esa-sceva/satcom-chunk-collection huggingface dataset:
# Specific-Input LIME Explanations for Tabular Data Based on Deep Learning Models
## 3 Materials and Methods
### Mathematical Representation of LIME  
This section explains our understanding of LIME in mathematical terms.  
The original, global representation of a given instance \(x\) is as follows:  
\[x\in\mathcal{R}^{2} \tag{1}\]  
A binary vector, on the other hand, is an explainable representation of an instance:  
\[x^{\prime}\in\{0,1\}^{d^{\prime}} \tag{2}\]  
The explainable representation indicates whether a feature or set of features is present or absent at a given location.  
Let us have a look at LIME's model-agnostic property. A deep learning model is represented by the letter \(g\). \(G\) denotes a set of models that includes \(g\) as well as other models:  
\[g\in G \tag{3}\]  
As a result, any other model will be explained similarly by LIME's algorithm. Because the gg domain is a binary vector, we can write it as follows:  
\[g\in\{0,1\}^{d^{\prime\prime}} \tag{4}\]  
The complexity of \(g\in G\) may make studying the neighbourhood of an instance difficult. This is something we must consider. As an example of the complexity of a model explanation, consider the following:  
\[\Omega(g) \tag{5}\]  
For humans to be able to explain a forecast, \(\Omega(g)\) must be low enough.  
The model can thus also be defined as follows:  
\[f:\mathcal{R}^{2}\rightarrow\mathcal{R} \tag{6}\]  
The significance of a proximity measurement between an instance \(z\) and the neighbourhood around \(x\) may be seen. This proximity measurement will be defined as follows:  
\[\Pi_{x}(z) \tag{7}\]  
Except for one crucial variable, we now have all the variables we need to define LIME. How close is this prediction to our model's global ground truth? Is it possible to explain why it is reliable? Our challenge will be answered by determining how unreliable a model \(g\) can be when estimating \(f\) in the locality \(\Pi_{x}\).  
A prediction could be a false positive, for all we know! It is also possible that the prognosis is a false negative! Worse yet, the prediction could be a true positive or negative for the wrong reasons.  
We use the letter \(L\) to represent unfaithfulness. \(L(f,g,\Pi_{x})\) will determine how untrustworthy \(g\) is when approximating \(f\) in the area we described as \(\Pi_{x}\).  
Finally, we may define a LIME-generated explanation \(\varepsilon\) as follows:  
\[\varepsilon(x)=argminL(f,g,\Pi_{x})+\Omega(g) \tag{8}\]  
The above is a complete mathematical representation of the LIME method by [PERSON] et al. [11]. Regardless of the model used, LIME will draw samples weighted by \(\Pi_{x}\) to optimise the equation and offer the best interpretation and explanations \(\varepsilon(x)\). LIME can analyse a wide range of models, fidelity functions, and complexity metrics.
# Breast Cancer Survival Analysis Model
## 4 Discussion
### SemiMarkov Chain
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Transition**} & \multicolumn{2}{c}{**Surgical Treatment**} & \multicolumn{2}{c}{**Radiotherapy**} & \multicolumn{2}{c}{**Chemotherapy**} & \multicolumn{2}{c}{**Hormonal Therapy**} \\ \cline{2-10}  & **Parameters** & \(p\) **Value** & **Parameters** & \(p\) **Value** & **Parameters** & \(p\) **Value** & **Parameters** & \(p\) **Value** \\ \hline
1\(\rightarrow\)2 & \(-\)1.691 & \textless{}0.001 * & 0.117 & 0.0769 & \(-\)7.176 & \textless{}0.001 * & 0.109 & 0.1253 \\
1\(\rightarrow\)3 & \(-\)2.237 & \textless{}0.001 * & \(-\)1.487 & \textless{}0.001 * & \(-\)1.745 & \textless{}0.001 * & \(-\)1.278 & \textless{}0.001 * \\
1\(\rightarrow\)4 & \(-\)3.216 & \textless{}0.001 * & \(-\)0.244 & \textless{}0.001 * & \(-\)3.294 & \textless{}0.001 * & \(-\)3.964 & \textless{}0.001 * \\
2\(\rightarrow\)1 & 2.892 & \textless{}0.001 * & \(-\)1.084 & \textless{}0.001 * & \(-\)8.965 & 0.0014 * & 0.008 & 0.543 \\
2\(\rightarrow\)3 & 3.762 & \textless{}0.001 * & 1.632 & \textless{}0.001 * & 9.44 & 0.639 & 0.305 & 0.259 \\
2\(\rightarrow\)4 & \(-\)2.493 & 0.1463 & \(-\)1.830 & \textless{}0.001 * & \(-\)8.51 & 0.920 & 1.075 & 0.427 \\
3\(\rightarrow\)1 & \(-\)1.254 & \textless{}0.001 * & \(-\)1.500 & \textless{}0.001 * & \(-\)5.52 & 0.020 * & \(-\)0.891 & 0.0012 * \\
3\(\rightarrow\)2 & \(-\)0.246 & 0.466 & 3.728 & \textless{}0.001 * & 2.495 & 0.032 * & 0.305 & 0.3897 \\
3\(\rightarrow\)4 & \(-\)3.237 & 0.0037 * & \(-\)2.79 & \textless{}0.001 * & \(-\)2.872 & 0.654 & \(-\)2.054 & 0.0288 * \\
4\(\rightarrow\)1 & \(-\)5.495 & \textless{}0.001 * & \(-\)6.071 & \textless{}0.001 * & \(-\)1.003 & \textless{}0.001 * & 6.568 & \textless{}0.001 * \\
4\(\rightarrow\)2 & \(-\)2.650 & 0.949 & \(-\)8.503 & \textless{}0.001 * & \(-\)8.75 & 0.075 & 7.075 & \textless{}0.001 * \\
4\(\rightarrow\)3 & 1.810 & 0.2694 & 12.253 & \textless{}0.001 * & \(-\)1.29 & \textless{}0.001 * & 2.400 & 0.124 \\ \hline \hline \end{tabular} Note: * means significant.  
\end{table} 
Table 13: Covariate parameters and \(p\)-values of each univariate model.
# A Novel Geo-Localization Method for UAV and Satellite Images Using Cross-View Consistent Attention
## 2 Related Works
### Vision Transformer  
Transformer [41], based on the attention mechanism, was first proposed on machine translation, outperforming the RNN and CNN-based methods. ViT [16] divided the input images into several patches, and fixed length vectors are projected onto Transformer for each patch. The procedure of the subsequent encoder is the same as that in the original Transformer. ViT attained competitive results compared with CNNs while requiring substantially fewer computational resources for training. PVT [42] was a Transformer-based architecture that constructed a feature pyramid to learn multi-scale and high-resolution features. Swin Transformer [43] proposed a hierarchical Transformer whose representation is computed with Shifted windows. CvT [44] introduced a hierarchy of Transformers containing the convolutional token embedding. A convolutional Transformer block was used to leverage the convolutional projection. [PERSON] [45] proposed two Transformer architectures to improve the accuracy of deep Transformers. LV-ViT [46] utilized all the image patch tokens to compute the training loss. When transferred to different downstream tasks, data-efficient image transformers (DeiT) [17] pre-learned on Imagenet were competitive. The distillation and teacher model is adopted to guide the DeiT based on Vision Transformer architecture to perform better. Since the pre-trained DeiT model has fewer parameters and higher training efficiency than the pre-trained ViT model, it is introduced to the pipeline of our method.
# Radon Transform Based on Waveform for AVO-Preserving Data Construction
## 2 Methods
### Inversion Transform Preserving AVO  
To preserve the AVO, we incorporated the least-squares principle when designing the inverse Radon transform. Unlike the traditional Radon transform, the inverse Radon transform defines the inverse transform first and uses the least-squares principle to obtain the forward transform formula. The proposed method already obtained the high-resolution Radon model result based on the energy principle, so we used the least-squares result based on the energy principle, to obtain the inverse transform. The misfit between the original seismic data and inverse seismic data was minimized and subjected to the least-squares principle.  
The AVO characteristics of the CDP gathers can be fitted by polynomials [25]. In addition, fewer coefficients of the low-order polynomials can describe the main energy of the amplitude ([PERSON] et al., 1995 [12]). Therefore, the AVO characteristics of the seismic reflection wave can be described as:  
\[\mathrm{F}(x)=c_{0}+c_{1}x+c_{2}x^{2} \tag{16}\]F(\(x\)) is the amplitude energy, \(x\) is the offset, and \(c_{0}\), \(c_{1}\) and \(c_{2}\) are the coefficients. The formula of the inverse transform is as follows:  
\[d^{\prime}(t,x)=\sum_{i=0}^{N}m\Big{(}\tau^{2}=t^{2}-p_{i}^{2}x^{2},p_{i}\Big{)} \text{F}(x) \tag{17}\]  
\(d^{\prime}(t,x)\) is the inverse Radon transform result of proposed method. The objective function of the least square is as follows:  
\[J=\abs{\abs{d-d^{\prime}}} \tag{18}\]  
By solving Equation (13), we obtain the values of \(c_{0}\), \(c_{1}\) and \(c_{2}\).  
The whole process of the proposed method does not include the time-variant kernel. It clearly improves the computational efficiency.
## 3 Study Site, Data, and Analysis
It will be seen that the bottleneck can be considered active since the downstream speeds remain free-flowing throughout the morning peak while the upstream speeds change from free-flowing to bottleneck and back to free-flowing again (Figure 2b). In between the transitions, the discharge rates can be measured from the slanted cumulative curves, which are also reflected in the time series graph of flow rates (Figure 2c). Figure 2d also shows that the density triggering the capacity drop can be determined from the time series graph of the densities near the bottleneck (Figure 2d). The measurement is done during the bottleneck activation. The time of the start of the capacity drop is shown in the slanted cumulative curves as the change in slope. This change in slope also represents the change in flow. Further, Figure 2d shows a time series of the densities triggering the capacity drop. Density was gathered through detections near the bottleneck in all lanes. The figure shows that density coincided with the reaching of the time series to 80 vehicles/km and that this value was steady throughout the bottleneck. It is also evident that discharges recover to pre-queue capacity once the density departs from this critical value. This result is observable across many days. However, there is a significant difference of what is considered critical density when the capacity drop is triggered between days with and without precipitation. This observation is also observed with upright reproducibility. The speed in congestion during different weather conditions was then plotted against accompanying queue discharge rates to see a relationship between the capacity drop and precipitation rates, and a regression analysis was performed. This is also done for the speed in congestion and the corresponding percentage of the capacity drop. Figure 2: Oblique cumulative curves, speed, flow, and density on 1 June 2022.
## 4 Handover and Mobility Process in 5G HetNet
### Key HO Parameters in 5G HetNet
#### 4.1.1 HO Decision Parameters
Another important parameter for making HO decisions is CSI, which estimates the condition of the radio link (attenuation, path loss, capacity, and interference) between the UE and the BS to select the best cell in the neighbor.
# WaQuPs: A ROS-Integrated Ensemble Learning Model for Precise Water Quality Prediction
## 9 Conclusions  
In our discussion, our research showcases the successful development of the water quality prediction system (WaQuPs), a sophisticated platform encompassing both a website and a mobile application. By leveraging advanced machine learning techniques, particularly ensemble learning models like Random Forest, the system can adeptly categorize water quality into distinct levels. Integration of Internet of Things (IoT) principles, with MQTT as the communication protocol, facilitates seamless interactions between servers and mobile devices, thereby expanding the system's potential for real-world applications. Our research methodology incorporates meticulous strategies, including ROS to address class imbalance, five-fold cross-validation, and hyperparameter tuning, collectively enhancing the robustness and precision of our machine learning model.  
WaQuPs demonstrates its superiority over Random Forest models, achieving an impressive 83% accuracy rate, along with precision of 82%, recall of 83% and an F1-score of 82%. Comparative analysis revealed that WaQuPs with the Random Forest model outperformed both XGBoost and CatBoost models, affirming its superiority in predicting water quality. In the preceding section, we emphasize key points essential for advancing future research in this field. Implementation of these suggestions has the potential to significantly elevate the quality and depth of forthcoming studies within the domain of water quality prediction and machine learning applications.  
**Author Contributions:** Conceptualization, S.M., F.F. and A.; methodology, S.M., F.F., A. and A.H.A.; software, S.M., F.F. and A.; formal analysis, S.M., F.F., A. and A.H.A.; investigation, S.M. and F.F.; validation, S.M. and A.; writing--original draft preparation, S.M., F.F. and A.H.A.; writing--review and editing, S.M., F.F., A. and A.H.A.; project administration, S.M. and A.; funding acquisition, S.M. and A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are all openly available. The dataset for water quality is found at [[https://data.jakarta.go.id/dataset/data-hasil-penguijian-kualitas-air-sumur-provinsi-dki-jakarta-tahun-2017](https://data.jakarta.go.id/dataset/data-hasil-penguijian-kualitas-air-sumur-provinsi-dki-jakarta-tahun-2017)]([https://data.jakarta.go.id/dataset/data-hasil-penguijian-kualitas-air-sumur-provinsi-dki-jakarta-tahun-2017](https://data.jakarta.go.id/dataset/data-hasil-penguijian-kualitas-air-sumur-provinsi-dki-jakarta-tahun-2017)), accessed on 12 April 2023.  
**Acknowledgments:** We thank Telkom University for help in the project administration.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 Experimental Results and Discussion
### The Result of Seeding Defects' Identification  
Finally, all images in the dataset were analyzed for defects. The experiment was implemented on an HP PC with an Intel Core-i7 processor and 8 GB RAM. In the experiment, the original image with a size of \(265\times 1024\) shown in Figure 21 was selected as the test image. The algorithm was written in the  
Figure 14: Feature scatter plot of the substrate area.  
Figure 15: Feature scatter plot of the c value of the substrate lower part.  
Halcon library. All pictures in the dataset were image segmented using the thresholds calculated above, and the corresponding features were identified for defects. The experiment result is shown in Table 4.  
Accuracy (see Equation (10)), precision (see Equation (11)), and recall (see Equation (12)) were used as indicators for analyzing the effect of defect identification. The analysis result is shown in Table 5. The normal images were taken as positive samples, and the defective seedling images were taken as negative samples.  
\[accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{10}\]  
\[precision=\frac{TP}{TP+FP} \tag{11}\]  
\[recall=\frac{TP}{TP+FN} \tag{12}\]  
where \(TP\) is the number of positive samples that are correctly identified; \(TN\) is the number of negative samples that are correctly identified; \(FP\) is the number of positive samples that are misidentified; \(FN\) is the number of negative samples that are misidentified.  
The experiment analysis indicated that: the accuracy was 97.6%; the precision was 97.4%; the recall was 99.8%; and the progress time was 71.4 ms. The algorithm had good accuracy in identifying seedling defects. The defect seedling misidentification rate was extremely low (recall was 99.8%). The algorithm had good real-time performance and could be applied in high speed cabbage transplanters.
# Reliability Analysis of CFRP-Packaged FBG Sensors Using FMEA and FTA Techniques
## 3 Reliability Analysis of CFRP-Packaged FBG Sensors Using the FMEA Technique  
### A Brief Introduction of the FMEA Technique  
FMEA is a systemic process to ascertain critical failure items, and the results can show the failure modes, failure causes, as well as their related components or subsystems. The FMEA method is a bottom-up analysis program and its analysis flow is shown in Figure 7. For more information about FMEA, please refer to [11; 12; 13; 14; 18; 19; 20].  
Figure 5: The structure of CFRP-packaged FBG sensors and their application on a WTB.  
Figure 6: Strain transfer process of a CFRP-packaged FBG sensor.  
FMEA adopts the risk priority number (RPN): the product of three indices: severity (S), occurrence (O), and detection (D), to assess the risk of one certain failure item, where severity represents the consequence of a failure, occurrence denotes a failure's likelihood, and detection reflects the ability of a failure to be observed.  
In engineering projects, the RPNs can be calculated by [20]:  
\[\text{RPN}=\text{A(S)}\times\text{A(O)}\times\text{A(D)} \tag{2}\]  
where A(S), A(O), and A(D) are the average values of severity, occurrence, and detection given by the specialists.
## 3 Overall Design of the Communication Channel Reconstruction  
In this section, the overall design of the communication channel reconstruction is presented. According to the principle of the sampled-value-based and phasor-based differential protection, data preparation, communication capacity and synchronization are analyzed for the design.
## 2 Numerical Simulation Modeling of Plate-Fin Heat Exchanger
### Boundary Conditions  
In addition to the solid domain of the model, two fluid domains, oil-side, and water-side, are defined in Fluent software for the heat exchanger model. All three simulation domains are meshed using tetrahedral unstructured meshes. A shear stress transport k-\(\omega\) turbulence model (SST k-\(\omega\)) and standard wall functions are used for numerical simulations, and velocity inlet and pressure outlet boundary conditions are used for both the oil and water sides. The heat exchanger walls are all set as wall boundary conditions with thermal conductivity and the other external walls are set as insulation walls. In order to obtain the best performance of the heat exchanger, 9 numerical simulation test groups (5 numerical simulation cases per group, 45 numerical simulation cases in total) are designed with oil flow rate, water flow rate, and the fin angle as input parameters, as shown in Table 4. Among them, the inlet flow rate covers the operating range of the heat exchanger, the oil inlet temperature is constant at 100 \({}^{\circ}\)C, and the water inlet temperature is constant at 65 \({}^{\circ}\)C.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Fin Angle** \\ **(Degree)** \\ \end{tabular} } & \multirow{2}{*}{**Direction**} & \multicolumn{2}{c}{**Oil-Side**} & \multicolumn{2}{c}{**Water-Side**} \\ \cline{3-6}  & & **Inertia** & **Viscous** & **Inertia** & **Viscous** \\  & & **Resistance** & **Resistance** & **Resistance** & **Resistance** \\  & & **Coefficient** & **Coefficient** & **Coefficient** & **Coefficient** \\ \hline \multirow{3}{*}{60} & x & 3085.18 & 27,150,232.23 & 3696.54 & 50,883,478.55 \\  & y & 211.48 & 11,140,364.43 & 111.96 & 17,094,499.37 \\  & z & 489.32 & 13,550,495.64 & 611.80 & 18,247,912.26 \\ \hline \multirow{3}{*}{75} & x & 4932.33 & 34,927,706.55 & 5421.13 & 111,562,991.10 \\  & y & 260.39 & 14,989,419.77 & 142.80 & 21,038,545.35 \\  & z & 260.68 & 15,760,390.93 & 251.51 & 22,156,993.76 \\ \hline \multirow{3}{*}{90} & x & 8109.04 & 64,959,109.79 & 9404.43 & 145,279,021.40 \\  & y & 431.28 & 22,517,311.17 & 227.92 & 32,326,301.05 \\ \cline{1-1}  & z & 231.38 & 23,986,847.43 & 150.94 & 30,901,507.98 \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._
# Assessing Spatiotemporal LST Variations in Urban Landscapes Using Diurnal UAV Thermography
## 4 Results
### GWR Results
Paved roads showed consistent strengthening of thermal relationships across the daily cycle, with increases of 63.8% from morning to afternoon and a further 40.7% from afternoon to evening, before a slight decline (\(-9.3\%\)) at midnight. Comparing across LULC types, water bodies exhibited the strongest overall thermal influence, with mean adjusted \(R^{2}\) of 0.536 across all time periods, followed by paved roads (0.433), while grassland showed the weakest overall relationship (0.076). The most pronounced thermal relationship for any LULC-time combination was observed for water bodies during evening hours (adjusted \(R^{2}=0.638\)), which was 21.3 times stronger than the weakest significant relationship (grassland during evening, adjusted \(R^{2}=0.030\)). These quantitative differences in model performance across time periods substantiate the temporal dependence of thermal relationships and highlight critical transitions, particularly the evening period (17:00-18:00) when thermal influence patterns undergo significant reconfiguration across most LULC classes. The results underscore that the thermal influence of various LULC types is both temporally and spatially heterogeneous. The high explanatory power of the models for water bodies and paved roads--especially during periods of lower solar radiation (evening and midnight)--suggests that these features play significant roles in modulating local temperatures. In contrast, the variable performance for bare soils and the generally weak associations for grasslands highlight that the effects of these land covers are more context \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**LULC Class** & \begin{tabular}{c} **Morning to** \\ **Afternoon** \\ \end{tabular} & \begin{tabular}{c} **Afternoon to** \\ **Evening** \\ \end{tabular} & \
\begin{tabular}{c} **Evening to** \\ **Midnight** \\ \end{tabular} &
\begin{tabular}{c} **Mean** \\ **Adjusted \(R^{2}\)** \\ \end{tabular} \\ \hline Bare Soil & \(-137.3\%\) & \(+1071.7\%\) & \(-10.7\%\) & 0.266 \\ Building & \(+8.7\%\) & \(+42.6\%\) & \(+10.4\%\) & 0.199 \\ Grassland & \(+298.2\%\) & \(-86.8\%\) & \(-133.3\%\) & 0.076 \\ Paved Road & \(+63.8\%\) & \(+40.7\%\) & \(-9.3\%\) & 0.433 \\ Water Body & \(-45.1\%\) & \(+88.2\%\) & \(-13.8\%\) & 0.536 \\ \hline \hline \end{tabular} Note: Values represent percentage changes in adjusted \(R^{2}\) between consecutive time periods. Positive values indicate strengthening relationships, while negative values indicate weakening relationships.  
\end{table} 
Table 4: Percentage changes in adjusted \(R^{2}\) values between consecutive time periods for different LULC classes. dependent, potentially influenced by additional factors such as moisture content, albedo variations, or adjacent land uses.
## 2 Materials and Methods
### Optimization of Magnetic Bead + EGCG Complex Concentration  
Various EGCG:T-Fe\({}_{3}\)O\({}_{4}\) weight ratios were investigated to determine the optimal composition of EGCG-modified magnetic beads. The synthesis was performed using the same protocol described above but with adjusted relative concentrations of EGCG and the T-Fe\({}_{3}\)O\({}_{4}\) magnetic nanoparticles. Three different weight ratios (10:1, 0.1:1, and 1:1) were evaluated. For the 10:1 ratio, 1 mg of EGCG and 0.1 mg of T-Fe\({}_{3}\)O\({}_{4}\) were used. The 0.1:1 ratio utilized 0.1 mg of EGCG and 1 mg of T-Fe\({}_{3}\)O\({}_{4}\), while the 1:1 ratio employed equal amounts of EGCG and T-Fe\({}_{3}\)O\({}_{4}\) (1 mg each). All other synthesis conditions remained consistent across the different ratio preparation processes.  
The optimal concentration of EGCG-modified magnetic beads for exosome isolation was investigated using urine samples. Three different magnetic bead concentrations were evaluated: 0.1\(\times\) (1 mg EGCG and 0.1 mg T-Fe\({}_{3}\)O\({}_{4}\)), 1\(\times\) (1 mg each of EGCG and T-Fe\({}_{3}\)O\({}_{4}\)), and 10\(\times\) (0.1 mg EGCG and 1 mg T-Fe\({}_{3}\)O\({}_{4}\)). During the isolation process, urine samples were mixed with the magnetic beads at different concentrations under identical conditions (room temperature incubation, constant rotation speed, and fixed incubation time). The only variable was the concentration ratio of the magnetic beads. The comparative analysis of the exosome isolation efficiency among the three different bead concentrations revealed that the 10\(\times\) ratio (0.1 mg EGCG and 1 mg T-Fe\({}_{3}\)O\({}_{4}\)) yielded the optimal exosome capture efficiency.