esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Case Analysis
### Prediction Accuracy with Factor Ablation
#### 3.2.2 Test Site GPP without PAR  
In this experiment, all Par data were deleted from the training data, which were then trained for each site flux. The final prediction results are shown in Figure 10. Compared to the prediction results without any feature ablation of the training data, the prediction accuracy trends of the sites are similar to those obtained after removing Ta. The prediction accuracy of the AR site has significantly improved, but not as much as after the removal of Ta. The prediction accuracy of the DXST, DXSW, NMC, and ZF sites is sharply reduced. For the DXST site, the decrease is greater than that after removing Ta, whereas the DXSW, NMC, and ZF sites show some recovery but still perform worse than without any feature ablation. The prediction accuracy of the GL, HBKO, HBSH, and HBSW sites as a whole is lower than that after removing Ta, though it does not change appreciably.
## 6 Conclusions  
The result of our research is a realistic method for estimating the distance of particle impact of the dispersed oil fraction for different inner diameters of slim tubes. The relationships and methods listed above are sufficient to provide a relatively accurate lower estimate of the thickness of the oil layer under the operating conditions. For steel pipes with a standard length of 6 m and an internal diameter in the range of 15 to 35 mm, the steel tube is anticorrosively modified from the device by applying the dispersion oil fraction to the device under normal operating conditions such as operating temperature 15 to 25 \({}^{\circ}\)C, compressed air pressure in the range from 5 to 8 bar. The condition is to keep the 20 s application time, continuous pipe rotation during and/or after the application and the application from both sides of the oven will be necessary. As mentioned above (see also Figure 10 (right)), we can expect that, at the given oil mist injection mode, the thickness of the film of a 25 mm pipe will be everywhere above the critical value of \(h_{c}=1.68675\) um. In the case of a 15 mm pipe, a lower bound exponential line intersects the critical value at the point \(x_{15}^{*}\doteq 3.11\) m, so we may expect that at least one half of the pipe will be covered by the oil film with sufficient thickness. In the case of 35 mm pipes, the \"critical\" value is \(x_{35}^{*}\doteq 2.48\) m, which is less than one half of the pipe's length, i.e., the injection mode needs adjusting.  
In case of time optimization and avoidance of duplex application, for example in large-scale production, it is possible to analyze, according to the analyzed methodology, the thickness of the applied oil layer. For other internal diameters and pipe lengths, we recommend experimentally verifying the thickness of the oil layer.  
This research work was supported by projects: APVV-15-0149 \"Research of new measuring methods of machine condition\", VEGA 1/0437/17 \"Research and development of rotary module with an unlimited degree rotation\" and APVV-15-0202 \"Development equipment for efficient compression and storage of hydrogen using new metal hydride alloys\". We thank unknown reviewers for valuable recommendations of text improvement.  
**Author Contributions**: [PERSON] suggested the concept of coating the inner surface of slim tubes with of a dispersed oil fraction; [PERSON] and [PERSON] designed and performed experimental measurements in the laboratory; [PERSON] analyzed the data and suggested a calculation model for estimating the thickness of the oil layer; [PERSON] performed a numerical coating simulation; [PERSON] contributed materials and analysis tools; [PERSON] and [PERSON] wrote the paper.  
**Conflicts of Interest**: The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
## 4 Experimental Analysis
### TCReC's Reconstruction Ability Under Different Packet Loss Rates
However, it is remarkable that the experimental results consistently exhibit similar changes in the data curves under different packet loss rates. This consistency suggests that TCReC possesses a high level of robustness in its reconstruction ability, and any differences observed in the experimental results can be attributed to the performance of the detection classifier rather than the reconstruction model itself. In summary, TCReC exhibits a strong reconstruction effect across various packet loss ratios.
# Fuzzy-Based Multi-Modal Query-Forwarding in Mini-Datacenters
## 4 Modeling the Query Flow Within the Mini-Datacenter
### Fuzzy Modeling of Granular Selection in Query Forwarding
#### 4.3.3 Modeling Output Membership Function  
The output membership function describing the forwarding technique is modeled as stated in Equations (13)-(15), and the degree of membership variation is demonstrated in Figure 5.  
\[\mu_{low}(x)=\begin{cases}x/0.3&0\leq x<0.3\\ 0&x>0.3\end{cases} \tag{13}\]  
\[\mu_{medium}(x)=\begin{cases}(x-0.3)/0.1&0.3\leq x<0.4\\ 1&0.4\leq x\leq 0.5\\ (0.6-x)/0.1&0.5\leq x\leq 0.6\\ 0&x>0.6\end{cases} \tag{14}\]  
\[\mu_{high}(x)=\begin{cases}0&x<0.6\\ (1-x)/0.4&0.6\leq x\leq 1\\ 1&x>1\end{cases} \tag{15}\]  
Each fuzzy rule generates a fuzzy set (membership function) for the output variable, and these fuzzy sets need to be combined to form the final output fuzzy set. The max operator is used to perform this aggregation, meaning that the fuzzy output set is created by taking the maximum membership degree of the individual rule outputs at each point.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & \(\mathbf{G_{availability}}\) & \(\mathbf{G_{overloaded}}\) & **Output** &
\begin{tabular}{c} **Forwarding** \\ **Technique** \\ \end{tabular} \\ \hline
1 & Low & Low & Low & Coarse \\ \hline
2 & Low & Medium & Low & Coarse \\ \hline
3 & Low & High & Low & Coarse \\ \hline
4 & Medium & Low & Medium & Fine \\ \hline
5 & Medium & Medium & Medium & Fine \\ \hline
6 & Medium & High & Medium & Fine \\ \hline
7 & High & Low & High & Fine \\ \hline
8 & High & Medium & High & Fine \\ \hline
9 & High & High & Low & Coarse \\ \hline \hline \end{tabular}
\end{table}
Table 1: Set of fuzzy rules.
## 3 Results and Discussions  
### Tube Tests  
#### 3.1.1 Effect of Constituent Concentrations and Ratios of Cementation Solution  
Table 3 shows the precipitation mass for different constituent concentrations and ratios obtained from the tube tests. With a variation of CaCl\({}_{2}\):urea from 1:0.75 to 1:1.75, the precipitation mass at the same calcium chloride concentration increased in varying degrees, but the increasing rate decreased with a rise in the urea ratio [46]. This may be due to the small ionic strength of the low-concentration  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Treatment** & \multirow{2}{*}{**Treatment Method**} & \multicolumn{2}{c}{**Organic Material**} & \multicolumn{3}{c}{**Number of Tests**} & \multirow{2}{*}{**Objective**} \\ \cline{3-4}
**Number** & & \multicolumn{2}{c}{**Concentration (g-L\({}^{-1}\))**} & & & & & \\ \cline{3-4}
**A1** & & 2 & 3 & 3 & - & - & - \\
**A2** & & 4 & 3 & 3 & - & - & - \\
**A3** & & 6 & 3 & 3 & - & - & - \\
**A4** & & powder & 8 & 3 & 3 & 1 & 1 & 1 \\
**A5** & & 16 & 3 & 3 & - & - & - \\
**A6** & & 32 & 3 & 3 & - & - & - \\
**B1** & & 2 & 3 & 3 & - & - & - \\
**B2** & & 4 & 3 & 3 & - & - & - \\
**B3** & & EICP + glutinous rice & 6 & 3 & 3 & - & - & - \\
**B4** & & powder & 8 & 3 & 3 & 1 & 1 & 1 \\
**B5** & & 16 & 3 & 3 & - & - & - \\
**B6** & & 32 & 3 & 3 & - & - & - \\
**C1** & & 2 & 3 & 3 & - & - & - \\
**C2** & & 4 & 3 & 3 & - & - & - \\
**C3** & & EICP + brown sugar & 6 & 3 & 3 & - & - & - \\
**C4** & & 8 & 3 & 3 & - & - & - \\
**C5** & & 16 & 3 & 3 & 3 & 1 & 1 \\
**C6** & & 32 & 3 & 3 & - & - & - \\
**D1** & Untreated & 0 & 3 & - & 3 & 1 & 1 \\
**D2** & Skim milk powder & \(a_{1}\) & 3 & - & - & - & - \\
**D3** & Glutinous rice powder & \(a_{2}\) & 3 & - & - & - & - \\
**D4** & Brown sugar & \(a_{3}\) & 3 & - & - & - & - \\
**D5** & EICP & 0 & 3 & - & 3 & 1 & 1 \\ \hline \hline \end{tabular} Note: Soil used in calcium carbonate content (CCC), SEM, and XRD tests were collected from samples after strength tests.  
\end{table}
Table 2: Scheme of soil reinforcement tests.  
calcium chloride solution and the limit reached for the amount of urea required to minimize the calcium ion mobility by forming hydration shells around the calcium ions. Then, as the ratio increased, the high-concentration solution gradually reached its limit. The precipitation mass of calcium carbonate for the 1:0.75 ratio was much less than that for the 1:1, 1:1.25, 1:1.5, and 1:1.75 ratios. The precipitation masses under the latter four proportions were relatively close, indicating that when the urea ratio was greater than that of calcium chloride, the precipitation mass of calcium carbonate mainly depended on the amount of calcium chloride. Overall, for the lower concentration of the cementation solution when the ratio of CaCl\({}_{2}\):urea reached 1:1, the calcium ion strength was inhibited. It is not economical to increase the urea proportion. When the cementation solution concentration was high, the efficiency of calcium carbonate formation with CaCl\({}_{2}\):urea maintained at 1:1.5 was the best.
###### Abstract  
This study investigates the potential of fluorescence imaging in conjunction with an original, fused segmentation framework for enhanced detection and delineation of brain tumor margins. By means of a test bed optical microscopy system, autofluorescence is utilized to capture gray level images of brain tumor specimens through slices, obtained at various depths from the surface, each of 10 \(\upmu\)m thickness. The samples used in this study originate from tumor cell lines characterized as Gli36\(\theta\)EGRF cells expressing a green fluorescent protein. An innovative three-step biomedical image analysis framework is presented aimed at enhancing the contrast and dissimilarity between the malignant and the remaining tissue regions to allow for enhanced visualization and accurate extraction of tumor boundaries. The fluorescence image acquisition system implemented with an appropriate unsupervised pipeline of image processing and fusion algorithms indicates clear differentiation of tumor margins and increased image contrast. Establishing protocols for the safe administration of fluorescent protein molecules, these would be introduced into glioma tissues or cells either at a pre-surgery stage or applied to the malignant tissue intraoperatively; typical applications encompass areas of fluorescence-guided surgery (FGS) and confocal laser endomicroscopy (CLE). As a result, this image acquisition scheme could significantly improve decision-making during brain tumor resection procedures and significantly facilitate brain surgery neuropathology during operation.  
fluorescence imaging; imaging of cancer margins; Fluorescence-guided surgery (FGS); confocal laser endomicroscopy (CLE); glioblastoma multiforme (GBM); unsupervised segmentation tools; fusion of cluster-based segmentation schemes; Otsu thresholding; entropy-based thresholding; mean shift clustering +
Footnote †: journal:  
_Article_
# Fast 3D Transmission Tower Detection Based on Virtual Views
## 1 Introduction  
Electricity networks represent a critical infrastructure and a pivotal element of the national power grid. Despite the rapid progression of new power systems under the national 'double carbon' strategy, most high-voltage transmission towers (TTs) in China are subjected to various security risks posed by the surrounding terrain and vegetation. These risks arise due to the long transmission distances and the need to traverse intricate geographical landscapes [1]. To alleviate potential surface threats adjacent to these lines, responsible departments carry out periodic inspections. In line with scientific and technological progress, the application of remote sensing technology to inspect electrical grid assets has emerged as a key area of research in the development of the smart grid [2, 3].  
For automated inspections of electrical grid assets using remote sensing technology, the primary data sources consist of SAR imagery, optical satellite images, optical aerialphotographs, and airborne LiDAR point cloud data. LiDAR point clouds stand out for their exceptionally high spatial resolution, achieving accuracy at the centimeter level. Furthermore, LiDAR data collection is not affected by adverse weather conditions such as cloud cover, fog, or sunlight, enabling continuous monitoring regardless of weather or time of day. This capability makes LiDAR particularly useful during large-scale natural disasters such as snowstorms and earthquakes [4]. In contrast to SAR or optical imagery, which require elaborate preprocessing to construct 3D point clouds, LiDAR point clouds inherently offer high-precision, structured 3D information of the entire scene without complex processing, thereby substantially simplifying subsequent point cloud analysis and object identification. Consequently, airborne LiDAR data are gaining prominence in automated inspections of electrical grid assets, especially for the detection of TTs [5].  
Thanks to significant advances in artificial intelligence and computer vision technology, object detection is increasingly becoming a key technology for automated inspections of electrical grid assets. Object detection methods have achieved significant success in the image domain, yet their shortcomings become more pronounced when applied to 3D point cloud data. For one, acquiring an adequate number of 3D patterns is challenging; in the case of TTs, for example, only a limited number of 2D samples are publicly accessible [6]. Additionally, the precision of current 3D object detection techniques is heavily influenced by the density of point clouds, leading to not only reduced detection efficiency but also limited adaptability across different scenarios [4, 7]. Therefore, there is an urgent demand for the development of fast and robust 3D object detection algorithms for automated inspections of TTs.
## 2 Materials and Method
### Principal Component Regression (PCR) Method  
#### 2.2.1 Data Preprocessing  
The efficacy of integrated principal component and regression modeling for analyzing lime-, cement-, and soil-stabilization mechanisms depends on rigorous preprocessing to ensure dataset integrity. As highlighted by [PERSON] et al., unreliable inputs compromise analytical outcomes [25]. Key steps include systematic collation, quality control, normalization, gap interpolation, and stratified partitioning into training and validation subsets. These processes preserve genuine experimental variance while minimizing biases that could skew multivariate models. [PERSON] et al. further stress adaptive preprocessing tailored to raw data distributions to align with dimensionality reduction requirements. Constructing a representative dataset through standardized protocols--mixing ratios, moisture conditioning, compaction, and scaling--enables accurate deciphering of stabilization pathways [25]. Structured, bias-resistant datasets enhance PCA and regression modeling, bridging empirical studies and field-scale implementation via reliability-driven analytics.
## 3 Results
### Influence of Depowdering Air Pressure on the Surface Roughness  
Figure 11 shows the results of this experiment. For the surface analysis, the roughness parameters for a plane \((Sa,Sq)\) are used to ensure that the profile evaluation is performed correctly. When using 2D roughness values, it must be ensured that the parts are examined in the build-up direction, since an evaluation parallel to the stacked layers will result in an underestimation of the actual roughness.  
Figure 11: Surface characteristics for different depowdering air pressures.  
Figure 10: Print job layout for measuring the warpage characteristics across the build platform.  
It is found that increasing air pressures lead to an increase in surface roughness. The lowest roughness with a Sa = 8.8 \(\upmu\)m is obtained with the depowdering pressure of 2 bar. At this low pressure, however, the depowdering takes more time and loose powder residues remain adhered to the component. These can then lead to contamination of the sintering furnace or the measuring equipment. Increasing the pressure to 3 bar leads to an increase in surface roughness to an average of Sa = 9.9 \(\upmu\)m, and reliably removes the adhering powder in a sufficiently short time. This trend continues with further increases in the pressure to 4 or 5 bar. The highest level of roughness is achieved at 5 bar with a Sa = 14.6 \(\upmu\)m, which means an increase of approximately 65% compared to the pressure level of 2 bar. The standard deviation decreases with increasing pressure. Whilst at 2 bar it is still approximately 1.3 \(\upmu\)m, it decreases successively with each next higher pressure level until at 5 bar it is only 0.8 \(\upmu\)m. The coefficient of variation decreases successively from 0.15 to 0.11 to 0.8 to 0.5. Nevertheless, the rather high standard deviations show that the manual depowdering process has only moderately good repeatability, even under strictly controlled process parameters.
# Analyzing and Forecasting Vessel Traffic Through the Panama Canal: A Comparative Study
## 3 Results
### Seasonal Autoregressive Integrated Moving Average Model (SARIMA Model)  
The SARIMA model was employed due to the presence of a seasonal component in the series (see Figure 7). Initially, five model variants with significant parameters were identified based on the Akaike Information Criterion (AIC) and Schwarz Bayesian Criterion (BIC). Validation included checking the stationarity of the series and confirming the absence of autocorrelation in the residuals through autocorrelation plots and the Box-Ljung test. The autoregressive coefficients were then estimated to complete the model fitting. Subsequently, goodness-of-fit tests were conducted, evaluating the linearity and normality of the residuals to ensure the model met the diagnostic phase outlined by the Box-Jenkins methodology. Finally, the SARIMA (1,0,1)(0,1,1)\({}_{12}\) model was selected for forecasting, demonstrating accurate short-term predictions and a significant seasonal influence on the number of vessels transiting the Panama Canal. The modeling process is presented below in a structured manner through diagrams that illustrate the key steps of the methodology.  
To verify the stationarity of the data, the Augmented Dickey-Fuller (ADF) test was applied. Upon identifying that the series was non-stationary, seasonal differencing was necessary to stabilize the mean and obtain a stationary series suitable for fitting the SARIMA model. This result was confirmed by the Dickey-Fuller test, which yielded an ADF statistic of \(-16.505\) and a \(p\)-value of 0.01 (<0.05). Therefore, the development of the model was deemed appropriate. Subsequently, the autocorrelation and partial autocorrelation plots of the stationary series were constructed (Figure 8).  
Based on the lags of the autocorrelation and partial autocorrelation functions (Figure 8), and after testing and analyzing several models, the SARIMA (1,0,1)(0,1,1) model was selected.  
Figure 9 shows that the residuals (top panel) exhibit random behavior around zero, with no evidence of discernible patterns. The residual values (bottom left) lie within the confidence bands of the ACF plot, indicating no strong signs of autocorrelation. Additionally, in the residuals plot (bottom right), the values tend toward zero and approximate a normal distribution. These results confirm that the residuals behave like white noise, suggesting that the model satisfactorily captured the dynamics of the time series.  
Figure 8: Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF).
# Monocular Vision-Based Depth Estimation of Forward-Looking Scenes for Mobile Platforms
## 1 Introduction
This module enhances the multiscale fusion effects of feature maps at different levels and helps prevent the loss of important semantic and detailed information. 2. To enhance the weights of significant channels for extracting more prominent features and thereby improving the accuracy of depth estimation, the Efficient Channel Attention Module (ECANet) is integrated into the proposed network. 3. We conducted experiments using both the _KITTI_ dataset, which was a public dataset and constructed in a real environment, and the virtual simulation dataset based on the _AirSim_ simulation environment. These datasets encompass various lighting conditions and application scenarios. The related experimental results demonstrate that the proposed method has been initially validated and shows promising performance when compared to the original _Monodepth2_. The remainder of this article is structured as follows: Section 2 presents the network architecture and innovations of our method. Section 3 outlines the experimental design and results. Finally, Section 4 concludes the article and suggests directions for future improvements.
###### Abstract
A number of studies have been conducted to analyze the postural control mechanism in participants suffering from a concussion while completing trials that involved walking, adjusting the standing environment such as standing surface and wall angle, and cognitive testing such as answering questions [7; 10; 30; 31; 32]. Although the information obtained from these studies is important, the methodologies would be difficult to apply outside of a lab environment as multiple permanent force plates are required to ensure uniform data recording for a moving participant, and cognitive testing and variable standing environments add complexity to data collection and minimize exact repeatability. Since it appears that no previous research has examined ApEn and the velocity of the COP in quiet standing postures, utilizing only tandem stances and closed eyes to introduce instability, additional work remains. Thus, identifying objective metrics from the COP with this methodology could inform work that could lead to better outcomes when screening for concussive damage. There are many analyses that can aid in learning more about the patterns of the dynamic COP and COM signals as they relate to stability. As the COP of a person with typical neurological functionality can be expected to follow a relatively regular pattern, approximate entropy (ApEn), a measure that quantifies the regularity of a signal [33], can be useful in identifying changes due to loss of stability. ApEn has been used in past studies to detect changes to postural stability in nonlinear, biological signals [34; 35; 36]. Additionally, the velocity of the COP can give information about how quickly a person sways back and forth in an attempt to maintain balance, potentially indicating changes in stability as well. Comparing the changing ApEn and velocity of COP between trials of different stability conditions may help identify useful indices for quantifying loss of postural control, independent of the cause, i.e., due to postural changes or neurological changes. The purpose of this study was to describe the center of pressure approximate entropy and velocity in healthy individuals under quiet standing positions and determine if they are sensitive enough to distinguish small changes to stability. Further, the methodology for data collection was designed specifically to be simple and able to be completed in various settings, not requiring a permanent lab setup. As patterns and behaviors are understood from this work, the knowledge of how these methods of COP look for a healthy individual can be compared to how they look for an individual under suspicion of abnormal neurological functioning, such as a concussion, in future work. Based on previous research, it was hypothesized that decreased stability would increase both the approximate entropy and velocity of COP.