esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Methodology Analysis
#### 3.2.2 Dissimilarity Filtering Comparison
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methodology** & **\# Samples** & **Precision** & **Recall** & **F-Score** \\ \hline Proposed & F0: 181 & & & \\  & F1: 21 & 0.92 & 0.93 & 0.92 \\  & F2: 15 & & & \\  & F3: 17 & & & \\ Entropy & F0: 1427 & & & \\ sampling & F1: 794 & 0.81 & 0.80 & 0.80 \\  & F2: 494 & & & \\  & F3: 332 & & & \\ Margin & F0: 2014 & & & \\ Sampling & F1: 995 & 0.84 & 0.83 & 0.84 \\  & F2: 521 & & & \\  & F3: 499 & & & \\ Random & F0: 107 & & & \\ Sampling & F1: 52 & 0.73 & 0.66 & 0.66 \\  & F2: 21 & & & \\  & F3: 20 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison between the proposed approach and different uncertainty sampling techniques. Figure 10: t-SNE map for the training data used in the source modelling and the target samples selected with the uncertainty sampling methods. The samples marked with an “x” correspond to target samples selected that are not correctly predicted after the model evaluation.
# Thai Rice Vinegars: Production and Biological Properties
## 1 Introduction
However, unpolished rice has become more popular due to health concerns, as its membrane contains many nutrients and trace elements. Recently, various varieties of unpolished rice have been shown to contain pigments, such as black rice, red rice, brown rice and purple rice [12; 13]. Many studies have indicated that these colored rice varieties contain more nutritional advantages than white or polished rice, such as proteins, vitamins and minerals. Furthermore, the presence of dietary fibers and important phytochemicals, such as essential oils, flavonoids, phenolic compounds and anthocyanins, have also been reported in colored rice [14; 15; 16]. Many studies have described the biological properties of Thai colored rice. The phenolic compounds and pigments in Thai rice are indicated as effective antioxidative compounds. Studies of Thai colored rice, such as black glutinous rice (Niew Dam) and black fragrant rice (Hom Nil), have revealed a relation between the amount of anthocyanins and the antioxidative activity. The greater the amount of anthocyanins, the more antioxidative activities are detected. Furthermore, the content of anthocyanins in Thai colored rice is higher than non-colored or polished rice [13]. Additionally, black glutinous rice has been reported as the colored rice containing the highest amounts of anthocyanins in eight studied rice samples. The anthocyanins detected in black glutinous rice are cyanidin 3-glucose and peonidin 3-glucose [15]. Moreover, phenolic compounds such as ferulic acid, p-coumaric and vanillic acid are found in Thai black glutinous rice. Other biological effects of colored rice such as antimutagenic activity, anticancer activity, inhibition of allergic reactions, reduction of cardiovascular disease development and decreased plasma levels of cholesterol have been reported [14; 16; 17]. Although rice vinegar is widely used not only for food seasoning, but also health benefits in many Asian countries such as China and Japan, production of rice vinegar and using rice vinegar for health benefit in Thailand are not popular as expected. This study aims to produce vinegar from four cultivars of traditional Thai rice: glutinous rice (Kiaw Ngu), black glutinous rice (Niew Dam), polished rice (Hom Mali) and black fragrant rice (Hom Nil), using solid-state fermentation. This method offers many advantages such as simple, inexpensive, high yield of productivity and suitable for many agricultural product utilizations including vinegar fermentation from rice. The biological properties of produced vinegars such as antibacterial activity, antioxidant activity and anticancer activity are determined to enhance the quality on health benefits of rice vinegars. Furthermore, rice vinegar production might be an alternative way for the utilization of traditional Thai rice.
## 3 Methodology  
Compared with horizontal land surface, the impacts of rugged topography on signals recorded by satellite sensor mainly come from two aspects: modification of the STS angular configuration and re-distribution of solar irradiance [5]. By compensating for these influences, first-order approximation of bi-directional reflectance for rugged terrain was set to equalize reflectance under Lambertian assumption. Then, the STS angle configuration was resolved by the STS slope coordinate system. An equation between the first-order approximation of bi-directional reflectance and the solar irradiance received by slope surface was established. Then, NDVI thresholds were used to divide land targets into eleven groups and MODIS BRDF parameters resampled to the TM resolution were averaged within each group. Finally, the equations established were solved by adding group-averaged BRDF parameters, and bi-directional reflectance was achieved for each pixel, with topographic effect being corrected.
## 4 Experimental Results
### Comparison and Results  
The quantitative results obtained from different state-of-the-art models, including our proposed model, using the Massachusetts road dataset are presented in Table 1, and those from using the DeepGlobe road dataset are presented in Table 2. Models such as U-Net [21], Attention U-Net [25], DeepLabV3+ with ASPP [13], and SegNet [34] were experimented with within our setup, and the metrics' results were reported following training and testing. The results for RFE-Link Net [28] and D-LinkNet [27] and DCSFEP [29] are reported from the original papers due to the relevancy in the setup and usage of the same dataset. As seen in Table 1, our proposed model achieved better performance using the Massachusetts dataset based on the IOU and Precision metrics, with scores of 67.21 and 81.38, respectively, compared to all other models. Additionally, our model showcased better performance compared to all models for the \(F_{1}\) Score, with 79.29, while the advanced model RFE-Link Net slightly outperformed our model with a score of 80.07.  
Similarly, as seen in Table 2, our proposed model was also successful in achieving better performance with the DeepGlobe dataset for the IOU and Precision metrics, with scores of 71.61 and 83.19, respectively, compared to all other models. Along this, the experimental results followed the same trend as obtained in above, in which our model again depicted better performance compared to all models for the \(F_{1}\) Score, with 81.75, while the advanced model RFE-Link Net slightly outperformed our model with a score of 82.85.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **IOU (\%)** & **Precision (\%)** & \(F_{1}\) **Score (\%)** \\ \hline U-Net [21] & 64.19 & 80.23 & 74.78 \\ \hline Original DeepLabV3+ [13] & 65.92 & 80.04 & 75.60 \\ \hline SegNet [34] & 58.67 & 78.56 & 73.73 \\ \hline DCSFEP [29] & 62.48 & - & 76.59 \\ \hline D-LinkNet[27] & 63.74 & 75.89 & 77.86 \\ \hline RFE-LinkNet [28] & 66.77 & 80.88 & **80.07** \\ \hline Proposed Model & **67.21** & **81.38** & 79.29 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantitative observation of results obtained from all models in Massachusetts road dataset. Bold values indicate the best performance in the respective metric.
## 2 Materials and Methods
### Volume of Fluid (VOF) Model  
The Volume of Fluid (VOF) model [19] is extensively applied in simulating the sloshing of oil tanks and belongs to the computational fluid dynamics (CFD) method. The model considers the sum of volume fractions for all components to be 1. By defining the volume fraction of each phase, the movement of each phase can be tracked and the interfaces between phases can be determined. The multiphase model described in this paper is a two-phase model, with the two phases centering upon air and liquid, respectively. It assumes the volume fraction of the air phase to be \(\alpha_{air}\). In each computational unit, three possible scenarios may occur [20].  
Figure 1: Two-way fluid-structure interaction.  
\(\alpha_{air}=1\): The calculation element is entirely filled with the gas phase, as indicated by the red portion in the Figure 2.
2. \(\alpha_{air}=0\): The calculation element is completely filled with the liquid phase, as depicted by the portion in the blue.
3. \(0<\alpha_{air}<1\): The calculation element is partially filled with the liquid phase, and the point where \(\alpha_{air}=0.5\) can be defined as the LNG liquid surface.  
The control equations of the VOF model are based on the Navier-Stokes equations, and one must consider the influence of gravity and surface tension during the calculations. The control equations for the VOF model are follows:  
Continuity equation:  
\[\frac{\partial\rho}{\partial t}+\
abla\cdot(\rho\overset{\rightarrow}{u})=0 \tag{1}\]  
Momentum equation:  
\[\frac{\partial(\rho\overset{\rightarrow}{u})}{\partial t}+\
abla\cdot(\rho \overset{\rightarrow}{u}\overset{\rightarrow}{u})=-\
abla p+\rho g+\
abla \cdot[\mu(\
abla\overset{\rightarrow}{u}+\
abla\overset{\rightarrow}{u}^{ T})]+\overset{\rightarrow}{F}_{CSF} \tag{2}\]  
Energy equation:  
\[\frac{\partial(\rho E)}{\partial t}+\
abla\cdot(\rho\overset{\rightarrow}{u} (\rho E+p))=\
abla\cdot(k_{eff}\
abla T)+\dot{m}_{\text{lg}}h_{\text{lg}} \tag{3}\]  
where \(\rho\) is the fluid density, \(u\) is the velocity vector, \(p\) is the pressure, \(\mu\) is the dynamic viscosity, \(g\) is the gravitational acceleration, \(F_{CSF}\) is the surface tension term, \(E\) is the energy, \(T\) is the temperature, and \(k_{eff}\) is the effective thermal conductivity. The material properties presented in the equations are determined by the presence of each phase in each fluid volume.  
FCSF is calculated by the continuum surface force (CSF) model [21]:  
\[\overset{\rightarrow}{F}_{CSF}=2\sigma_{l_{\text{D}}}\times\frac{\alpha_{l} \rho_{l}C_{\text{v}}\
abla\alpha_{\text{v}}+\alpha_{\text{v}}\rho_{\text{v}}C _{l}\
abla\alpha_{l}}{\rho_{l}+\rho_{\text{v}}} \tag{4}\]  
where \(\sigma_{l_{\text{D}}}\) is the surface tension pressure; \(C\) is the surface curvature.  
The subscript \(l\) is the liquid phase, and \(v\) is the air phase.
## 5 Conclusions and Future Works  
In conclusion, we introduced a SHAP-values-assisted feature subset selection (FSS) method for the identification of relevant features in multivariate time series (MTS) problems in the context of a data center. It is a class of additive feature attribution values that obey desirable accuracy, missingness, and consistency properties. It is also more consistent in attributing feature importance and is more in line with human intuition. Furthermore, SHAP addresses the high frequency and cardinality of features that occur in feature-importance-based feature selection methods. SHAP computed the importance of each feature based on game theory concepts that calculate the contributions of each feature towards model development. As a result, the SHAP-value-based FSS method is useful for identifying relevant FSS to effectively model data center operations while providing insight into the data, improving model performance, and lowering computational expenses. Understanding the underlying patterns enables data center operators to make data-driven decisions while maintaining their data center operations ahead of time, ensuring service continuity and resource availability. We proved the effectiveness of the SHAP-assisted FSS method compared with several commonly used feature selection approaches by using real data streams obtained from an HPC data center (ENEA CRESCO6) cluster. We demonstrated the experiment by selecting ten of the most significant features of each method. The results in Tables 2 and 3 demonstrate that, with better interpretability, the SHAP-assisted FSS method outperformed the other commonly used feature selection methods discussed in this paper. Additionally, unlike other methods, SHAP is a model-agnostic approach that can be applied to machine learning and deep learning techniques.  
The method needs further investigation with more data and validation with other additive feature attribution methods in future work. We will extend our investigation to apply the SHAP method for explaining complex black-box models and determining controllable features to identify the optimal solution for optimizing data center operations. We will also extend our work to investigate the SHAP method for identifying important features in real-time predictions in both machine learning and deep learning forecasting models.  
Conceptualization, methodology design, experimentation and analysis, and writing the paper, Y.G.; supervision, editing and reviewing, D.D.; supervision, S.N.; editing and reviewing, D.D.C., editing, reviewing the paper, M.C. All authors have read and agreed to the published version of the manuscript.  
This paper received funding from the European Union's Horizon 2020 research and innovation program under the [PERSON] grant agreement No 778196. However, the contents of the paper reflect only the authors' views, and that the Agency is not responsible for any use that may be made of the information it contains.  
Not applicable.  
The authors state that no conflict of interest.
## 3 Results and Discussion
### Discussion
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classification** & **1 st Phase** & **2 nd Phase** & **3 rd Phase** \\ \hline \multirow{2}{*}{PMU Application} & Wide Area MonitoringSystem & Wide Area Monitoring and Control & Wide Area Monitoring Protection and Control \\ \hline \multirow{2}{*}{Ensure Stability} & Voltage Stability & Frequency Stability & Angle Stability \\ \hline \multirow{2}{*}{Countermeasure} & \multirow{2}{*}{Stability Assessment} & Under Frequency Load & Tripping Generation \\  & & Shedding (UFLS) & Units \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Each technology phase target for phasor measurement unit (PMU) application. Figure 20: Frequency and angle responses for the cases with and without special protection scheme based on proposed method; (**a**) frequency, (**b**) angle. and determined the adequate SPS. When a contingency occurs, the generator electrical output and angle increase. Subsequently, increasing the angular swings in some generators may result in the loss of their synchronism. As shown from the results, the equilibrium state between the electromagnetic torque and mechanical torque of each synchronous machine in the system is restored by the SPS. This implies that the acceleration energy accumulated during the fault must be released quickly by tripping more generators. Thus, the proposed method may be applied for the stabilization to control the detected instability state by calculating the stability margin. The proposed method allows for the stabilizing of the system to be elaborated; further, it can be extended for conceptual virtual loads. In summary, the advanced SPS connected with the WAMPAC system for real-time assessment and protection control can ensure angle stability.
## 4 Conclusions  
We investigated a tuning capability of CuO and Na2 CO3 concentration on thermal properties (Tg, Tx, Ts, CTE) and optical properties of a PSAB glass system. From TMA analysis, the thermal parameters Tg and CTE showed a decreasing trend with increasing CuO concentration. As a function of Na2 CO3 concentration, the Tg decreased but CTE increased. It was observed that the CTE difference for singly CuO- and Na2 CO3-doped PSAB systems was found to be high of the order of \(2\times 10^{-6}\)/K. Thus, the tuning capability of codoping on CTE was investigated and the optimum concentrations of CuO and Na2 CO3 were found to be 1 wt% and 2 wt%, respectively. The difference of CTE between the optimized glass system (\(9.89\times 10^{-6}\)/K) and the FTO coated glass panel (\(10.23\times 10^{-6}\)/K) was found to be \(0.34\times 10^{-6}\)/K. The optical absorption at 810 nm also increased for the codoped PSAB system and reached up to 100%. The glass fiber with a diameter of 180 \(\upmu\)m was successfully pulled for the codoped PSAB glass system. The tensile strength tests were performed for the laser-sealed FTO substrates, using the glass fiber sealant, and sealing strength was found to be 41 MPa. Therefore, the investigated PSAB-based glass fiber was proved as a potential candidate as a laser sealing material in the packaging industry.  
Figure 10: **(a)** Schematic diagram of zig mounted with FTO substrates; **(b)** universal tensile tester mounted with zig; **(c)** tensile load changes with time for the laser-sealed FTO substrate (laser power of 35 W, laser scan velocity of 40 mm/s, laser scanning cycle of 300, and preheating of 190 \({}^{\circ}\)C).  
Figure 9: The microscopic photos of different stages of laser sealing as a function of sealing cycle and plate temperature. Glass fiber type of sealant is based on 1 wt% CuO-1 wt% Na2 CO3 codoped PSAB glass system.  
**Author Contributions:** Laser sealing and Analysis, S.Y.K.; Glass Fabrication, J.P.; Formal Analysis, S.H.K.; Writing--Original Draft Preparation, J.H.C.; Writing--Review & Editing, L.K., J.H.K. and J.H.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by [the ministry of trade, industry and energy (MOTIE) and the Ministry of SMEs and Startups (MSS, Korea)] by grant number [No. N0001886, S2725003].  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Generic Language for Partial Model Extraction from an IFC Model Based on Selection Set
## 3 Concept of the Selection Set
### Extraction Rules Based on Selection Set  
According to Condition 1, the Selection Set has at least one Entity \(e\). In the IFC model, all IFC instances matching the Entity \(e\) should be extracted. Furthermore, other IFC instances related to explicit and inverse attributes of the target entity should be extracted according to Rule 2 and Rule 3.  
Rule 4: IFC instances matching Entity \(e\) in the Selection Set are contained in the partial model.  
\[\forall e[e\in S\wedge\exists f_{M}[f_{M}(e)=E_{e}]\to E_{e}\subset M_{p}] \tag{6}\]  
where \(E_{e}\) is the set of entities related to Entity \(e\); and \(f_{M}\) is a function from \(e\) to \(E_{e}\), working on original model \(M_{o}\).  
Numerous complex relations exist between various objects in a building project. For example, the binary relation can be divided into different types of relations, such as containment, parallel, and crosscutting relations. Consequently, relation entities should be set into Selection Set. All corresponding IFC instances within the user-defined relations in the Selection Set would be extracted to form the partial model. In this study, the object which contains other object(s) or is relied by other object(s) is set as the relating object, and other object(s) are called related object(s).  
Rule 5: The corresponding relating entity and related entities are included in the partial model, when a relation entity is included in the Selection Set.  
\[\begin{split}&\forall e_{rel}\exists e_{relating}\exists E_{related}\\ &[[e_{rel}\in S\wedge IFCREL(e_{rel},e_{relating},E_{related})]\\ &\rightarrow[(e_{relating}\cup E_{related})\subset M_{p}]]\end{split} \tag{7}\]  
where \(e_{rel}\) is a relation entity; \(e_{relating}\) is a relating object entity; \(E_{related}\) is the set of related object entities; and \(IFCREL\) is a function to test if the relation entity \(e_{rel}\) exists between relating object entity \(e_{relating}\) and related object entities \(E_{relatal}\).  
The attributes of IFC entity represent different essential characteristics from other entities. These attributes can form different rules. As a result, the according model data can be extracted by designing different rules.  
Rule 6: The entities included in the partial model satisfy the rules in the Selection Set.  
\[\forall r[r\in S\to r\cap M_{p}] \tag{8}\]  
where \(r\) is a Rule.
# Examination of Landslide Susceptibility Modeling Using Ensemble Learning and Factor Engineering
## 3 Methodology
### XGBoost  
XGBoost (eXtreme Gradient Boosting) is an optimized gradient boosting algorithm that extends traditional gradient boosting decision trees (GBDTs). Designed to handle large-scale and high-dimensional data, XGBoost iteratively adds decision trees to correct the residuals of previous models and minimizes the loss function by incorporating regularization terms during each tree construction. This method assumes that additive tree structures can effectively approximate complex patterns, while regularization terms (L1/L2) suppress irrelevant features, reducing overfitting and improving the generalization ability. XGBoost excels in regard to binary classification, multi-class classification, and multi-label classification tasks, providing both class labels and probability predictions for each class. Its key advantages include the incorporation of L1 and L2 regularization to mitigate overfitting, parallel processing for efficient tree construction, and it has a built-in mechanism to handle missing values. Additionally, XGBoost employs pruning techniques  
Figure 4: Graphical representation of the overall methodology.  
to prevent overfitting and integrates cross-validation functionality to automatically determine the optimal number of iterations, significantly streamlining the model selection and tuning process.
# Pressure-Reducing Design of 3D-Printed Diabetic Shoe Midsole Utilizing Auxetic Lattice Structure
## 2 Research Methods
### Foot Model Construction and Gait Measurement  
This study utilized a three-dimensional foot model to simulate the actual conditions of the foot-shoe interaction during motion. This foot model was derived from a 3D scan of the left foot of a male (EU size 42), 175 cm in height, using an eFoot 350 medical 3D foot scanner (Stereo3D Technology, Shenzhen, China) with a scanning accuracy of 0.5 mm. Additionally, a skeletal model corresponding to the foot size was embedded within the foot model to produce the most realistic foot characteristics.  
An optical motion capture system (NOKOV Mars 9H, Beijing, China) was employed to collect foot motion data and the body's center-of-mass coordinates under walking and running conditions. Markers for motion capture were attached to the heel, toe, both sides of the ankle, calf, thigh, abdomen, and buttocks. The body's center of mass, commonly used in biomechanics and movement balance studies, is located 40% of the distance from the abdomen to the buttocks [33]. Measurement required the participant to be barefoot and wear special trousers for marker tracking to collect the most accurate foot motion trajectories. The participant was also instructed to hold their arms up during data collection to prevent arm movements from obstructing camera detection during motion. The optical motion capture system used in this study had a resolution of 9 million pixels and a frequency of 300 Hz. This study imported the gait data into an FEM to simulate the stress and strain conditions of the foot-footwear interface during walking and running.  
Figure 2D shows that the amplitude of motion in the direction perpendicular to the ground (\(y\)-axis) was comparable across the walking and running states. However, compared to running, the walking state exhibited a more significant period in the waveform, indicating more gradual changes. This suggests that the speed of motion in the direction perpendicular to the ground is lower during walking.