esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
With advances in technology, pre-concentration techniques for VOCs and methods for their separation and identification, centered around GC-MS, have been improved. In particular, headspace solid-phase micro-extraction (HS-SPME) technology offers significant advantages for collecting and concentrating VOCs from tea leaves. This method allows for the efficient extraction of trace aroma compounds in a highly sensitive and rapid manner, enabling ultra-trace (nanogram-level) quantification when combined with GC-MS [13; 30; 31; 32]. Additionally, gaschromatography-ion mobility spectrometry (GC-IMS), a novel technique for the separation, identification, and quantification of VOCs, has also gained prominence in tea research. Compared to HS-SPME-GC-MS, GC-IMS offers the advantage of direct extraction and analysis of volatiles from tea samples without the need for extensive pretreatment, making it particularly effective in separating isomers and complementing HS-SPME-GC-MS in volatile aroma profiling [24; 33]. In this study, both HS-GC-IMS and HS-SPME-GC-MS were employed to comprehensively analyze the effects of drought stress on the volatile profiles of fresh tea leaves from FD and WNZ cultivars. Key odor-active aroma compounds were identified based on their OAVs, and differences in key aroma markers between the two tea cultivars under drought conditions were assessed using variable importance in projection (VIP) scores in a partial least squares discriminant analysis (PLS-DA) model. Furthermore, KEGG pathway enrichment analysis was performed to investigate the differential metabolic strategies adopted by the two cultivars in response to drought stress. The findings from this study offer valuable insights into how drought stress affects the composition and presentation of key odor-active aroma compounds in fresh tea leaves, particularly via terpenoid biosynthesis, phenylalanine metabolism, fatty acid synthesis, and cutin, suberine, and wax biosynthesis. These results contribute to our understanding of the mechanisms responsible for aroma formation, particularly in cultivar-specific responses to drought stress, while also providing a foundation for optimizing water management in tea cultivation and refining processing techniques to enhance tea quality.
## 5 Experimental Verification
### Modal Parameter Identification  
Modal parameter identification with measured accelerations and reconstruction displacements was performed with the same Hankel matrix in the same period of 20-30 s, and the corresponding results are presented in Figure 17. The range of the state space model order was 1-60, the line number of the Hankel matrix was 600, and the frequency, damping, and modal shape thresholds were 1%, 5%, and 5%, respectively. Moreover, the appropriate sliding window size was 2 s, and the corresponding sliding step was 1 s. Figure 17a displays the stability diagram obtained from modal parameter identification based on measured accelerations in the period of 0-2 s. It is noticeable that the first three modal frequencies were accurately identified from measured accelerations; however, a strong dependency of modal frequencies on the model order was detected. For example, the frequency and damping ratio of the first mode could be determined when the model order reached 30. The stability diagram obtained from modal parameter identification based on reconstructed displacements in the period of 0-2 s is presented in Figure 17b. Therefore, reconstructed displacement-based modal parameter identification could avoid the loss of low-frequency modes of the plate.  
In order to avoid the error caused by random factors, the modal analysis was performed 30 times with different acceleration signals and reconstruction displacements. The mean values of calculated modal frequencies and damping ratios are presented in Table 3. It is evident that the displacement-based modal analysis had higher accuracy as compared to the conventional acceleration-based modal parameter identification method. Meanwhile, to establish the effectiveness of the proposed method, the energy-oriented categorization  
Figure 16: Spectrum diagram of measured accelerations obtained from the sinusoidal sweep experiment.  
Figure 15: (a) Time histories of measured accelerations and reconstructed displacements and (b) spectrum diagrams of measured accelerations and reconstructed displacements.  
of modal components, SSI [1], was used to identify the modal parameters of the structure, and the modal parameters are presented in Table 3.
# Design and Evaluation of Flooding-Based Location Service in Vehicular Ad Hoc Networks+
## 2 Related Work  
Location services for VANETs can be divided into two categories. In the first, protocols use information from the network of vehicles and from the urban infrastructure, such as cellular-network base stations or road-side units (RSUs) [10]. This infrastructure is usually present on the roads. This allows reliable positions to be obtained by reducing the number of signaling messages exchanged between nodes. However, these algorithms are dependent on urban-structure providers and do not allow a spontaneous VANET to emerge. The second category of algorithms does not rely on any urban infrastructure, and thus allows the creation of independent spontaneous networks. Our proposal falls within the second kind of location services.  
The state of the art shows that all proposals for this second kind are based on the choice of the number of vehicles that are used as location servers (usually called rendezvous vehicles) [11]. These elected nodes store and distribute location information. The performance of such algorithms also primarily depends on the choices made to elect those servers. In VANETs, another way to set up a location service consists of sharing location information between all nodes in the network using broadcast messages. The main advantage of this technique is that it eliminates the phase of choosing the servers, this phase often being quite complex. Its main drawback is that flooding may overload the network, which can lead to the rapid degradation of overall performance.  
In this article, we propose a flooding-based mechanism for VANETs where a partial broadcast of location-information packets is performed. The following are the most notable location algorithms found in the literature.
## 4 Experimental Results and Error Analysis  
### Experimental Setup  
In order to validate the proposed method, an image of the temperature distribution of a combustion field was reconstructed. The temperature distribution was generated by a butane burner. As shown in Figure 16, the test system consists of a laser source, a butane burner, a K-type thermocouple (HH-K-24-SLE), a light switch (customized), a sensor and a host computer.  
The laser beam was generated by two distributed feedback (DFB) diodes. Two central wave frequencies of 7185.60 cm\({}^{-1}\) (NTT, NLK1E5 GAAA) and 7444.36 cm\({}^{-1}\) (NTT, NLK1B5 EAAA) were used, which are in the vicinity of the central spectral absorption line of H\({}_{2}\)O molecules. The temperature-current drive of the lasers was realized by external input voltage signals to two laser drivers (Stanford DC-501) [35; 36; 37]. In order to realize the laser wavelength modulation, the voltage signal input to the two laser drivers was generated by a data acquisition device (DAQ, NI, PCI-6115). This voltage signal is a 1 kHz sinusoidal sweep signal superimposed on a 100 kHz sinusoidal modulation signal. The two laser beams were split into two beams by a 2 \(\times\) 2 single-mode fiber coupler. One laser beam is passed through a standard lens with a free spectral range (FSR) of 0.01 cm\({}^{-1}\) for wavelength monitoring, while the other laser beam is guided to an optical multiplexer for scanning control. For one input, the optical multiplexer has 100 output channels, and the scanning interval is 10 ms/per channel. The switching loss is only 0.2 dB per channel. A detailed description of the experiment is given in the literature [  
The 3D model and partial view of the butane torch burner experimental platform are shown in Figure 17a,b, and Figure 17c,d provide photographs of the experimental combustion flame and its focused view. Nine K-type thermocouples were spaced evenly (20 mm in between) over the same cross-section location. The measurements taken from the thermocouples were used as the temperature distribution references to evaluate the reconstruction accuracy of the proposed method. The field optical path arrangement is given in Figure 17e. The laser beam was arranged according to the optimal layout of the optical path for the field measurement. The overall test system can accommodate a multi-angle arrangement for 100 laser beams.
# Deep Class-Guided Hashing for Multi-Label Cross-Modal Retrieval
###### Abstract
Data-to-Data Metric Learning still plays a dominant role, inevitably leading to intra-class dispersion. This limitation, in turn, undermines the preservation of the intra-class similarity structure. Figure 1: Illustration of hash codes learned using different learning strategies. To answer the above questions, we proposed a novel Deep Class-Guided Hashing (DCGH) method for multi-label cross-modal retrieval. We have introduced a variance constraint to ensure that the distances between each data point and its corresponding proxy are as consistent as possible, preventing semantic bias. Regarding the issue of intra-class dispersion, considering that in multi-label datasets, the number of irrelevant pairs in pairwise loss is much smaller than the number of relevant pairs and that positive sample pairs have a stronger constraint and are more likely to cause intra-class dispersion compared to negative sample pairs, we assign different small weights to the positive and negative sample constraints of pairwise loss. This allows for the aggregation of similar data points to be primarily guided by proxy loss, thereby addressing the issue of intra-class dispersion. To sum up, the main contributions of this article are threefold:  
* General advancements: Starting from the perspective of intra-class aggregation and inter-class structural relationship maintenance, this paper proposes a combination of proxy loss and pairwise loss;
* Innovative methodologies: In further considering the issues arising from the combination of proxy loss and pairwise loss, the DCGH method is proposed;
* Comprehensive experiments: Extensive experiments on three benchmark datasets demonstrate that the proposed DCGH algorithm outperforms other baseline methods in cross-modal hashing retrieval. The remainder of this paper is organized as follows. Section 2 introduces representative supervised cross-modal hashing methods and unsupervised cross-modal hashing methods. Section 3 introduces the proposed method in detail. The experimental results and analysis are provided in Section 4, and Section 5 summarizes the conclusions and future work.
# Embedded One-Dimensional Orifice Elements for Slosh Load Calculations in Volume-Of-Fluid CFD
## 2 Materials and Methods
### Orifice Flow Losses
#### 2.1.1 Viscous Losses  
The pressure loss along the length \(L\) of a long orifice due to viscous effects (including the wall friction) is written in a generic form, assuming isothermal flow, as  
\[\Delta p_{visc}=\frac{K_{f}L}{D_{H}}\frac{\beta\tilde{u}^{2}}{2}\,, \tag{3}\]  
where \(K_{f}\) is the dimensionless Darcy-Weisbach Friction Factor, \(L\) is the length of the orifice section and \(D_{H}\) is the hydraulic diameter [37]. The empirical equation by [PERSON] [38] will be used in this work to determine \(K_{f}\) since it is valid over all Reynolds numbers up to \(10^{8}\) and is suitable \"for the entire range of the Moody chart, that is valid for laminar, transitional and fully turbulent regions\" [38]. Furthermore, a smooth surface will be assumed, as is the case in the problems modeled in this work. [PERSON] has a general empirical equation for problems with non-zero surface roughness, which can be implemented and used similarly  
Figure 1: Contracting and expanding flow in a long orifice.  
as the smooth relation used here. The empirical equation by [PERSON] [38] for smooth surfaces is given by  
\[K_{f}=\frac{6.4}{\left(ln\Big{(}\frac{1}{Re}\Big{)}\right)^{2.4}}+\left(\frac{64} {Re}-\frac{6.4}{\left(ln\Big{(}\frac{1}{Re}\Big{)}\right)^{2.4}}\right)e^{- \left(\frac{Re}{260}\right)^{8}}\,. \tag{4}\]
# Overlapping Community Detection of Bipartite Networks Based on a Novel Community Density
## 4 DSNE Algorithm
### Construct Sub-Communities for Each Node Pair  
In this stage, it is important to calculate node pairs' density (Equation (8)). The flow of the first stage is shown in Algorithm 1.  
Figure 6: Bi-EgoNet microscopic network model of node pairs \((U_{2},V_{1})\).  
```
0: A bipartite network \(G(U,V,E)\);
0: Sub-communities set: \(Sub-DC\);
1: Input bipartite network G(U, V, E);
2: To traverse the bipartite network. For each node pair \((u_{i},v_{j})\), \((u_{i},v_{j})\) as the Bi-Ego, get the set \(PN(u_{i})\) of all node pairs including node \(u_{i}\), get the set \(PN(v_{j})\) of all node pairs including node \(v_{j}\);
3: Calculate the set \(NeiPN(u_{i},v_{j})\) of all adjacent node pairs of node pair \((u_{i},v_{j})\);
4: Each node pair \((u_{i},v_{j})\) is numbered, the first node pair is numbered as 1, the second node pair is numbered as 2, and so on, the t node pair is numbered as t, until all node pairs have been numbered;
5: With Bi-Ego \((u_{i},v_{j})\) numbered as t as the center, the initial value of t is 1, the sub-community \(G_{t}(U_{t},V_{t},E_{t})\) is constructed. In the beginning, there is only one node pair \((u_{i},v_{j})\) in the community;
6: Repeat step 5 and increment t by one each time until all Bi-Ego \((u_{i},v_{j})\) as the center in the G(U, V, E) has been traversed. Correspondingly, all sub-communities have been constructed;
7: For communities \(G_{t}(U_{t},V_{t},E_{t})\), Suppose we add a node pair \((m,n)\) from the set \(NeiPN(u_{i},v_{j})\) to this community, calculate the density \(D_{t_{(m,n)}}\) of node pair \((m,n)\) after joining. If \(D_{t_{(m,n)}}>\alpha\), add \((m,n)\) to the community, Otherwise, do not join;
8: Repeat step 7 until all node pairs in the set \(NeiPN(u_{i},v_{j})\) have been traversed;
9: Repeat steps 7 and 8 until all sub-communities in the set \(Sub-DC\) have been traversed;
10:return\(Sub-DC\).
```  
**Algorithm 1** Construct Sub-communities For Node Pair.
## 1 Introduction  
Welding processes are the most selected joining processes in the manufacturing and assembling of many components because of their good degree of reliability and high production velocity [1]. Another advantage that motivates this preference is the economic feasibility of the various types of welding compared with other manufacturing processes [2, 3]. Gas Metal Arc Welding (GMAW) is one of the most applied and preferred techniques in the industry [4] because of its advantages, namely the capability of all-position welding and good quality of welds. This welding method is versatile, as it can be used in semi-automatic and fully automatic modes, in consonance with the requirements of each type of application [5, 6].  
Even though there are plenty of advantages in welding processes, one critical problem is that they can often produce high levels of defects, such as shrinkage and distortions [7]. During the welding process, usually non-uniform expansions and contractions occur among the weld and the surrounding regions, and these effects cause distortions [8]. One factor that contributes to the distortions' appearance is the high temperature of the welded area and its thermal expansion, which is restricted by the surrounding areas where the metal is at a lower temperature, causing compressive stress [9], while high values of tensile stresses are generated in the weld bead [10].  
A lot of research has been conducted to overcome these problems and find strategies to control the appearance of the welded components due to distortions. Distortions often increase production costs and time, especially when their values outpace the accepted limits [11]. In order to achieve a minimal occurrence of the distortion effects and residual stresses, it is necessary to analyze the parameters used in the welding process and also the fabrication conditions [12], particularly the material specifications, level of heat input, stiffener arrangements, joint shapes, welding type and continuity, initial distortions and welding sequences, and heat treatments before and after the welding [13; 14].  
The objective of this work was to investigate the influence of three different welding sequences of the GMAW process on angular distortion in butt-joint welds, performed on S235 steel plates by metrological assessment.
# An Enhanced Message Distribution Mechanism for
## 6 Future Works
### Flexible Mode Switching  
The experiment shown in Section 5.4 shows that our mechanism has poor performance when the subscription rate of a certain topic is too high. In the future, we will consider adding a flexible selection mechanism, which switches to normal mode when the subscription rate of some topics is higher than the threshold for a certain period of time.
## 3 Materials and Methods
### GC\(\times\)GC-ToFMS Analysis  
The volatiles of the _Brassica_ vegetable samples were isolated using headspace solid phase microextraction (HS-SPME) and analyzed using SPME-GC\(\times\)GC-TOFMS for the comparative, semiquantitive (peak area percentage) analysis only. The SPME fiber carboxen/polydimethylsiloxane (DVB/CAR/PDMS) was used for volatile extraction (Supelco, Bellefonte, PA, USA). The vegetables were cut into approximately 0.5 cm pieces with a kitchen knife and 4 g of each was placed in a separate SPME vial. The samples were pre-incubated for 5 min at 60 \({}^{\circ}\)C, then the fiber was exposed for 30 min at the same temperature to adsorb volatiles. The volatile compounds isolated by SPME were desorbed in the injector port on the GC\(\times\)GC-ToF-MS system (Pegasus 4D LECO, St. Joseph, MI, USA). The GC was equipped with a DB-5 column (25 m \(\times\) 0.2 mm \(\times\) 0.33 \(\upmu\)m, Agilent Technologies, Santa Clara, CA, USA) and a Supelcowax 10 (1.2 m \(\times\) 0.1 mm \(\times\) 0.1 \(\upmu\)m, Supelco Bellefonte, PA, USA) as the second column. The injector temperature was set at 250 \({}^{\circ}\)C and injection was performed in a splitless mode. The gas flow was set at 0.8 mL/min. The primary oven temperature was programed as follows: 40 \({}^{\circ}\)C for 1 min, then rising at 6 \({}^{\circ}\)C/min to 200 \({}^{\circ}\)C, where it then was reduced at 25 \({}^{\circ}\)C/min to 235 \({}^{\circ}\)C, where it was held for 5 min. In the secondary oven, the following programming was used: Held at  
Figure 5: Mean values from all Brussels sprout cultivars (Ajax, Marte, Maximus, Neptuno and Profitus) for total peak areas of the main groups in raw non-frozen and frozen-thawed vegetables. The results are the mean of three replicates for each cultivar (i.e., 15 analyses), error bars show standard deviation. Amounts were expressed in total (sum of) peak areas.  
65 \({}^{\circ}\)C for 1 min, then rising at 6 \({}^{\circ}\)C/min to 225 \({}^{\circ}\)C, where it then decreased at 25 \({}^{\circ}\)C/min to 260 \({}^{\circ}\)C, where it was held for 5 min. The transfer line temperature was 260 \({}^{\circ}\)C. The modulation time was 4 s. The time-of-flight mass spectrometer was operating at a mass range of _m/z_ 38-388 and detector voltage \(-\)1700 V at 150 spectra/s. Total analysis time was 34.07 min. All analyses for any particular cultivar and preparation method were done in triplicates.
# Silver Nanowires: Synthesis, Antibacterialativity and Biomedical Applications
## 5 Emerging Biomedical Applications of Silver Nanowires
### Drug Delivery  
In the area of drug delivery, the constant need for improved safety and efficacy of therapies has fueled a lot of research in nanotechnology-based systems, AgNPs have the potential to provide new munitions in the fight against aggressive disease such as tumors. [PERSON] et al. [7] investigated the cytocompatibility of AgNPs in view of their use as theranostic agents. They suggested that since AgNPs are amenable to chemical modification, they could be engineered for targeted delivery; once accumulated at the tumor site they could be exploited as: thermal ablation agents due to their thermal conductivity; tunable contrast agents thanks to their localized surface plasmon modes; and drug carriers by chemical conjugation.
# An Evaluation of Machine Learning Models for Forecasting Short-Term U.S. Treasury Yields
## 2 Literature Review  
Forecasting government bond yields, particularly the U.S. 10-year Treasury yield, has been a long-standing topic of interest in both macroeconomics and financial research. Numerous models and approaches have been proposed, ranging from traditional econometric frameworks to recent applications of machine learning (ML) and artificial intelligence.
## 5 Experimental Analysis
### Example for Evaluating Uncertainty of Perpendicularity Measurement  
According to Formula (15), the uncertainty component caused by indication errors can be calculated as follows in perpendicularity measurement:  
\[u_{\mathrm{E}}=\frac{2\cdot A}{\sqrt{6}}=\frac{2\times 3}{\sqrt{6}}=2.449\ \mathrm{\upmu m} \tag{37}\]  
Similarly, under the condition of repeatability, complete 10 times' continuous and rapid measurement of the measurement task, calculate the standard deviation of the repeatability experiment as \(S_{\mathrm{r}}\) = 0.682 \(\mathrm{\upmu m}\); if the average value of three times' measurements is taken as the best estimate of the perpendicularity measurement, then the standard uncertainty caused by measurement repeatability is as follows:  
\[u_{\mathrm{r}}=\frac{S_{\mathrm{r}}}{\sqrt{3}}=0.394\ \mathrm{\upmu m} \tag{38}\]  
Similarly, complete the reproducibility measurements by three surveyors and try to have the sampling strategy and other factors kept as different as possible, thus obtaining the standard uncertainty caused by the reproducibility of perpendicularity measurement as \(u_{\mathrm{R}}=1.060\ \mathrm{\upmu m}\). See Table 6 for the uncertainty component for perpendicularity measurement:  
\begin{table}
\begin{tabular}{c c c} \hline
**Standard Uncertainty** & **Source of Uncertainty** & **Evaluation Result** \\ \hline \(u_{\mathrm{E}}\) & Indication error & 2.021 \(\mathrm{\upmu m}\) \\ \(u_{\mathrm{r}}\) & Repeatability & 0.357 \(\mathrm{\upmu m}\) \\ \(u_{\mathrm{R}}\) & Reproducibility & 0.915 \(\mathrm{\upmu m}\) \\ \hline \end{tabular}
\end{table}
Table 5: The uncertainty budget for flatness measurement.  
The combined standard uncertainty is as follows for perpendicularity measurement tasks:  
\[u_{\text{c}}=\sqrt{u_{\text{E}}^{2}+u_{\text{r}}^{2}+u_{\text{R}}^{2}}=2.7\ \upmu\text{m} \tag{39}\]  
Similarly, the expanded uncertainty is as follows:  
\[U=k\cdot u_{\text{c}}=5.4\ \upmu\text{m} \tag{40}\]
# Partial Discharge Pattern-Recognition Method Based on Embedded Artificial Intelligence
## 1 Introduction
Finally, the discharge type recognition is accomplished using a CNN in the STM32H743 microcontroller (Makers: STMicroelectronics; Source City: Guangdong, China). Laboratory test results show that the accuracy of the discharge type recognition of this system can reach more than 98%, and the system as a whole is completed on the microcontroller, greatly reducing the cost and power consumption of the system, while simultaneously improving the portability of the system, making it more convenient to identify the pattern of PD.