esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Design and Laboratory Testing of Encapsulated PCM Panels for PCM-Air Heat Exchangers
## 3 Results and Analysis
### Laboratory Test
#### 3.2.4 Thermal Load  
During the melting and solidifying process, the air was cooled down or heated up to a correspondent cooling (\(Q_{c}\)) or heating load (\(Q_{th}\)). To estimate those values, Equations (14) and (15) were used. The heating and cooling cycles ceased when the outlet temperature stabilizes and achieve values close to the inlet temperature. These results are presented in Figure 21. Interestingly, even the existing TB having 18 panels per TB and the new TB 14 panels (representing less 22% heat transfer surface area) the average heating/cooling power are remarkably similar. This result shows that the new panel provides a faster response when compared to the existing panel. Curiously, for the melting period with two TB, the opposite occurs. This is probably caused by fluctuations in temperature during the transition from charge to discharge mode and vice-versa.  
Figure 21 shows that after 4 h for 1 TB, the TB is fully melted or solidified as a small amount of energy is released or absorbed by the TB. For the new TB, 6 h 30 min is needed to complete the cycle and after this time, the energy released or absorbed by the TB remains stable. A similar pattern occurs with 2 TB (Figure 21, right) where more time for  
Figure 21: Thermal load for existing and new panel with one (left) and two (right) thermal batteries with error bar showing the uncertainty of 3.7%.  
solidification was needed. This is explained by 8 kg of extra PCM the two TB are holding, however, the extra PCM reflects in more energy stored.  
The PCM used for this present study (CrodaTherm 24 TM) was subjected to a 3 LC (three-layer-calorimeter) test in order to assess heat storage properties [28]. The results of this test shows that the energy stored during the heat up from 15 to 30 \({}^{\circ}\)C and cooling from 30 to 15 \({}^{\circ}\)C are in agreement with the laboratory tests as it can be seen in Table 11. The results present an average of 105.4 Wh (\(\pm\)44.5 Wh) or 4.4% (\(\pm\)6.9%).  
Holding 30% more material, the new panel needs more time to solidify and melt when compared to the existing panel. Table 12 summarizes the time required and the increase in percentage between new and existing TB.  
These results can also be seen from the cooling and heating loads. It was compared with a 3 LC test and the results presented a good agreement, indicating an average difference of 105.4 Wh (\(\pm\)44.5 Wh) or 4.4% (\(\pm\)6.9%).
# Research on Dynamic Reserve and Energy Arbitrage of Energy Storage System
## 4 Description and Introduction of Simulation Environment  
### System Model and Settings  
This study takes the Kinmen grid as the system under study. Kinmen Island is a small island west of Taichung City (R.O.C.), Taiwan, very close to mainland China. The winter load is about 21.9 to 42.95 MW while the summer load is about 43.26 to 73.81 MW.  
Kinmen Island uses diesel to generate electricity making its cost usually higher because of the cost of transporting fuel. On the other hand, solar energy is a cheaper replacement for fossil fuel.  
Currently Kinmen Island has two power plants and a 12.3 MW PV plant. Power plant 1 has 10 heavy oil diesel generators. Power plant 2 has 6 light oil generators and 2 ESSs. ESS1 is 2 MW/1 MW/1 MWh lithium-ion batteries used for frequency regulation, while ESS2 is 1.8 MW/10.8 MWh sodium-sulfur batteries used for energy arbitrage. It is expected that an additional 4 MW/24 MWh ESS will be built in 2023 for energy arbitrage.  
This study considers the future winter conditions of Kinmen Island. Only heavy oil diesel generators will be used because of the low operational cost of heavy oil diesel generator. There is a 27 MW PV plant with the three ESSs mentioned in the previous paragraph. The system has 4-22.8 kV busbars, 4-11.4 kV busbars, 4 main transformer loads, and 2 substations. A simplified schematic diagram of connections between all facilities is shown in Figure 3. The trip settings for the underfrequency relays has four levels, 57.3, 57.0, 56.5, and 56.0 Hz. After triggering the underfrequency relay, it takes about 5-6 cycles to open the circuit breaker [5; 29]. In this study, \(F_{nadir}\) is set to 57.3 Hz, in order not to trigger UFLS.  
The ED is solved using mixed integer linear programming (MILP) using the IBM CPLEX 12.10.0 solver. The computer used is for these simulations is an Intel Core (TM) i5-7500 CPU @ 3.4 GHz. 16G RAM. PSS(r)E version is 33.4.0. It is coded in a Python program for automated simulation.
# Additional Taxi-Out Time Prediction for Flights at Busy Airports by Fusing Flow Control Information
## 4 Experiment and Results
### Results and Discussion  
#### 4.2.1 Performance Comparison of Different Prediction Models  
To validate the effectiveness of the proposed NOA-XGBoost model, we compare it with commonly used taxi-out time prediction methods RF, SVR, and the traditional XGBoost algorithm. The results are shown in Table 5 and Figure 9.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Parameter** & **Value** \\ \hline NOA & Population size & 50 \\  & Maximum iterations & 100 \\ \hline  & N estimators & 100 \\  & Max depth & 6 \\  & Learning rate & 0.3 \\  & Gamma & 0 \\  & Subsample & 1 \\  & Colsample bytree & 1 \\  & Reg alpha & 0 \\  & Reg lambda & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Algorithm parameter settings.  
As seen in Table 5, the NOA-XGBoost algorithm outperforms other algorithms across all six metrics. Compared to the XGBoost model, it improves MAE, RMSE, R\({}^{2}\), \(\pm\)3 min accuracy, \(\pm\) 5 min accuracy, and (+5, \(-\)10) min accuracy by 4.19%, 4.21%, 2.67%, 1.32%, 1.08%, and 1.04%, respectively. This indicates that optimizing XGBoost parameters using the NOA algorithm can enhance its prediction accuracy and precision. Compared to the RF model, the NOA-XGBoost algorithm improves MAE, RMSE, R\({}^{2}\), \(\pm\)3 min accuracy, \(\pm\)5 min accuracy, and (+5, \(-\)10) min accuracy by 8.44%, 7.46%, 5.48%, 5.48%, 2.17%, and 2.11%, respectively. Compared to the SVR model, the NOA-XGBoost algorithm shows better performance, with improvements in MAE, RMSE, R\({}^{2}\), \(\pm\)3 min accuracy, \(\pm\)5 min accuracy, and (+5, \(-\)10) min accuracy of 9.25%, 14.42%, 11.59%, 5.48%, 2.17%, and 2.11%, respectively.  
Figure 9 shows the prediction results for flight numbers 1050 to 1300, with an enlargement of the prediction results for 100 flights numbered 1050 to 1100. The enlarged view indicates that the NOA-XGBoost model provides the best fitting results.
# Deep Feature Fusion via Transfer Learning for Multi-Class Network Intrusion Detection
## 2 Related Work
[19] proposed an automatically detecting early depression (AEDE) device for depression prevention and developed a method that fuses pupil wave and pulse rate variability (PRV) signals via a multi-feature cross-attention (MFCA) technique. [PERSON] et al. [20] demonstrated that despite transfer learning's excellent performance in isolation, its limitations become evident in data-scarce environments. Integrating transfer learning with feature fusion to effectively combine complementary information from both domains achieves outstanding generalization performance and robustness, even when confronted with noise and limited data. This approach shows significant usefulness for accurately identifying complex network traffic and diverse abnormal signal patterns. Therefore, this study proposes an intrusion detection model that surpasses existing approaches by applying transfer learning for performance enhancement in conjunction with advanced feature fusion techniques. The key contributions of this research in improving attack type classification performance for network anomaly detection are as follows:  
1. This study employs the CIC-IDS dataset for attack-type classification in network anomaly detection. To address the persistent class imbalance challenge documented in previous research, the methodology incorporates strategic data preprocessing: rare attack categories were eliminated, while similar attack types were consolidated. Subsequently, random resampling techniques were implemented to equilibrate data distribution, accompanied by standard scaling to normalize all features to a mean of 0 and variance of 1, thereby enhancing the model's generalization capabilities across diverse network environments. 2. By transferring learned feature representations from source domain models to the target domain, the proposed approach achieves superior detection performance despite limited training data availability. Furthermore, the model effectively leverages common feature spaces even when the source and target domains exhibit partial incongruence, significantly reducing computational training requirements while mitigating overfitting phenomena that commonly plague complex network detection systems. 3. To maximize detection efficacy, feature fusion techniques are integrated with the transfer learning-based architecture, amalgamating feature representations utilized in both binary classification and multi-class classification processes. This integration enables the model to synthesize diverse feature perspectives and derive more generalized pattern recognition capabilities, substantially enhancing its precision in identifying and categorizing various network attack typologies.
## 3 Results
### Meaningful Thermal Stress Risk Frequency  
Following the methodology described in Section 2, \(RiskT\) and \(UTCI\) values were computed for each hour of the data collected from M. v. Juvara station during 2019. These are plotted in Figures 2 and 3 to qualitatively analyze how this thermal-stress-related risk was manifesting, and quantitative reported frequency data are summarized in Table 3.  
The risk for both \(RiskT\) and \(UTCI\) is notably concentrated in summer (29.49% and 30.58%). However, Figure 3 displays that for \(UTCI\), rare and low intensity risk arose during the mid-season (2.05%), and no risk was registered during winter. On the other hand, Figure 2 presents that \(RiskT\) would communicate a higher risk intensity in summer, a larger frequency and intense risk for the mid-season (3.42%), and a rare risk during winter (0.66%).  
The above could result from the assumption made on \(MRT\) for computing \(UTCI\) and the allocated weights used for estimating \(RiskT\). The classification made with the \(RiskT\) criterion was used, given the significant effect that direct solar radiation falling on a person has on heat exchange and thermal perception [33].  
Figure 3: Degree of thermal stress expressed in \(UTCI\), based only on the values for 2019 from Milano v. Juvara of \(t_{db-air}\), \(RH\), and \(V_{a}\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**RiskT**} & \multicolumn{2}{c}{**UTCI**} \\ \hline
**Season** & **\# of** & **Hours at Risk** & **Ratio** & **Hours at Risk** & **Ratio** \\  & **Hours** & & & & \\ \hline Whole year & 8760 & 1324 & 15.11\% & 1343 & 15.33\% \\ Summer and mid & 4392 & 1295 & 29.49\% & 1343 & 30.58\% \\ Summer & 2208 & 1145 & 26.21\% & 1253 & 28.69\% \\ Mid & 2184 & 150 & 3.42\% & 90 & 2.05\% \\ Winter & 4368 & 29 & 0.66\% & 0 & 0.00\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary results of the allocated thermal stress, when using \(RiskT>0.5\) or \(UTCI>26\) as the criterion, differentiated by season.
# Review of Hygroscopic Coating on Aluminum Fin Surface of Air Conditioning Heat Exchanger
## 4 Challenges and Future Works
### For Desiccant-Coated Heat Exchangers  
DCHE has shown great potential in air condition system applications. Combining with mentioned above, there are some potential areas for consideration.  
1. After the addition of the hygroscopic salts, the adsorption capacity of the conventional desiccants has been significantly improved. However, their negative consequences, such as corrosion and deliquescence, require further research.
2. How to reduce the pressure drop and enhance the mass and heat transfer of the DCHE, and reduce the desiccants regeneration temperature need to be developed.
3. In the air conditioning system, the dehumidification and regeneration system is complex, difficult to control, and large, which requires a more competitive design.
## 3 Results and Discussion  
### The Uncontaminated Water Analysis  
#### 3.1.1 Static Contact Angle Measurement  
Figure 2 shows the static contact angles of the water drops onto different plate surfaces with and without vibration. Without vibration, the contact angles for iron casting, steel, PVC and PE were \(58.4^{\circ}\), \(48.2^{\circ}\), \(40.3^{\circ}\), \(32.5^{\circ}\), respectively. The reasons for this were due to the fact that non-metals and metals have different roughness. As is shown in Figure 3b, the vibration decreased the contact angles by 3-10%, which was due to the fact that the vibration helped with the water drops' dispersion. A 300 Hz vibration could decrease the water drops' contact angles onto iron casting, steel, PVC and PE to \(56.3^{\circ}\), \(43.4^{\circ}\), \(37.9^{\circ}\), \(30.8^{\circ}\), respectively.  
Figure 2: The static contact angles of water drop onto the iron casting, steel, PVC, PE (**a**) without vibration; (**b**) with 300 Hz vibration.  
Figure 3 shows that the contact angles of the water drops onto the different pipeline materials' surfaces with and without 300 Hz vibration with different aging times. The vibration influenced the solid wettability [23, 26, 46]. The results indicated that when the aging time was 0 d, the vibration decreased the contact angles by 50%, as shown in the results in Figure 3. However, when the aging time increased from 0 to 300 Hz, the contact angles without vibration were lower than those with 300 Hz vibration, the reasons were as follows [37, 47, 48]: on the one hand, when the vibration aging time increased by 100%, the erosion for different materials was enhanced, and the roughness alteration was enhanced, then the contact angles increased by 30%. On the other hand, during the contact angle measurement process, the vibration helped to disperse the water drops. Comparing with the iron casting and steel, the contact angles' alterations were low for PVC and PE, which was due to the fact that the PVC and PE erosion was low.  
Figure 4 shows the vibration frequency on the dynamic contact angles of water drops onto the iron casting, steel, PVC and PE with 7 days aging time. The results indicated that the vibration could make the dynamic contact angles higher. When the vibration frequency increased to 300 Hz, the dynamic contact angles remained stable. Therefore, 300 Hz was chosen as the studied frequency.  
Figure 3: The static contact angles of water drops onto the (**a**) iron casting; (**b**) steel; (**c**) PVC; (**d**) PE, without or without 300 Hz vibration with different aging time.
# Multi-Objective Electric Vehicles Scheduling Using Elitist Non-Dominated Sorting Genetic Algorithm
## 2 Optimization Problem and Methodology
### Hybrid Evolutionary Algorithm  
The evolutionary algorithm used in this paper (NSGA-II) was proposed by [PERSON] et al. [PERSON] et al. (2017), and since then several authors have been using this algorithm to solve different multi-objective optimization problems [PERSON] et al. (2017), particularly in the power system field [PERSON] et al. (2017); [PERSON] et al. (2018). Figure 2 displays the flowchart of the NSGA-II algorithm.  
The NSGA-II algorithm starts with the initialization of several parameters, namely the population size \(N_{POP}\), the number of generations \(N_{GEN}\), the crossover probability \(p_{Cross}\) and the mutation probability \(p_{Mut}\). Then, the parent population \(P_{t}\) is generated based on the deterministic technique, which is further explained below. Each individual of \(P_{t}\) is evaluated for the two objective functions that compose the multi-objective function. The next step consists in ranking all individuals of \(P_{t}\) and determining the crowding distance among them using the methods described in [PERSON] et al. (2017). The rank mechanism ranks all individuals (or solutions) based on nondomination levels: level 1 is the best one, level 2 is the second-best one, and so on. The crowding distance metric determines an estimate of the perimeter of the cuboid formed by using the nearest neighbours of the solution as the vertices. The idea is to assign a high crowding distance value for solutions located in a less crowded region.  
A binary tournament selection with the crowded tournament operator, which uses the ranking and crowding distance, is used to select the best individuals of \(P_{t}\). The first criterion is the selection of the individual with the lower rank (meaning it is better). Otherwise, if both individuals have the same rank, then the solution with a higher crowding distance is selected. The objective of this operator is to obtain a well-spread population for the next generation \(t+1\). Next, the crossover and mutation are performed to generate an offspring population \(Q_{t}\) with same size \(N_{POP}\). Once again, all individuals of \(Q_{t}\) are evaluated for each objective function.  
Thereafter, the two populations \(P_{t}\) and \(Q_{t}\) (i.e., parent and offspring, respectively) are combined creating a new population \(R_{t}\) with twice the size of the two populations; then, the combined population is ranked, and the crowding distance is also determined. The next step selects the best \(N_{POP}\) individuals of \(R_{t}\) based on the ranking and crowding distance, which creates the parent population (\(P_{t}+1\)) for the next generation \(t+1\). The NSGA-II algorithm returns to the tournament selection block and continues the rest of the flowchart until \(t\) is equal to the number of generations \(N_{GEN}\). However, the NSGA-II algorithm can also stop after some iterations when no significant improvement in the population occurs.
# Evaluation of ISO 31010 Techniques for Supply Chain Risk Management in Automotive Suppliers
## 3 Materials and Methods  
The ISO 31010:2009 standard presents 31 risk management tools, of which 14 were classified by ISO itself as applicable or highly applicable to the stages of risk identification, analysis, and assessment. This classification, established by one of the main international standardization bodies, served as a starting point for this research, lending rigor and credibility to the study. Since ISO 31010 adopts a generalist approach, applicable to different types of organizations, and since this research focuses on suppliers to the automotive industry, the investigation was conducted in two stages to identify which of these fourteen tools are the most appropriate for this context. In the first stage, the Delphi method was used with experts from Tier 2 suppliers in order to select, evaluate, and validate the most appropriate tools for risk management in the supply chain of these suppliers.  
The second stage complemented this analysis using the AHP method, allowing the selected tools to be ranked. The use of AHP made the results more robust, enabling the detection and correction of inconsistencies in the experts' responses. While the Delphi was conducted remotely via Google Forms, this phase took place in person with seven experts from a Tier 1 auto parts supplier, which allowed for immediate review of any inconsistencies. In addition, the participation of experts from a Tier 1 supplier broadened the research perspective, ensuring a more comprehensive view of the supply chain.  
The combination of Delphi and AHP methods was essential to structure decision-making in a context with multiple perspectives and uncertainties. Delphi consolidated the specialized knowledge of Tier 2 suppliers, identifying a consensual set of tools, but without establishing a systematic prioritization. To fill this gap, AHP allowed pairwise comparisons and measurement of the consistency of responses, ensuring an additional level of rigor. In this way, the complementarity between the methods ensured not only the capture of specialized knowledge of the automotive supply chain, but also its structured and hierarchical organization, resulting in a robust and replicable classification of the most appropriate tools for risk management in the sector.
# A Forecast Model Applied to Monitor Crops Dynamics Using Vegetation Indices (NDVI)
## 1 Introduction
(2012) applied different statistical forecasting methods to an agroclimatic index (the water requirement satisfaction index: WRSI) and the sugarcane spectral response (normalized difference vegetation index: NDVI) registered on National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA-AVHRR) satellite images [28]. [PERSON] and [PERSON] (2017) tested drought forecast using long-term forecast data and compared it with using just climatological data to obtain drought forecast information in uncalibrated areas. Additionally, they used machine learning techniques of long-term forecast data or climatological data combined with remote sensing data to compare the performance with Kriging spatial interpolation [29]. [PERSON] and [PERSON] (2018) predicted the vegetation dynamics using MODIS NDVI time series data sets, a long short term memory network and an artificial neural network [30]. Most recently, [PERSON] et al. (2020) and [PERSON] et al. (2017) approached the study of NDVI series using multi-scaling analysis [31,32]. The objective of this paper is to offer an algorithm for forecasting vegetation indices (NDVI) time series derived from optical remote sensing data. Specifically, we use NDVI data derived from a Moderate Resolution Imaging Spectroradiometer sensor (MODIS) for different types of crops across seasons during the February 2013--December 2017 period. This main objective can help to resolve the following questions: (1) Does it makes sense to combine forecasting model temporal series to obtain a more accurate prediction model? (2) What weather parameters have an impact on the forecasting NDVI models associated with crop response? (3) Can NDVI temporal series trends be predicted with prediction models? (4) How long in advance can we forecast the crop NDVI associated with the changes in environmental conditions? The main contribution of this paper is to analyze predictions associated with different univariate methods (smoothed and ARIMAX) and their combinations, comparing their behavior. The literature shows that a combination of the predictions made by different procedures improves the precision of the individual predictions. The idea of combining predictions implicitly assumes that it is not possible to identify the underlying process in a series, and that each prediction method is capable of capturing different aspects of the information available for prediction; hence, a combination of predictions made using different techniques is the most accurate approach. The paper is organized as follows: Section 2 describes the estimation and forecast methodologies used and the satellite datasets. The results and discussion are shown in Section 3, and Section 4 describes the main conclusions of this work and further future research.