_{\text{EC}}\) (see Section 2.2). The JSBACH modelled start of season of DBF (\(\text{DBF\_SOS}_{\text{mod}}\)) was the first day of the year when the LAI exceeded the tiny threshold of 0.01 m\({}^{2}\cdot\)m\({}^{-2}\). This date typically coincides with the transition to the growth phase in LoGro-P.  
Since we used a proxy indicator from satellite time series of FSC for \(\text{ENF\_SOS}_{\text{sat}}\), we also included JSBACH modelled daily FSC in our assessment and calculated the proxy indicator from it using the method described in Section 2.5.2. The JSBACH FSC of fully snow-covered ground is by definition smaller or equal to 0.95 [53] and depends on terrain variation within the grid cell, whereas the FSC from satellite observation equals one in the case of fully snow-covered ground. Thus, to apply the remote sensing method, we scaled the modelled FSC based on the maximum simulated value for each grid cell during the period from 2003 to 2010, which is considered to correspond to full snow cover.  
In addition to \(\text{ENF\_SOS}_{\text{mod}}\) and \(\text{DBF\_SOS}_{\text{mod}}\) described above, the date of the start of the JSBACH \"growth phase\" (Section 2.4.1) of DBF and ENF was used in this study. We compared the JSBACH growth phase date of DBF simulated with a regional climate model and FMI gridded temperature for the analysis of remaining bias in the start of season due to the driving temperature. For this comparison, the grid cells at the edge of the area with FMI temperature observations were excluded.
# Event-Based Path-Planning and Path-Following
## 3 Two-Level Control System  
### Control System Architecture  
A two-level control system (see Figure 2) is developed in order to provide the AUV with both the path-planning and path-following capabilities. The path-following controller at the lower level is designed to drive the AUV along the path that is generated by the top level. In order to be closer to practice, the controller is implemented as a digital one with a sample time \(h\).  
The main component of the top level is a discrete-event system (DES). We consider a class of logical discrete-event systems that can be modelled by finite state machines. Based on the detected events, the DES changes its state, thus starting a sequence of actions performed by the top-level controller to build a new path. Therefore, the main goal of the DES is to identify situations that require updating the current path. For each monitored event, we assign a time interval at which the event-triggering condition is checked. In order to reduce the load on the on-board computer,  
Figure 2: Control system architecture.  
this interval for most events is assigned to be much larger than \(h\). For simplicity of implementation, we will assume that this interval is the same for all events and equals \(h_{e}\).  
In general, the path-planning process includes the following steps:  
1. detect events by processing data from FLS and other sensors;
2. change the DES state; and,
3. perform a suitable sequence of actions to rebuild the current path or change the active segment of the reference path.  
Path-planning algorithms proposed in the paper can generate two types of paths: to avoid obstacles and return to the reference path.
# A Perspective on Information Optimality in a Neural Circuit and Other Biological Systems
## 4 Discussion
### Information Processing as an Algorithm in Biological Systems
#### 4.2.2 Genetic Inheritance  
It is possible to frame the inheritance of genetic material through an information-based perspective (Figure 4). The genetic material is a sequence of elements, where each element is a nucleic acid molecule, and is inherited by an individual and serves as the informational code to form the new organism. These processes are usually presented with terminology and pathways that originate in the chaotic path of scientific discovery, and a pre-Linnaean approach to ontology of natural systems, but these informational processes are generalizable and categorical in nature. In the case of generating proteins, the above genetic sequence is the informational template that is processed for creating multiple copies, and then processed further to convert the copies to molecular sequences of amino acids--the protein sequence. These proteins then fold to form complex shapes in three-dimensional space. This higher dimensional form leads to complex dynamics, such as in its interactions with other molecules, its potential to increase the rate of biochemical reactions, and transformation of stored energy in molecular bonds to motion at the molecular level.  
Figure 4: Simplification of the pathway for conversion of a one-dimensional genetic sequence to a three-dimensional protein shape. (**A**). Genetic sequence where each word is 2 bits in width. This sequence is representative of the proximate code of a protein molecule; (**B**) multiple copies of the genetic sequence are created from (**A**); (**C**) protein sequence is formed from (**B**), encoded by each word of the genetic sequence of a width of 6 bits, although the bit size is compressible in the corresponding protein code; (**D**) protein shape in three dimensions, where the shape is formed by the dynamics of physical interactions within the protein and between other molecules.  
The presumption is that the processing of a linear sequence of genetic information to a protein shape is optimally designed, such as in the genetic code for protein translation, although an idealized optimality is constrained by biological processes. The linear sequence is one-dimensional, a compression of the three-dimensional shape of the protein, along with its corresponding spatiotemporal dynamics in a cellular environment. Biological molecules are the operators to convert the information along the steps of this pathways, like an algorithm.
# Transient Attitude Motion of TNS-0#2 Nanosatellite during Atmosphere Re-Entry
## 4 Measurements Processing Results and Passive Attitude Motion Regimes Analysis
### Aerodynamic Attitude Stabilization  
The satellite exhibited the tumbling transient motion for about two weeks. The telemetry analysis for 5 September 2019 is provided below. The satellite altitude is approximately 180 km and the atmosphere density is 3 \(\times\) 10\({}^{-10}\) kg/m\({}^{3}\). The aerodynamic torque is larger than the magnetic one by an order of magnitude. The deviation of the axis of the approximate dynamical symmetry from the direction of the incoming airflow does not exceed 30 degrees according to Figure 16. The angular velocity decreased to 0.5 deg/s (Figure 17) and a slow rotation around OZ axis is observed.
###### Abstract  
A better study of postnatal retinal development is essential for the in-depth understanding of the nature of the vision system. To date, quantitative analysis of postnatal retinal development is primarily limited to endpoint histological examination. This study is to validate in vivo optical coherence tomography (OCT) for longitudinal monitoring of postnatal retinal development in developing mouse eyes. OCT images of C57 BL/6J mice were recorded from postnatal day (P) 14 to P56. Three-dimensional (3D) frame registration and super averaging were adopted to investigate the fine structure of the retina. Quantitative OCT analysis revealed distinct outer and inner retinal layer changes, corresponding to eye development. At the outer retina, external limiting membrane (ELM) and ellipsoid zone (EZ) band intensities gradually increased with aging, and the IZ band was detectable by P28. At the inner retina, a hyporeflective layer (HRL) between the nerve fiber layer (NFL) and inner plexiform layer (IPL) was observed in developing eyes and gradually disappeared with aging. Further image analysis revealed individual RGCs within the HRL layer of the young mouse retina. However, RGCs were merged with the NFL and the IPL in the aged mouse retina. Moreover, the sub-IPL layer structure was observed to be gradually enhanced with aging. To interpret the observed retinal layer kinetics, a model based on eyeball expansion, cell apoptosis, and retinal structural modification was proposed.  
Postnatal retinal development; retinal layer thickness; OCT 1  
[PERSON]; [PERSON]; [PERSON]; [PERSON] Quantitative Optical Coherence Tomography for Longitudinal Monitoring of Postnatal Retinal Development in Developing Mouse Eyes. _Appl. Sci._**2022**, _12_, 1860. [[https://doi.org/10.3390/appl2041860](https://doi.org/10.3390/appl2041860)]([https://doi.org/10.3390/appl2041860](https://doi.org/10.3390/appl2041860))