esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[PERSON] et al. [31] have proposed an automated  
Figure 1: Example of thermo-mechanical simulation. inverse method to calibrate thermal finite element models, which ensure that both the thermal history data and melt pool geometry are predicted with accuracy. According to the author's knowledge, both the analytical method and FE with a 2D heat source can simulate a half-elliptical melt-pool cross-sectional shape. However, in case of deep penetration, the existing heat source in the FEM method must be calibrated with the experimental results for each combination of parameters; therefore, a heat source that does not require calibration with experimental results and is capable of correctly predicting the melt pool's dimension is still lacking. In this article, a new temperature-dependent heat source is developed to simulate deep penetration in SLM. The parameters of the heat source are no longer fixed during the simulation. At the beginning of each time step, the parameter of laser penetration is adjusted according to the pre-defined criteria. Experience shows that the temperature-dependent heat source needs to be calibrated only once for each material. The FE model with the new heat source is employed to simulate the SLM AlSi10 Mg and SLM Inconel 718. The organization of paper is as follows: (i) Section 2 first expounds the FE model using the new proposed heat source; (ii) as illustrative examples, Section 3 shows the comparisons of simulation results and experimental measurements [32; 33]; and (iii) conclusions and perspectives are presented in Section 4.
## 3 Experimental Phenomena and Their Analysis
### Analysis of Experimental Results and Discussion
#### 3.4.3 Stress-Strain Analysis  
The test specimen strain data are shown in Figure 18, where LS represents the bolt strain, Z represents the steel strain on the column, L represents the steel strain on the beam, H represents the concrete strain, and J represents the steel plus haunched plate strain. Analysis of the strain data from both sets of tests showed that the strain variation pattern was almost identical in all parts. The strains in the precast beam reinforcement and concrete strains were small without significant fluctuations before cracking and increased after cracking of the specimens. As the load increased, the strain on the reinforcement and concrete increased, and the strain on the reinforcement at the expanded end of the precast beam was greater than that at the end of the precast beam. Concrete cracking at the expanded end of the precast beam was evident, with a sudden change in the concrete strain at this location. The strain trend of the internal longitudinal reinforcement in the precast beam did not change abruptly, indicating that no plastic deformation of the internal longitudinal reinforcement occurred. The integral strain of the reinforcement on the precast column was small and did not change significantly, indicating that the precast column was subjected to less deformation throughout the low-cycle loading process. The strain trend of the bolt and steel haunched plate is consistent with the strain trend of the reinforcement at the expanded end of the precast beam, which shows that the precast beam expanded end and the bolt were the most stressed parts during the whole loading process, following the design concept of these new joints.  
Figure 17: Stiffness degradation curves.
## 8 Results and Discussion
### Comparative Analysis of DR Scenarios  
In this subsection, we provide a comparative analysis of the four DR scenarios studied, focusing on their impact on energy management, cost-effectiveness, and the use of primary and backup energy sources. The implementation of DR programs significantly influences the household's energy load distribution. In the baseline scenario (No DR), the load varies widely, peaking at 1658 W in the late afternoon. With the introduction of DR programs,  
Figure 17: The LPSP values in scenario 4.  
the peak load is progressively reduced: 1555.50 W in the 20% DR scenario, 1504.24 W in the 30% DR scenario, and 1452.99 W in the 40% DR scenario as shown in Figure 18. These reductions demonstrate the effectiveness of DR strategies in shifting energy consumption to off-peak periods, thereby flattening the load curve and reducing peak demand stress on the energy system. PV and wind energy generation follow similar patterns across all scenarios, dictated by local weather conditions. PV generation starts around 6:00 a.m., peaking at midday with values around 901 W to 910 W. Wind generation is more variable, peaking at approximately 880.1 W in the late afternoon. The consistent generation profiles highlight the reliability of these renewable sources in contributing to the household's energy needs.  
The reliance on backup systems varies across the scenarios. In the No DR scenario, batteries discharge significantly during peak periods, contributing up to 1004.42 W. FCs and EVs provide additional support, with FCs peaking at 988.79 W. As the DR programs are introduced, the discharge rates of these backup systems are optimized, showing reduced peak contributions in the 20%, 30%, and 40% DR scenarios. This optimization underscores the improved efficiency in energy management facilitated by DR strategies. Cumulative operating costs are a critical metric in evaluating the effectiveness of DR programs. The baseline scenario (No DR) incurs the highest cost of $767.067 for the day. With the implementation of DR programs, these costs are reduced to $753.56 in the 20% DR scenario, $749.09 in the 30% DR scenario, and $749.09 in the 40% DR scenario. The cost savings highlight the economic benefits of transferring energy usage to off-peak periods and optimizing the use of RESs and the backup system.  
Across all scenarios, the LPSP values remain within the acceptable range of 0 to 0.05, demonstrating the reliability of the HEMS. The highest LPSP values are observed during peak demand periods but remain low, indicating a robust system capable of meeting energy demands with minimal risk of power shortages. The comparative analysis of the four DR scenarios demonstrates the substantial benefits of implementing DR programs in a residential setting equipped with a hybrid energy system. By reducing peak load demand, optimizing the use of renewable and backup energy sources, and achieving significant cost savings, DR programs enhance the efficiency and sustainability of HEMSs. The findings underscore the potential for DR strategies to contribute to more balanced and reliable energy systems in residential applications.
# Experimental Validation of GNSS Interferometric Radio Occultation
## 4 Data Processing, Analysis and Discussion
### Interferometric RO Processing
#### 4.3.4. Zenith iRO Processing
Consequently, the reference signal in the Zenith iRO would be better matched to the occulting signal than the ideal PRN sequence. This might explain the slightly better performance of the Zenith iRO technique compared to the clean-replica open loop processing. This conclusion should be taken with caution, as only one experimental evidence has been collected. \begin{table}
\begin{tabular}{c c c c} \hline
**SoD** & \textless{}**48,000** & **48,000–50,600** & \textgreater{}**50,600** \\ \hline CR\_OL & 1.31 Hz & 0.87 Hz & 4.61 Hz \\ Zenith iRO & 1.31 Hz & 0.89 Hz & 4.26 Hz \\ \hline \end{tabular}
\end{table} 
Table 2: RMS deviation of the Doppler estimates respect to the predicted Doppler. Figure 13: Comparison between the Doppler observations obtained with the zenith iRO approach and the clean replica open loop processing.
## 6 Experimental Results and Analysis
### Verification of the Experimental Setup  
Research on fluid characteristics under turbulent flow within plain tubes has reached a relatively mature stage. In these experiments, data from a plain tube were examined, and the test results were compared with established theories on fluid characteristics under turbulent flow within a plain tube. This is conducted to verify the reliability of the current outcomes. The current study compares the values of the tested Nusselt numbers and friction coefficients of the plain tube, calculated using the corrected formulae of [PERSON], [PERSON], and [PERSON] [30].  
For Reynolds numbers (_Re_) greater than 10,000 and Prandtl numbers (_Pr_) between 0.6 and 160, the [PERSON] correlation is as follows:  
\[Nu=0.023 Re^{0.8}Pr^{0.4} \tag{14}\]  
When the Reynolds numbers range from 3000 to 5 \(\times\) 10\({}^{6}\) and Prandtl numbers are between 0.5 and 2000, [PERSON]'s correlation is as follows:  
\[Nu=\frac{(f/8)(Re-1000)Pr}{1+12.7\sqrt{f/8}\left(Pr^{0.66}-1\right)} \tag{15}\]  
where \(f\) denotes the Darcy friction factor.  
For Reynolds numbers ranging from 3000 \(<Re\leq\) 2 \(\times\) 10\({}^{4}\), turbulent flow in tubes is described according to the Blasius equation as follows:  
\[f=0.316/Re^{0.25} \tag{16}\]  
Figure 9 compares data from a plain tube with the theoretical calculations for \(Nu\) and \(f\). From this figure, as _Re_ increases, the trends of both predicted and experimental values are  
Figure 8: Effect of V-rib attack angle (\(\beta\)) and tape arrangement on the local Nusselt number at _Re_ = 10,000. (**a**) Typical twisted tape (TT). (**b**) Forward arrangement, CVRT, \(\beta\) = 45\({}^{\circ}\). (**c**) Backward arrangement, CVRT, \(\beta\) = 45\({}^{\circ}\). (**d**) Backward arrangement, CVRT, \(\beta\) = 15\({}^{\circ}\).  
identical, and their values are quite close. Using the Dittus-Boelter equation, the maximum relative error is 7.98% at \(Re\) = 8000 and 0.68% lower at \(Re\) = 12,000. The maximum relative error for the friction factor is 9.64%. All errors are within 10%, which indicates that the experimental apparatus is dependable and the results are accurate.
# Design and Implementation of a Smart Traffic Signal Control System for Smart City Applications
## 5 System Implementation and Experiment Results
### STSC System Implementation  
OBU is implemented by an embedded computer (Raspberry Pi 3), which connects 802.11p, GPS modules, and a small LCD screen for display messages, as shown in Figure 14a,e. The 802.11p communication module (IWCU V4.2) developed by the Industrial Technology Research Institute (ITRI), as shown in Figure 14b, is used for V2X communication. The hardware interface of the module includes two PCIe gigabit ethernet interfaces, one RS232 port, and two USB2.0 ports. It is a Linux-based  
Figure 13: Traffic signal switching and compensation mode.  
operating system shipped with a software development toolkit and a cross compiler. In the STSC system, we adopt the 802.11p module in both the RSU controller and the OBU equipped in the EV.  
A small industrial computer is adopted as the RSU controller (SEM\(-\)6338), as shown in Figure 14c, where the main components in it includes embedded SOC (AMD\({}^{\circledotimes}\) G-Series), 8G DDR3 ram, 2 RS232 ports and 1 RS232/422/485 Port, an ethernet interface and four USB ports. Through a variety of hardware interfaces, the RSU controller can effectively access the data of external peripheral modules. The operation system of the RSU controller is Linux Ubuntu version 18.04. Several peripheral devices are connected to the RSU controller including 802.11p module, traffic signal controller (as shown in Figure 14d), CMS module, 4G module, and Bluetooth/Wi-Fi module.  
In order to quickly and cost-efficiently deploy the proposed STSC system, one key successful factor is that it should be compatible with legacy traffic signal controllers. The RSU controller can connect to a legacy traffic signal controller as shown in Figure 14d and is designed following the Metropolitan Traffic Control Protocol V3.0 national standard defined by the Ministry of Transportation and Communication (MOTC). In this work, the signal controller manufacturer modified the firmware to support the special designed signal scheme so that the RSU controller can issue more control commands to the signal controller, such as green and blinking yellow. By this approach, the proposed STSC can be compatible with the legacy traffic signal controller and it can be quickly deployed for multi-modal smart city ITS applications.
## 2 System Structure  
In general, the HPS can be classified into two structures: active and passive. Their main difference lies in whether one or more DC/DC converters are employed.
## 2 Results
### Volatiles Emitted In-Vivo from Four Cold-Hardy Grape Cultivars
#### 2.2.2 Emissions from Single Berries  
Thirty-nine VOCs emitted in-vivo were also detected by modified glass vial (vacuum assisted) method in Frontenac, Marquette, St. Croix, and La Crescent cultivars grown in Iowa and South Dakota. Table 2 presents the VOCs that are characteristic of these 4 cold-hardy cultivars during the 2013 growing season in Iowa and South Dakota, detected in-vivo from single berries, and determined through multivariate statistical analysis previously discussed.  
In-vivo detection of VOCs by modified glass vial did not identify a key representative compound in 2013 South Dakota Frontenac grapes at veraison (Figure 5). At harvest, palmitic acid was emitted and detected in these berries.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**Cluster**} & \multirow{2}{*}{**No. of** **Members**2 **} & \multirow{2}{*}{**Most Representative Variable**3 **} & \multirow{2}{*}{**Cluster**} & \multirow{2}{*}{**Total**} \\  & & & & **Proportion of** **Variation** \\  & & & **Explained**4 ** & **Explained**5 ** \\ \hline SD Fronttenac & 1 & 2 & Palmitic acid & 0.830 & 0.415 \\ (0.750) 1  & 2 & 2 & Acetic acid & 0.669 & 0.334 \\ \hline SD Marquette & 1 & 7 & 1,4-Butanolide & 0.878 & 0.473 \\ (0.870) 1  & 2 & 4 & Ethyl octanoate & 0.883 & 0.272 \\  & 3 & 2 & 2-Ethyl-1-hexanol & 0.819 & 0.126 \\ \hline IA St. Croix & 1 & 4 & 3-Methyl indole & 1.000 & 0.500 \\ (0.896) 1  & 2 & 3 & Acetic acid & 0.722 & 0.271 \\  & 3 & 1 & Benzyl alcohol & 1.000 & 0.125 \\ \hline  & 1 & 3 & Nonanal & 0.802 & 0.241 \\ SD St. Croix & 3 & 3 & Diacetone alcohol & 0.713 & 0.214 \\ (0.855) 1  & 2 & 2 & 1,4-Butanolide & 1.000 & 0.200 \\  & 4 & 1 & 5-(Hydroxymethyl)-2-furancarboxaldehyde & 1.000 & 0.100 \\  & 5 & 1 & Ethyl acetate & 1.000 & 0.100 \\ \hline  & 1 & 28 & 2-Ethyl-1-hexanol & 0.973 & 0.757 \\ IA La Crescent & 2 & 4 & 3-Methyl indole & 0.772 & 0.086 \\ (0.909) 1  & 4 & 2 & Ethanol & 0.636 & 0.035 \\  & 3 & 2 & 2-Phenylethanol & 0.556 & 0.031 \\ \hline  & 1 & 3 & 2-Phenylethanol & 1.000 & 0.300 \\ SD La Crescent & 3 & 3 & Diacetone alcohol & 0.853 & 0.256 \\ (0.936) 1  & 4 & 2 & Acetic acid & 0.976 & 0.195 \\  & 2 & 2 & 6-Methyl-5-hepten-2-one & 0.926 & 0.185 \\ \hline \hline \end{tabular}
\end{table}
Table 2: ‘Characteristic’ VOCs emitted from single berries of Frontenac, Marquette, St. Croix, and La Crescent grapes grown in Iowa and South Dakota. These VOCs were indicated to be the most representative variable from hierarchical clustering analysis after the PCA (JMP Pro 12.0.1, SAS Institute Inc., Cary, NC, USA).  
Marquette  
VOCs detected by modified glass vial emitted from 2013 Marquette grown in South Dakota generally did not vary during berry development. The variability decreased between the replicate samples, indicated by less spread between the data points as the berries developed. Aromas from berries grown in South Dakota during the 2013 growing season (Figure 6) can be characterized from 3 VOCs. These compounds were 2-ethyl-1-hexanol, ethyl octanoate and 1,4-butanolite.
## 4 AHP/TOPSIS-Based Multi-Criteria Decision-Making
### The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)  
One of the multi-criteria decision-making (MCDM) methods is the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), which was created in 1981 by [PERSON] et al. [10]. It is a compensatory aggregation method based on the idea that the ideal candidate or alternative must have the smallest geometric distance to a PIS (positive ideal solution) and the geometric farthest distance from an NIS (negative ideal solution) [36]. In other words, the benefit is maximized and cost is minimized by PIS; on the other hand, the benefit is minimized and cost is maximized by NIS. It is assumed that, for each criteria, maximization or minization is applied. TOPSIS requires normalization as the parameters or criteria are often of incongruuous dimensions in multi-criteria problems [10; 37]. Ranking a number of feasible alternatives based on the closeness to the ideal solution is best achieved via the TOPSIS approach. TOPSIS also avoids pairwise comparisons, thus allowing it to be computed in a simple and efficient manner [25]. The TOPSIS method implementation can be summarized with the following steps [38]:  
1. Establish a decision matrix;
2. Calculate a normalized decision matrix;
3. Determine the weighted decision matrix;
4. Identify the positive and negative ideal solutions;
5. Calculate the separation distance of each competitive alternative from the ideal and non-ideal solutions;
6. Measure the relative closeness of each location to the ideal solution; and Rank the preference order.
## 2 The Applied Methodology
### The Froelich Equation Review  
To facilitate the analysis approach, the following simplifying assumptions are adopted.  
Figure 4: General model of the overhead line magnetic energy harvester with an active rectifier (OLMEH-AR) with a constant voltage load (**a**); its equivalent short circuit States 1, 3, and 4 (**b**) and the equivalent charging State 2 (**c**).  
Figure 3: Semiconductor conduction states during State 1 (**a**); State 2 (**b**); State 3 (**c**); and State 4 (**d**).  
Firstly, ideal (lossless) rectifiers and ideal switches are assumed with no voltage drop and zero ON resistance. Secondly, the core is assumed to be un-hysteretic (lossless), whose nonlinearity can be described by the Froelich equation:  
\[B(H)=\frac{aH}{b+H}, \tag{1}\]  
Here, the parameter \(a\) is the saturation flux density at infinite field intensity:  
\[B_{sat}=\lim_{H\rightarrow\infty}\frac{aH}{b+H}=a, \tag{2}\]  
whereas \(b\) is related to the magnetic permeability of the core at the low field intensity (linear) region  
\[\mu(H\ll b)=\frac{a}{b} \tag{3}\]
## 4 Test and Results  
This section presents the results found by the HEGA to solve the static and multistage TNEP problem. The Garver, IEEE 24-bus, and South Brazilian 46-bus were used to show the applicability of the proposed approach. The data of these systems can be found in [15] or via authors. Four tests were performed for each electrical network for both static and multistage TNEP. Test 1 corresponds to the traditional planning (static or multistage), Test 2 corresponds to the planning considering SCC devices, Test 3 corresponds to the planning considering active power losses, and Test 4 corresponds to the planning considering both SCC devices and active power losses.The number of piecewise linear blocks is Y = 5, 7 and 10 for the Garver system, IEEE-24 bus test system and the South Brazilian 46-bus system, respectively. For all simulations, the penalty factor \(\alpha\) is equal to 1 US5 MW and the maximum voltage angle \(\theta_{ij}^{max}\) is equal to \(\pi/4\)[44]. To solve the TNEP problem considering active power losses, the number of linear piecewise blocks is Y = 5.  
Table 1 presents the types of SCC devices implemented in the tests. Note that the compensation rate and installation cost of each type of SCC are indicated in columns 2 and 3, respectively. Furthermore, the compensation costs for each case were assumed as a percentage of the investment cost of the transmission line. Note that the higher the cost of the capacitor bank, the higher its compensation.  
Figure 6: Flowchart of the HEGA.  
All of the algorithms proposed in this paper are implemented in the C++ programming language. The simulations are executed using a personal computer with an Intel(r) Core (TM) i7-8850H processor (2.6 GHz) with 16 GB of RAM.
## 3 Results and Discussion  
### Bio-Analytical Method Validation of EPA and DHA by HPLC-UV  
The bio-analytical method for EPA and DHA from digesta and cell medium was validated in order to optimize the analysis, which is revealed in Table 1. The target compounds separated well, eluting at 7.59 min and 8.98 min for EPA and DHA, respectively, using HPLC-UV. Each standard solution of EPA and DHA was within the range of 1 to 50 \(\mu\)g/mL,which demonstrated a correlation coefficient (r\({}^{2}\)) that exceeded 0.9966 for EPA and 0.9981 for DHA. The accuracy (%) of the bio-analytical method was in the range of 85.80-101.83 for EPA and 91.78-100.31 for IQC. The precision (% RSD) ranged from 0.90-1.10 for EPA and 0.03-0.50 for DHA. The LOD and LOQ were determined to be 0.182 and 0.553 \(\upmu\)g/mL for EPA, and 0.035 and 0.107 \(\upmu\)g/mL for DHA, as shown in Table 1. These results confirmed that the bio-analytical method of EPA and DHA was well validated by the HPLC-UV.
# Contrastive Enhanced Learning for Multi-Label Text Classification
## 3 Materials and Methods
### Contrastive Enhanced Label Representation Learning
#### 3.2.2 Contrastive Label Correlation Enhancement  
In multi-label text classification (MLTC), each document is typically associated with multiple labels. To effectively capture the relationships between these labels, we treat the labels corresponding to a document as positive samples and those that are not associated with it as negative samples. The objective is to create embeddings where positive samples (labels that are actually assigned to a document) are closely aligned in the embedding space, while embeddings for positive and negative samples (labels not assigned to the document) are well separated. This contrastive approach is crucial for enhancing the correlation information between labels and for improving the overall accuracy of label predictions.  
Given a document, \(X_{i}\), and its corresponding label set, \(Y_{i}=\left\{y_{1,i},\ldots,y_{k,i},\ldots,y_{L,i}\right\}\), we define the set of positive sample embeddings as \(A_{i}=\left\{Y_{m,i}^{*}\right\}y_{m,i}=1,m\in\left\{1,2,\ldots,L\right\} \right\},\) which represents the embeddings of the labels actually associated with \(X_{i}\). The set of negative sample embeddings is defined as \(I_{i}=\left\{Y_{n,i}^{*}\right\}y_{n,i}=0,n\in\left\{1,2,\ldots,L\right\}\), representing the negative sample embeddings. For a given sample, \(i\), our contrastive loss can be defined as follows:\[L_{con,i}=\sum_{z\in A_{i}}\sum_{q\in A_{i}\backslash z}-log\frac{e^{(z \cdot\frac{q}{\gamma})}}{\sum\limits_{z^{*}\in I_{i}}e^{(z\cdot\frac{z^{*}}{ \gamma})}}, \tag{1}\]  
where \(\gamma\in\mathbb{R}^{+}\) is the temperature factor, and the symbol \(\cdot\) denotes a similarity metric. In I this context, we use cosine similarity to construct the label vector space. The numerator indicates the similarity between positive sample pairs, while the denominator represents the contrast between positive and negative sample pairs. By applying this contrastive loss, the model learns to pull together the embeddings of labels that frequently co-occur with the same documents, while pushing apart those that are less related. This process enables the model to capture the intrinsic structure and relationships between labels, thereby constructing a more effective and informative label vector space.  
Furthermore, this enhanced label vector space facilitates better label predictions by providing a richer representation of the label semantics. The effectiveness of this metric will be demonstrated in our subsequent experiments, where we compare different similarity metrics and assess their impact on the quality of the label embeddings and the overall classification performance.