esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Thus far, 31 volatile sulfur compounds have been identified in onions, such as dimethyl disulfide, methylpropyl disulfide, allyl methyl disulfide, methylpropenyl disulfide, isopropyl disulfide, dipropyl disulfide, allyl disulfide, 3,5-diethyl-1,2,4-trithiopentane, 4,6-diethyl-1,2,3,5-tetrathione, 2,4-dimethylthiophene, and 5,7-diethyl-1,2,3,4,6-pentathione. However, the changes that occur in the sulfur compounds in fresh onions under different storage conditions are not clear. Aliium plants have complex matrix systems and low contents of flavor components, and the flavor components can be easily lost or denatured in the process of extraction and separation. Common extraction and separation methods include steam distillation, simultaneous distillation extraction (SDE), solid-phase microextraction (SPME), solvent-assisted flavor evaporation (SAFE), and supercritical fluid extraction (SFE). Gas chromatography-mass spectrometry (GC-MS), gas chromatography-olfactometry (GC-O), and comprehensive two-dimensional gas chromatography time of flight mass spectrometry (GC \(\times\) GC-TOFHS) are the main methods used to identify volatile compounds [8]. Among these, SPME combined with GC-MS is the technique that is most frequently used to test volatile compounds qualitatively and quantitatively in foods because of its easy operation and results in no damage to the sample. More recently, gas chromatography-ion mobility spectrometry (GC-IMS) has been increasingly used for aroma characterization due to its ability to distinguish the differences effectively and intuitively in aroma among different food products. Combining the excellent separation capacity of GC with the high sensitivity and fast response of IMS, GC-IMS improves the accuracy of qualitative analysis. This technology separates the ions under atmospheric pressure, using the ion mobility of detected substances in an electric field [20]. IMS is used in many different fields, such as environmental analysis [21],medical diagnosis [22], pharmaceutical analysis [23], food safety [24], etc. The combination of GC-MS and GC-IMS, utilizing the advantages of the two methods, can provide more information on onion aroma under different storage conditions comprehensively, reliably, and scientifically. As a result, the aims of this study were to: (1) determine the volatile profiles and comparatively analyze the differences in onions with different fresh-cut styles by HS-GC-IMS and (2) characterize the sulfur compounds composition in onion samples with different fresh-cut styles and different storage temperatures by SPME-GC-MS.
## 3 Results
### HS-GC-IMS Analysis of Smoke-Flavored Sea Bass in Different Production Steps  
#### 3.2.1 Identification of Volatile Compounds  
HS-GC-IMS is a non-targeted analytical method for the identification of volatile compounds in a sample by providing retention time and drift time of the compounds. Figure 4 shows the 2D topographic subtraction plots of volatile compounds in smoke-flavored sea bass in different production steps. It was obtained by subtracting fresh fish stage from other samples. Each point represents a volatile compound, and the color and area of the point represent the size of the substance content. The redder the area is, the higher the content of volatile compounds, and the opposite is true for blue [40]. Figure 4 directly shows the migration time of each sample is about 13 ms, and the retention time of various volatile compounds is between 100 and 500 s. There were significant differences in volatile compounds at different stages. The content of some substances decreased in the salted stage, while the content of volatile substances increased significantly in the dried, smoked and heated samples, but there were multiple blue areas in the heated sample.  
Figure 3: PLS-DA and the important flavors (VIP \(\geq\) 1.0) for smoke-flavored sea bass at different production steps. (**a**) PLS-DA scores scatter plot. (**b**) Important volatiles (VIP \(\geq\) 1.0) identified by PLS-DA. The colored boxes on the right indicate the relative concentrations of the corresponding volatiles at different stages.  
The smoke-flavored sea bass has a total of 26 volatile compounds that have been tentatively identified (Table S2). Of note, the instrument detected seven volatile compounds (1-propanol, 2-propanol, 3-methylbutanol, 3-methylbutanal, hexanal, furfural and ethyl acetate) in both monomer and dimer forms [41]. Among the 26 volatile compounds tentatively identified, the carbon chains were generally concentrated in the range of C4-C9, which mainly included aldehydes, alcohols and ketones. There were 13 species of aldehydes, with the largest number, which was followed by alcohols (8) and ketones (5).
# A Review in Fault Diagnosis and Health Assessment for Railway Traction Drives
## 4 Fault Diagnosis in Railway Traction Drives  
The railway traction drive is a key point for the Railway system availability. Railway traction drives are fed with DC supply provided by an external substation, as it is shown in Figure 9, or with AC supply, including a transformer and a rectifier onboard, as it is shown in Figure 10. In both cases, the main parts of a Railway Traction drive are the electric machine, the power electronics, the traction control unit (TCU) and the sensors. Although there are railway traction drives based on other technologies, such as DC motors and thyristors, nowadays, the most usual configuration is based on induction motors (IM) and Insulated Gate Bipolar Transistors (IGBT). Thus, this configuration will be proposed as example in this article.  
In power electronics, power converters, power switches and passive components, as the DC-link capacitor, are included. The power converters in a DC catenary supplied Railway traction drive are an inverter to supply the motor and a braking chopper, whereas a rectifier is needed to be added, in case of AC catenary supply. The inverter transfers power in DC-link to the electric machine, normally an induction motor, during traction or returns to DC-link from motor during braking operation. Furthermore, by means of a chopper, energy is dissipated in the braking resistor in case that the energy cannot be returned to catenary. In case of AC catenary, a controlled rectifier for AC/DC conversion is used.  
The electric machine is connected to a gearbox, which is connected to the bogie axle to move the train [19]. One inverter can supply one or more electric motors in parallel.  
The TCU is the responsible for the execution of the switching modulation for power converters, as well as main and pre-charge contacts and protections management. Sensors measurements are monitored by the TCU for control strategy and fault diagnosis algorithms.  
From 2005, several research works related to fault diagnosis in Railway traction drives have been published. In this review, fault diagnosis research works have been classified in three groups, as a  
Figure 10: Structure of a Railway traction drive connected to AC catenary.  
Figure 9: Structure of a Railway traction drive connected to DC catenary.  
function of the item where the approach is applied. Thus, these three groups are electric machines, sensors and power electronics separately.  
Although there are interesting articles and reviews in fault diagnosis for traction drives [28; 30], the applications works for Railway traction drives are more limited. Thus, in the following subsections, the fault diagnosis research in Railway traction drives is presented, analysing the fault diagnosis in electric machines, sensors and power electronics. Furthermore, the fault diagnosis approaches implemented in Railway traction drives are summarized in Table 2.
# Dynamic UAV Inspection Boosted by Vehicle Collaboration Under Harsh Conditions in the IoT Realm
## 5 Simulation
### UAV Energy Consumption  
Figure 14 illustrates the variation in the remaining battery percentage of three UAVs over different time intervals. The x-axis represents time (in minutes), ranging from 0 to 300 min, while the y-axis represents the remaining battery percentage, ranging from 0% to 100%. Each curve, shown in a different color, represents the battery consumption of a single UAV during its flight. From the graph, it is evident that the battery levels of the three UAVs exhibited significant periodic fluctuations, with rapid declines in battery charge followed by gradual recoveries over time.  
In practical applications, UAV battery consumption is influenced by various factors, including the complexity of the flight mission, flight speed, payload requirements, and external weather conditions. To ensure the continuity and reliability of UAV operations during long-duration missions and prevent interruptions due to battery depletion, the central control station plays a crucial role in monitoring the UAV's battery levels and flight status. By continuously tracking the remaining battery charge, along with the UAV's current location and task demands, the control station can effectively schedule subsequent data collection tasks. With precise predictions of battery consumption, the system ensures that the UAV returns to the base station for battery replacement before it is depleted, thus mitigating potential flight safety risks from over-discharge.
# Comparison of Gait Smoothness Metrics in Healthy Elderly and Young People
###### Abstract  
The goal of this study is to compare gait smoothness using different metrics. Methodologically, harmonic ratio (HR), spectral arc length (SPARC) and log dimensionless jerk (LDL) metrics were applied to acceleration and angular velocity data collected during gait using a wearable inertial sensor placed on the trunk. Sixteen healthy elderly (\(68.3\pm 4.4\) years) and sixteen young (\(22.6\pm 1.1\) years) participants volunteered for the analysis. They were requested to walk at self-selected normal and fast velocities. The results obtained with the three metrics showed variations in smoothness within and between groups. Notably, the older group exhibited contradictory smoothness trends compared to prior studies, presenting a smoother gait than the younger cohort, except for specific directional accelerations. Contradictory outcomes arose between metrics, challenging the influence of age on gait smoothness. Despite concerns about LDL's susceptibility to disturbances, it emerged as the most robust metric. Conversely, HR exhibited alignment with the existing literature on specific acceleration directions. The differences observed in gait smoothness between the two age groups while walking at different speeds suggest that pace might affect smoothness evaluation. The study posits that aging may have a less pronounced impact on gait smoothness compared to cognitive impairment, implying potential clinical utility in discerning age-related gait changes. These findings highlighted the importance of a comprehensive approach to estimating gait smoothness, integrating different metrics and considering several walking speeds, crucial for understanding age-related gait alterations and their clinical implications.  
walling; gait; IMU; smoothness; SPARC; HR; LDLJ
## 3 Validation of the Computational Model
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Mesh**} & **Base Size** & **Basic Refinement** & **Glottis Refinement** & \multirow{2}{*}{**Average No. of Cells**} & \multirow{2}{*}{**\(\Delta\)t**} \\  & **(mm)** & **(mm)** & **(mm)** & & **(s)** \\ \hline MB & 0.5 & 0.25 & 0.0625 & 2.4 M & 1.00 \(\times 10^{-6}\) \\ M1 & 0.56 & 0.28 & 0.070 & 1.8 M & 1.12 \(\times 10^{-6}\) \\ M2 & 0.64 & 0.32 & 0.080 & 1.3 M & 1.28 \(\times 10^{-6}\) \\ M3 & 0.68 & 0.34 & 0.085 & 1.1 M & 1.36 \(\times 10^{-6}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of the meshes used for the grid resolution reduction study and their properties. The basic and glottis refinement areas are represented in Figure 4.  
Figure 5: Pressure evolution for a cycle of oscillation at the pressure probes P1–P5 (parts (**a**–**e**)) in the supraglottal region introduced in Figure 1 for the experimental larynx model and the numerical simulation cases C1–C5 introduced in Table 1. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\mathbf{Err_{rel}^{1}}\) & **P1** & **P2** & **P3** & **P4** & **P5** \\ \hline C1 & 0.76 & 0.71 & 0.78 & 0.93 & 0.99 \\ C2 & 0.69 & 0.72 & 0.77 & 0.91 & 0.95 \\ C3 & 0.50 & 0.46 & 0.53 & 0.28 & 0.22 \\ C4 & 0.22 & 0.20 & 0.33 & 0.28 & 0.22 \\ C5 & 0.47 & 0.44 & 0.49 & 0.60 & 0.60 \\ \hline \end{tabular}
\end{table} 
Table 3: L\({}^{2}\) error norms of the pressures shown in Figure 5 relative to those obtained in the experiment. C1–C5 are the simulation cases introduced in Table 1.  
Figure 6: Planar velocity contours with vectors in the mid-coronal plane of the PIV measurement and case C4 at the middle of the oscillation cycle. Figure 7: Time evolution of the magnitude of the velocity along the mid-axis of the supraglottal channel for an oscillation cycle. The velocity evolution of the computational model is related to one instantaneous cycle but the experimental velocity evolution is phase-averaged. The averaged velocity evolution of the computational model, smoothed by a low-pass filter (cutoff frequency = 5000 Hz), is added to the plots for the sake of better comparison.
###### Abstract  
With the help of community participants, smartphones can become useful wireless sensor network (WSN) components, form a self-governing structural health monitoring (SHM) system, and merge structural mechanics with participatory sensing and server computing. This paper presents a methodology and framework of such a cyber-physical system (CPS) that generates a bridge finite element model (FEM) integrated with vibration measurements from smartphone WSNs and centralized/distributed computational facilities, then assesses structural reliability based on updated FEMs. Structural vibration data obtained from smartphones are processed on a server to identify modal frequencies of an existing bridge. Without design drawings and supportive documentation but field measurements and observations, FEM of the bridge is drafted with uncertainties in the structural mass, stiffness, and boundary conditions (BCs). Then, 2700 FEMs are autonomously generated, and the baseline FEM is updated by minimizing the error between the crowdsourcing-based modal identification results and the FEM analysis. Furthermore, using 151 strong ground motion records from databases, the bridge response time history simulations are conducted to obtain displacement demand distribution. Finally, based on reference performance criteria, structural reliability of the bridge is estimated. Integrating the cyber (FEM analysis) and the physical (the bridge structure and measured vibration characteristics) worlds, this crowdsourcing-based CPS can provide a powerful tool for supporting rapid, remote, autonomous, and objective infrastructure-related decision-making. This study presents a new example of the emerging fourth industrial revolution from structural engineering and SHM perspective.  
structural reliability estimation; structural health monitoring; modal identification; finite element model updating; cyber-physical systems; crowdsourcing +
Footnote †: journal: Applied Soft. 2019, 9, 2840; doi:10.3390/app9142840
## 3 Results
### PCA of Bioclimatic Variables and Seed Characteristics  
To elucidate the role of other factors controlling genome plasticity of turnip landraces, PCA was conducted to group landraces on the basis of the complete data set--for instance GS information plus 28 predictor variables. The approach to cluster landraces through PCA enables researchers to illustrate the interrelationship of variables as vectors of defined magnitude and direction in the climatic space created by principal components. The first principal component (PC1) explained 60.62% of total variance and the second component (PC2) explained 14.33% of the variance exhibiting robust distribution of landraces (Figure 5). Together, these two principal components grouped particular subset of landraces. For example, landraces belonging to the Hengduanshan environment formed cluster I, landraces of the Himalayas formed cluster II, and landraces from the QTP formed cluster III. Moreover the relationship of 29 variables in our case was reduced to two components as clusters helping us to comment on landrace usage [(20)]. For instance, it was possible to infer that variables related to temperature and precipitation (BIO1, BIO3, BIO6, BIO8, BIO9, BIO10, BIO11, BIO13, BIO16, BIO18, and BIO19) have a correlated effect on the groupings of landraces due to similar direction of their associated vectors. It was surprising to observe that 19 out of 28 vectors were directed towards landraces from cluster I, i.e., landraces from the Hengduanshan region which signify the highest landrace usage compared to other environments. Five vectors were pointed towards cluster II (Himalaya environments) and cluster II (the QTP), showing the least landrace usage from the QTP and the Himalayas (Figure 6).  
Figure 5: Correlation of genome size with environmental variables showing genome size is unaffected by environmental variables apart from latitude and altitude. The scatter plot shows correlations of (**a**) genome size with latitude, (**b**) genome size with longitude, (**c**) genome size with altitude, (**d**) genome size with annual mean temperature, (**e**) genome size with annual precipitation, and (**f**) genome size with precipitation of warmest quarter. The filled circles represent landraces from Hengduanshan, filled triangles represent landraces from Qinghai Tibetan Plateau, and the filled dumbbell shapes represent landraces from the Himalayas.
## 7 Conclusions
\begin{array}{l}\left(\frac{\partial j_{\beta}(t)}{\partial\beta}\right)_{ \beta=0}=\frac{\pi}{2}\;Y_{0}(t)\\ \left(\frac{\partial j_{\beta}(t)}{\partial\beta}\right)_{\beta=0}=-K_{0}(t) \end{array}  \tag{13}\]  
Therefore,  
\[ \begin{array}{l}\left(\frac{\partial\;W_{1,\beta+1}(-\frac{t^{2}}{4})}{ \partial\beta}\right)\bigg{|}_{\beta=0}=-\ln\big{(}\frac{t}{2}\big{)}J_{0}(t) +\frac{\pi}{2}Y_{0}(t)\\ \left(\frac{\partial\;W_{1,\beta+1}(\frac{t^{2}}{4})}{\partial\beta}\right) \bigg{|}_{\beta=0}=-\ln\big{(}\frac{t}{2}\big{)}I_{0}(t)-K_{0}(t)\end{array}  \tag{14}\]  
For \(\beta=1\) we have  
\[ \begin{array}{l}\left(\frac{\partial j_{\beta}(t)}{\partial\beta}\right)_{ \beta=1}=\frac{I_{0}(t)}{t}+\frac{\pi}{2}\;Y_{1}(t)\\ \left(\frac{\partial j_{\beta}(t)}{\partial\beta}\right)_{\beta=1}=K_{1}(t)- \frac{I_{0}(t)}{t}\end{array}  \tag{15}\]  
which gives  
\[ \begin{array}{l}\left(\frac{\partial\;W_{1,\beta+1}(-\frac{t^{2}}{4})}{ \partial\beta}\right)\bigg{|}_{\beta=1}=\left(\frac{2}{7}\right)\left[-\ln \big{(}\frac{t}{2}\big{)}I_{1}(t)-\frac{I_{0}(t)}{t}+\frac{\pi}{2}Y_{1}(t) \right]\\ \left(\frac{\partial\;W_{1,\beta+1}(\frac{t^{2}}{4})}{\partial\beta}\right) \bigg{|}_{\beta=1}=\left(\frac{2}{7}\right)\left[-\ln\big{(}\frac{t}{2}\big{)} I_{1}(t)+K_{1}(t)-\frac{I_{0}(t)}{t}\right]\end{array}  \tag{16}\]  
Derivatives for \(beta=1/2\) are  
\[ \begin{array}{l}\left(\frac{\partial j_{\beta}(t)}{\partial\beta}\right)_{ \beta=1/2}=\sqrt{\frac{2}{\pi t}}\left[\sin t\,Ci(2t)-\cos t\,Si(2t)\right]\\ \left(\frac{\partial j_{\beta}(t)}{\partial\beta}\right)_{\beta=1/2}=\sqrt{ \frac{2}{\pi t}}\left[e^{t}E(-2t)-e^{-t}Ei(2t)\right]\\ I_{1/2}(t)=\sqrt{\frac{2}{\pi t}}\,\sin t\quad;\quad I_{1/2}(t)=\sqrt{\frac{2}{ \pi t}}\,\sinh t\end{array}  \tag{17}\]which leads to  
\[ \begin{split}&\left(\frac{\partial\,W_{1,\beta+1}(-\frac{2}{\pi})}{ \partial\beta}\right)\bigg{|}_{\beta=1/2}=\frac{2}{\sqrt{\pi}\,i}\big{[}-\ln \big{(}\frac{t}{2}\big{)}\sin t+\sin t\,Ci(2t)-\cos t\,Si(2t)\big{]}\\ &\left(\frac{\partial\,W_{1,\beta+1}(\frac{2}{\pi})}{\partial \beta}\right)\bigg{|}_{\beta=1/2}=\frac{2}{\sqrt{\pi}\,i}\big{[}-\ln\big{(} \frac{t}{2}\big{)}\sinh t+\frac{1}{2}\big{(}e^{t}Ei(-2t)-e^{-t}Ei(2t)\big{)} \big{]}\end{split}  \tag{34}\]  
If variable is changed to \(t=2x^{1/2}\), these results can be equivalently written in different form, for example (33) is  
\[ \begin{split}&\left(\frac{\partial\,W_{1,\beta+1}(-x)}{\partial \beta}\right)\Big{|}_{\beta=0}=-\ln\sqrt{x}\,J_{0}(2\sqrt{x})+\frac{\pi}{2}Y_{ 0}(2\sqrt{x})\\ &\left(\frac{\partial\,W_{1,\beta+1}(x)}{\partial\beta}\right) \Big{|}_{\beta=0}=-\ln\sqrt{x}\,I_{0}(2\sqrt{x})-K_{0}(2\sqrt{x})\end{split}  \tag{35}\]
## 6 Performance Evaluation
### The Performance of the Revised IEEE 802.11 Protocol
#### 6.2.3 The fairness of network  
The existing algorithms do not integrate with the issue of congestion and fairness of network in Section 2. In order to illustrate the revised IEEE 802.11 protocol on the fairness of network, we adopt \"CL-APCC\" and \"Drop Tail\" strategy by random sampling nodes to make simulations. During the simulations, the original priorities of data packets in the data source node are randomly set to 1, 2, and 3. The results are shown in Figure 17.  
Figure 16: The impact of revised IEEE 802.11 protocol for the RPR of the network.  
Figure 17: (a) The average initial priority of packets to BS; (b) The average sending priority of packets to BS.  
Figure 17(a) shows that the advantage of CL-APCC is not particularly evident in regard to the average priority of data packets received by BS. However, it can be seen in Figure 17(b) that the average sending priority of data packets with CL-APCC is much higher than 'Drop Tail', and the largest gap is 1.3 (the average sending priority is only 2). That is because CL-APCC considers not only the original priority of data packets but the timeliness of the network in the course of sending packets. As described in Section 4.2, in CL-APCC, the sending priority of data packets is correspondingly improved as the service time at the node increases, which ensures the fairness of network.
# Unmanned Autonomous Intelligent System in 6G Non-Terrestrial Network
## 3 UAV-Assisted Non-Terrestrial Networks
### UAV Path Planning and Control in NTN Communications
The trajectory problem was converted to a Euclidean k supplier location problem that minimizes the maximum distance of any ground users to the nearby UAV base station facility. Moreover [35], designed a deep Q learning network and greedy algorithm-based UAV trajectory optimization paradigm for unevenly distributed ground users. In [36], the authors emphasized a multi-target coverage dynamic trajectory optimization scheme utilizing a knowledge-incorporated approach considering inter-UAV connectivity maintenance. The study showed promising applications in both UAV base station communication coverage and monitoring coverage tasks.
## 5 Results and Discussion
### Comparison of the ML Models  
The overall classification performance of the two models was compared using bootstrap sampling. 1000 bootstrap samples were taken from the test dataset with replacement and the performance of both models was tested on each sample set. This gave normal distributions of accuracy and kappa (Figure 12). The mean performance of ERT is higher than that of RF, however, the 95% confidence interval for the two models overlap in terms of both accuracy and kappa as shown in Table 8.  
This indicates that statistically, ERT does not significantly outperform RF.