esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving the Sensory Quality of Black Tea by Blending Varieties During Processing
## 3 Results and Discussion
### Analysis of Volatile Components by GC-MS  
Volatile compounds were determined using SPME-GC-MS to investigate the impact of different blending methods on aroma formation in black tea. A total of 97 volatile compounds were tentatively identified by comparing their mass spectra with those in the NIST and Wiley Libraries, and selecting compounds with a similarity index higher than 80. These volatiles were categorized into nine groups: 24 esters, 15 hydrocarbons, 19 alcohols, 10 aromatic hydrocarbons, nine heterocyclic compounds, eight aldehydes, six terpenes, four ketones, and two sulfides (Supplementary Materials Table S2, Figure S5). Esters, hydrocarbons, and alcohols were the three primary volatiles, accounting for 59.8% of all identified volatile compounds.
# Assessing the Probability of Winning a Point in Men's Padel: A Comprehensive Analysis
## 3 Results
### Match Analysis  
Table 4 presents a match analysis taking into account different performance indicators. After analyzing all the matches, we observed that 11.1% of the points ended with a short rally and a final hit from zone 1. In these cases, the serving pair won 13.8% of those points through winners while the returning team (RT) won 18.2%. It is important to note that there was a high rate of points won on serve due to unforced errors (86.2% serving and 81.8% returning), with a predominance of hitting the net errors. A total of 13.1% of the points culminated in a short rally and a final hit from zone 2. In this situation, the serving pair achieved 84.7% of the points by winners (22.2% RT) and there was a balance between those obtained with cross and parallel strokes in both pairs. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Serving Team**} & \multicolumn{4}{c}{**Returning Team**} \\ \hline  & **Fr. (\%)** & **Total** & **Won** & **Lost** & **Prob.** & **Total** & **Won** & **Lost** & **Prob.** \\ \hline First service & 88.5 & 867 & 521 & 346 & 60 & 867 & 346 & 521 & 40 \\ \hline Second service & 11.5 & 112 & 60 & 52 & 54 & 112 & 52 & 60 & 46 \\ \hline First Service–Short rally & 42.9 & 372 & 264 & 108 & 71 & 372 & 108 & 264 & 29 \\ \hline First Service–Medium rally & 28.1 & 244 & 139 & 105 & 57 & 244 & 105 & 139 & 43 \\ \hline First Service–Long rally & 29.0 & 251 & 118 & 133 & 47 & 251 & 133 & 118 & 53 \\ \hline First Service-Short & 12.6 & 30 & 12 & 18 & 40 & 79 & 4 & 75 & 5 \\ \hline First Service–Short & 14.8 & 106 & 78 & 28 & 74 & 22 & 8 & 14 & 36 \\ \hline First Service-Short & 15.6 & 89 & 73 & 16 & 82 & 46 & 34 & 12 & 74 \\ \hline First Service–Medium & 5.3 & 15 & 3 & 12 & 20 & 31 & 4 & 27 & 13 \\ \hline First Service–Medium & 13.0 & 62 & 35 & 27 & 56 & 51 & 20 & 31 & 39 \\ \hline First Service–Medium & 9.8 & 47 & 33 & 14 & 70 & 38 & 28 & 10 & 74 \\ \hline First Service–Long & 4.4 & 15 & 3 & 12 & 20 & 23 & 8 & 15 & 35 \\ \hline First Service–Long & 13.8 & 53 & 32 & 21 & 60 & 67 & 41 & 26 & 61 \\ \hline First Service-Long & 10.7 & 54 & 39 & 15 & 72 & 39 & 36 & 3 & 92 \\ \hline \hline \end{tabular} Note: Fr—frequency; Total—total points occurred in that match situation; Prob—probability (%).  
\end{table} 
Table 3: Analysis of the probability of winning a point as a function of service, type of rally, and strike zone.
## 5 Results
### Evaluation of the Signal-to-Noise Ratio (SNR)  
Figure 3 shows the simulation results of the SNR when the transmitter powers are set as 1 W, 2 W, 10 W, 20 W, 30 W, and 40 W. This figure plots the SNR as a function of the transmission distance. The distances are from 100 m to 1000 m.  
First, when the transmission distance increases, the SNR decreases. This is because the \"transmission loss\" is one of the main elements of the SNR. When the distance increases, the transmission loss would be accumulated; subsequently, the accumulated transmission loss makes a negative impact on the SNR. Therefore, the SNR is inversely proportional to the transmission distance between the underwater sensor nodes. The same phenomenon occurs in all the cases of different transmitter powers. The phenomenon confirms that our channel models are reasonable (when calculating the SNR) and applicable to different transmitter powers.  
Second, when the transmitter power is higher, the SNR is higher. Specifically, when the transmission distance is the same, the transmitter powers from low SNR to high SNR are: 1 W, 2 W, 10 W, 20 W, 30 W, and then 40 W. This is because one of the main elements of the SNR is the \"source level\"; when the transmitter power is higher, the source level is higher. Thereby, the SNR  
Figure 3: Simulation results of the Signal-to-Noise Ratio (SNR) when the channel models are applied to different transmitter powers (1–40 W) and distances (100–1000 m).  
is directly proportional to the transmitter power. Because the results are consistent in all the cases of different transmission distances (from 100 m to 1000 m), the simulation results confirm that our channel models are also applicable to different distances.
# Using Connected Vehicle Data to Evaluate National Trip Trends
### Emerging Connected Vehicle Data Opportunities  
Emerging connected vehicle (CV) data can act as an alternative data source to potentially deliver a more scalable method to measure temporal and spatial usage of passenger vehicles in near real-time for a variety of applications, including trip origin-destinations, quantifying traffic congestion, travel mode choice, traffic safety, and infrastructure planning [16; 17; 18; 19; 20]. Early studies have shown that these CV datasets capture around 500 billion vehicle records in a month nationwide [21] for the United States, with a rough market penetration of 5-7% [22].  
Although the 2022 NextGen NHTS has incorporated raw mobile device location data to document national mobility, this still lacks information on the type of vehicle being used and will be reliant on cellular connectivity. CV data offers highly accurate trip statistics owing to the data being recorded directly by the vehicle with limited scope for errors, unlike the aforementioned methods, and provides additional detailed insights at the vehicular level, which may be difficult to capture with traditional survey-based methods.  
This data has already shown wide-ranging applicability to prioritize traffic signal retiming, monitor mobility in varying weather conditions, and assess work zone mobility [23; 24; 20]. Including additional probe, CV data sources provide unique opportunities to evaluate transportation infrastructure and prioritize investments, including pavement markings, pavement quality, and the deterioration of roadway conditions during winter storms using friction data [21; 25]. Combining all of these emerging CV data attributes can provide stakeholders and planning commissions with a holistic view of the transportation network and present opportunities to invest in infrastructure improvements, including electric vehicle charging. Previous studies have looked at the use of CV data to assess interstate exit utilization to aid in charging infrastructure site selection and to monitor EV and hybrid vehicle (HV) usage patterns, including operational performance measures such as vehicle miles traveled and charger utilization [17; 18].  
Thus, as outlined in the preceding text, current survey practices for collecting trip data face multiple concerns in terms of the cost and time of acquisition, low response rates, accuracy of self-reported trip data, and data representativeness, among others. CV Data, already in extensive use by multiple agencies as outlined above, has the potential to fill in these gaps and report accurate trip statistics recorded directly onboard vehicles at representative levels. It is widely available without the need for any fixed infrastructure installations to record the data. This paper uses a one-month CV dataset to demonstrate the applicability of this data in documenting national trip trends.
# Digital Shearography for NDT: Phase Measurement Technique and Recent Developments
## 5 Potentials and Limitations  
### Potentials  
Digital shearography not only has the advantages of optical NDT techniques, but it also has many advantages compared to other optical methods:  
Figure 14: (**a**) The new shearography system with a spatial light modulator; (**b**) experimental result of a thimble-loaded aluminum plate; (**c**) experimental result of a composite plate with three flaws.  
1. Digital shearography has all of the advantages of the optical NDT technique; it is full-field, noncontact, highly sensitive, and robust.
2. Compared to noncoherent measurement methods (such as DIC), digital shearography has a much higher measurement sensitivity. Combined with the phase-shift technique, digital shearography can achieve measurement sensitivity at dozens of nanometer levels.
3. Compared to other coherent measurement methods (such as holographic interferometry and ESPI), digital shearography is more robust and well suited for practical applications. This is because digital shearography directly measures the first derivative of deformation, which is the strain information. Therefore, digital shearography is less sensitive to environmental disturbances than other coherent methods.
4. Digital shearography simplifies the optical system using a self-reference interferometric device. At the same time, the self-reference interferometric device does not require a coherence length of the laser source and can be illuminated with a low-cost laser diode.
# Survey on Modelling and Techniques for Friction Estimation in Automotive Brakes
## 2 Techniques for the Estimation of BLCF in Automotive Disc Brakes
### State Observer for BLCF Estimation
#### 2.5.2 Wheel Torque Observer  
In this context, a state observer based on the linear Kalman filter is proposed as a tool to estimate the BLCF online, encompassing the chassis dynamics and even the wheels' rotational dynamics. For a better understanding of this approach, the reader is referred to the authors' previous work [73].  
As shown in Figure 10, the developed observer requires information coming from the chassis accelerometers and the hub sensors to provide an estimate of the wheel torque for each wheel. Instead, the prediction part draws upon a tire model that must be fed with the actual tire vertical load, \(F_{z_{i}}\), and wheels' longitudinal and side slips, \(s_{L_{i}}\) and \(s_{S_{i}}\), respectively.  
The filter states and sensor outputs are as follows, wherein the \(i\) index identifies the four wheels:  
\[x=\left\{\omega_{i},\dot{\omega}_{i},\ F_{x_{i}},T_{w_{i}}\right\} \tag{13}\]  
\[z=\left\{\omega_{i}^{s},F_{x_{i}}^{\mathrm{ss}}\right\}, \tag{14}\]  
where:  
\[\dot{\omega}_{i}=\frac{T_{w_{i}}-F_{x_{i}}R_{\omega}}{I_{\omega}} \tag{15}\]  
\[F_{x_{i}}=F_{x_{i}}\big{(}s_{L_{i}},s_{S_{i}},F_{Z_{i}}\big{)} \tag{16}\]  
\[F_{x_{i}}^{\mathrm{ss}}=\alpha_{i}\big{(}F_{z_{i}},s_{L_{i}},s_{S_{i}}\big{)} \times(m_{0}a_{X}-F_{R_{x}}). \tag{17}\]  
Referring to the process states of Equation (13), the hub speed of each wheel \(\omega_{i}\) is updated once its derivative \(\dot{\omega}_{i}\) is known from the wheel dynamics of Equation (15). The process parameters, \(R_{\omega}\), \(I_{\omega}\) and \(m_{\mathrm{v}}\), are the wheel radius, the wheel inertia and the vehicle mass, respectively. In accordance with Equation (16), the longitudinal tire force \(F_{x_{i}}\) is instead provided by a tire model as a function of the longitudinal and lateral wheel slips \(s_{L_{i}}\) and \(s_{S_{i}}\), and the tire vertical load \(F_{Z_{i}}\), e.g., the Burckhardt tire model [75]. Finally, for the wheel torque state \(T_{w_{i}}\), a random walk approach is used during the prediction step; it will then be corrected in the update phase. With reference to the output vector used during the update phase, \(\omega_{i}^{s}\) is the hub sensor measurement for each wheel; instead, in accordance with Equation (17), \(F_{x_{i}}^{\mathrm{ss}}\) represents the longitudinal force distributed on each wheel by means of the \(\alpha_{i}\) parameter, proportional to the vertical load \(F_{z_{i}}\) and to the slip factors \(s_{L_{i}}\) and \(s_{S_{i}}\). The apex \(vs\) stands for virtual sensor, and indeed the longitudinal force is virtually measured by exploiting the longitudinal accelerometer signal \(a_{X}\) and a model of the longitudinal resistant forces \(F_{R_{X}}\), namely the air drag and the rolling resistance. The vertical wheel loads and the slip ratios, in turn, are observed in accordance with the methodologies reported in [75; 76].
# Design of a Digital Array Signal Processing System with Full Array Element
## 2 System Architectural Design
### System Software Architecture Design  
The software architecture of the broadband digital array signal processing system is shown in Figure 8. The software process includes system control, data exchange, IF pre-processing, HIF architecture implementation, multi-channel synchronization, broadband calibration, and MIMO phased array digital multi-beam formation.  
The main goal of system control and data interaction is to realize the transmission and storage of the original baseband signal data, the control of radar parameters such as beam pointing angle and PRT, the setting of RF front-end attenuation and inverter local oscillation frequency, and the configuration of clock network and sampling, etc.  
IF pre-processing and HIF architecture implementation involve the processing of IF signals by the transceiver module before and after RF conversion, including DUC, DDC, extraction, interpolation, and other operations, to achieve HIF architecture.  
Multi-channel synchronization means that the transceiver module realizes high-speed synchronous sampling of multiple transceiver channels in IF through the internal self-synchronization mechanism of RFSoC and the external clock network synchronization. Each channel can realize synchronous sampling under a sampling rate of up to 10 GSPS.  
Figure 7: HIF architecture: (**a**) Traditional analog HIF architecture; (**b**) Digital HIF architecture.  
Broadband calibration means solving amplitude-phase errors caused by channel amplitude-phase inconsistencies in a wide band by constructing equalization filters.  
MIMO phased array digital multibeam formation involves the implementation of the MIMO phased array digital multibeam formation algorithm in the system to verify the digital array signal processing capability.
## 5 Analysis of Experimental Data
### Ground Fault Testing in TN Configuration  
The ground fault test in the TN configuration involved grounding the neutral point of branch 1, eliminating the grounding capacitor responsible for residual current in the A-phase, and removing the B-phase load in branch 1 to induce three-phase load imbalance. The fault point was positioned on damp ground to record the residual current at measurement points 1, 2, and 3, along with the current flowing through the ground wire of branch 1 post fault.  
Figure 15 reveals the dynamic nature of the fault impedance fluctuations throughout the fault process. Notably, the current amplitudes in channel 2 and channel 4 are identical, indicating that the inherent residual current on branch 1 primarily originates from the  
Figure 14: Residual current waveform of unstable fault closed section.  
Figure 13: High impedance and unstable ground fault recorder: (**a**) recorded waveform; (**b**) partial view.  
current at the grounding point. This observation substantiates the source of inherent residual current in the TN-C and TN-C-S configurations, elucidating the challenges in installing residual current protection. In Figure 15b, a magnified view of (a) depicts a scenario where the primary fault resistance gradually changes, underscoring the limitations of existing protection methods when dealing with minimal current levels.  
Figure 16 illustrates the residual current within the closed section established by measurement points 1, 2, and 3, as depicted in Figure 15. This aligns with the observations from the TT configuration test. Prior to the fault occurrence, the residual current within the closed section remains minimal, enabling it to effectively reflect the fault current post fault. Consequently, the residual current protection method remains unaffected by the inherent residual current of the system and can be successfully implemented in both the TN-C and TN-C-S configurations.  
The experiments above demonstrate the sensitivity of the proposed distributed residual current protection method based on closed sections. In practical applications, it is necessary to clarify the network topology of the low-voltage distribution network, construct closed sections by multiple LTUs, obtain the current matrix and detect possible grounding (leakage) faults according to the set threshold. However, further discussion is needed on how to adaptively adjust the threshold when considering complex scenarios such as an unknown network topology or choice of LTU installation location.
# Improved Video Action Recognition Based on Pyramid Pooling and Dual-Stream C3D Networks
## 2 Methodology
### Feature Fusion Based on Dynamic and Static Features
#### 2.2.2 Gaussian Distribution-Based Feature Fusion  
Both dynamic and static features can be regarded as a set of vectors, and the initial description of these two types of features through histograms can be further fitted using Gaussian distribution. Specifically, assuming that the static and dynamic features are represented as two vectors, these features can be described by a two-dimensional Gaussian distribution function as shown in Equation (9) [13].  
\[P(N)=\frac{1}{2\pi\sqrt{\left|\sum\right|}}e^{\left[-\frac{1}{2}(N-\mu)^{T} \sum^{-1}(N-\mu)\right]} \tag{9}\]  
where N is a vector consisting of static and dynamic features, \(\mu\) is a mean vector containing the mean \(\mu_{s}\) of the static features and the mean \(\mu_{m}\) of the dynamic features, i.e., \(\mu=\begin{bmatrix}\mu_{s}\\ \mu_{m}\end{bmatrix}\) is the covariance matrix, which represents the correlation between the static features and the dynamic features and is usually denoted as \(\sum=\begin{bmatrix}\mu_{s}^{2}&\rho\sigma_{s}\sigma_{m}\\ \rho\sigma_{s}\sigma_{m}&\mu_{m}\end{bmatrix}\), where \(\sigma_{s}\) is the standard deviation of the static features. \(\sigma_{m}\) is the standard deviation of the dynamic features, and \(\rho\) is the correlation coefficient between the static features and the dynamic features [14].  
As the scales of static and dynamic features are usually inconsistent, normalisation is required. To eliminate the effect of scale inconsistency on the results, scaling can be performed using a scale matrix. Specifically, the static and dynamic features are scaled using the following scale matrix as in Equation (10) [15]  
\[Scaling\quad matrix=\begin{bmatrix}\frac{\sigma_{s}}{\sigma_{s}+\sigma_{m}}&0 \\ 0&\frac{\sigma_{m}}{\sigma_{s}+\sigma_{m}}\end{bmatrix} \tag{10}\]  
This scaling process can help to balance the scale differences between static and dynamic features, thus ensuring that static and dynamic features can work together to form a more stable feature fusion in a Gaussian mixture model [16].
## 3 Modeling and Results
### Development of the NTL Attenuation Map  
PSVE plays a crucial role in guiding post-disaster reconstruction efforts by selecting specific regions of interest to predict power system damage. During verification, we chose a sample region from a historical earthquake event that aligns with the seismic characteristics of the area and exhibits relevant power attenuation. This deliberate selection ensures that the verification results offer a more accurate depiction of potential impacts. Subsequently, we have developed damage distribution maps for the power system after earthquakes at both micro and macro scales.  
At the micro level, these maps are generated based on seismic event groupings and display only the spatial regions that may be affected, as depicted in Figure 8. The approxi  
Figure 7: Learning curve of each model. Vertical axis: ROC_AUC; horizontal axis: the number of samples participated in training; the transparent areas represent confidence intervals.  
mate location and estimated value of the power system damage are shown in the figure, so the PSVE can serve the emergency response after a disaster.  
Taking a broad temporal and spatial perspective, we developed Figure 9, grouped by year, covering the entire study area. These maps provide valuable insights into a city's seismic resilience and its preparedness for emergency management. A larger circular area with a lower color value indicates that the city remains relatively unscathed by higher-magnitude earthquakes. Conversely, a smaller circular area with a higher color value signifies significant light attenuation, suggesting potential economic and social consequences in the aftermath of major earthquakes. Of particular concern are the yellow dots appearing in earthquake-prone regions. These dots indicate that even in low-magnitude but high-frequency earthquakes, there may be unforeseen impacts on the city, as evidenced by the night light attenuation. In Figure 9b, it can be found that on a large spatio-temporal scale, it is necessary to focus on the seismic capability of power systems in some prefecture-level cities, such as Yuxi, Leshan, etc.  
Figure 8: Damage distribution of the urban power system after earthquake. Each damaged area is marked with a light attenuation value, unit is nW/cm\({}^{2}\)/sr. (**a–d**) Four different seismic events are represented.  
From a real-time and localized standpoint, these maps enable the city to quickly assess the extent of the potential impact based on simple parameters immediately following an earthquake. This information can serve as crucial decision support for post-earthquake response efforts.
## 3 Datasets and Evaluation Metrics
### HDR-VDP3
#### 3.2.2 Application of LDR Metrics to HDR Images  
Although LDR quality metrics are widely used, their direct application to HDR images is not straightforward due to the nonlinearity between color values and visual perception. To address this, the HDR content must be adapted, either via tone mapping or using methods like PU21. PU21, for example, adjusts pixel values to a scale closer to human sensitivity using linearizing perception to facilitate the application of LDR metrics [37]. This makes it an alternative to using tone mapping operators as a preprocessing step when evaluating HDR images with metrics originally developed for LDR.