esa-sceva/satcom-chunk-collection huggingface dataset:
# Predicting Shot Accuracy in Badminton Using Quiet Eye Metrics and Neural Networks
## 6 Discussion
### Interpretation of Key Findings  
Our results highlight several important insights:  
1. The strong predictive power of QE duration and fixation points in determining shot accuracy underscores the importance of visual attention strategies in badminton performance.
2. Elite players exhibited significantly longer QE durations (M = 289.5 ms) compared to intermediate (M = 213.7 ms) and novice players (M = 168.3 ms), supporting QE as a marker of expertise.
3. The strong positive correlation (r = 0.72) between QE duration and shot accuracy across all skill levels suggests that QE training could benefit players at various stages of development.
4. The varying performance of our model across different shot types (smashes: 89.2%, drops: 84.5%, clears: 83.4%) indicates that QE strategies may need to be tailored to specific shot requirements.
## 4 Realization of the Processing Algorithm
Compared to the MLSE, algorithm (9) has a lower memory consumption since it does not depend on the packet length. For example, when packet length is \(N=100\), the gain in memory consumption reaches more than two orders. Figure 2: Possible realization of processing algorithm (9) for SDR. Figure 3: Possible realization of processing algorithm (9) for SDR. We can make a subconclusion that the proposed decision feedback with the estimation of subsequent symbols in the observation interval by the maximum likelihood ratio can be implemented on the basis of a universal digital platform using universal microprocessors (e.g., starting from family Intel 3000) as a PCHost. We obtained the values of the computational complexity of the proposed algorithm depending on signal parameters (signal duration, level of ISI). Using these values, it is possible to estimate the increase in the computational complexity under conditions of fixed energy losses and an increased transmission rate. In conditions of specific transmission rates up to 3 bps/Hz and signal duration (8-16)\(T\), the computational complexity of the proposed algorithm is more than two orders lower compared to that of MLSE.
# Design, Implementation, and Field Testing
## 3 System Design
### System Design Rationale  
#### 3.2.1 Privacy Protection Design  
The rationale for our privacy protection mechanism has been given in our previous work [29; 30; 32]. We provide a brief overview here. To ensure the privacy protection, we devised a registration mechanism where a consenting nursing assistant must make a specific pose while pressing a button on the smartwatch. On pressing the button, the Kinect runtime would attempt to find a particular user in its view that is making the predefined registration posture. If one and only one is found, that user is identified to be the consenting nursing assistant. This registration posture must be a posture that is not normally made by any nursing assistant or the patient. Hence, the system would only register the consenting nursing assistant.  
Furthermore, the system tracks a user only via the skeleton data, not the color video frames, depth frames, or infrared frames. Our system guarantees that only the skeleton data that belong to the tracked user are analyzed for possible non-compliance activities. Our system logs only three types of events: (1) the registration of a user when the tracking of that particular user starts; (2) the user has gone out of the view of the Kinect, or the user has instructed Kinect to stop tracking, when the tracking of the user stops; and (3) when a non-compliance activity is detected.  
In addition to the registration mechanism, which relies on the synchronization of two modalities, we also considered other approaches to identify a consenting nursing assistant. One alternative approach is the use of a marker where a nursing assistant would place the marker on her or his uniform. However, this approach may require the marker to be visible all the time, which is not practical because a nursing assistant could be in any position relative to the Kinect sensor. Doing so would also significantly increase the complexity of the system because the system would have to do both skeleton tracking andobject detection concurrently the entire time. Another alternative could be the use of NFC (near-field communication) sensors. However, because NFC's operating range is only up to 10 cm, we cannot use an NFC sensor to identify which skeleton is the participating nursing assistant.  
In summary, the system does not use color or greyscale frames to operate and does not record such frames in anyway. Hence, an administrator or a hacker cannot misuse the system for any purposes that might violate the privacy of the patients, or track a nursing assistant in a way not intended by our design.
## 3 Results and Discussion
### Vitamin C Analysis by HPLC  
The results of HPLC analysis for vitamin C are shown in Table 3. Vitamin C content in all honey samples was between 0.25-2.59 mg/100 g. Sidr (_Ziziphus spina-christi_ L.) had the highest vitamin C content of 2.59 mg/100 g, whereas Spring flower honey had the lowest vitamin C quantity of 0.25 mg/100 g. Tallh (_Acacia gerrardii_ Benth) and Langnese vitamin C content were in the moderate range of 1.4 and 1.35 mg/100 g, respectively. Previous studies showed that vitamin C content varied in honey due to the geographical and plant sources [31]. For instance, Vitamin C in echium honey, lavelander honey, and honeybee are 2.18, 2.11, and 0.77 mg/100 g, respectively [35]. Furthermore, the content of Vitamin C in Sulla honey was 0.84-1.5 mg/kg, and in citrus honey it was found to be 1.6-3.0 mg/kg [48]. Vitamin C content ranged between 88-457 mg/100 g in the honey produced from different altitudes in the south-western part of Saudi Arabia [25]. Vitamin C in various floral sources honey could not be detected by Ghedolf et al. [49]. Vitamin C was detected at a very low level in rape, acacia, and multiiflora honey [50].
## 3 Numerical Modeling of the Xulong High Arch Dam
### Brief Introduction of Xulong Super-High Arch Dam  
The Xulong hydropower station is located on the main stream of Jinsha River, juncture of the Degin county, Yunnan Province and Derong county, Sichuan Province. The total storage capacity of it is 829 million m\({}^{3}\), and it has an installed capacity of 2220 MW. The principal structures consist of a double-curvature arch dam with a height of 213 m, underground powerhouse, diversion tunnel, and plunge pool. There are 3 upper outlets and 4 middle outlets placed in the numbers 9-12 dam monolithts. The entrances of the upper outlets are at elevation level (EL) 2286 m. The size of the middle outlets is 8 m \(\times\) 6 m (height \(\times\) width) at the entrances and 5 m \(\times\) 7.2 m (height \(\times\) width) at the exit. The entrances of the middle outlets are at EL 2222 m. The ratio of thickness and height of the arch dam is 0.217 and the length of the crest on the upper surface is 482.9 m.  
The dam site is deep canyon topography with an aspect ratio of 1.8. The Triassic Indosinian granite dike at the dam site slopes into the riverbed. The left bank is Mesoproterozoicxiongsong Group plagioclase amphibole schist and the right bank is Mesoproterozoicxiongsong Group migmatite. According to the double-hole acoustic testing results, the fresh granite, migmatite, and plagioclase amphibole schist have longitudinal wave velocities of 4300-5900 m/s, 4000-5600 m/s, and 3000-5800 m/s, respectively. The average longitudinal wave velocities of the fresh granite, migmatite, and plagioclase amphibole schist are 5100 m/s, 5000 m/s, and 4600 m/s, respectively, which can mainly be classified as the type II rock mass. The type II rock masses have an acoustic velocity of 4800-5500 m/s, weak permeability, and good uniformity. The width of strongly unloading zones due to the dam construction is generally less than 30 m. The strongly unloading rocks are only at the EL2300 and 2308 m, which can be classified as the type IV rock mass. The width of weakly unloading zones is between 15 and 35 m with the type III1 rock masses and III2 rock masses. A total of 70 faults are found on the surface of the dam site, of which the statistics are shown in Figure 4. Ten of them are low-angle faults. The width of the faults is 0.20-0.50 m, and the length of the faults is usually less than 100 m. The fault strikes can be divided into 4 groups: NE\(\sim\)NEE group, NWW group, NNE group, and NNW\(\sim\)NW group. F1, f3, f10, f11, f26, f57, f74, and f75 are relatively large faults. Figure 5 illustrates the bedrock distribution of the dam-foundation interface.
# Simulation of Test Arch Based on Concrete Damage Plasticity Model and Damage Evolution Analysis
## 5 Analysis of Results
### Analysis of Displacement Results
#### 5.1.2 Damage Conditions at the Top Plate of the Arch Foot  
Figure 13 illustrates the displacement changes following damage to the top slab of the springing. The graph revealed that the structural displacement disparity escalated from the 10 th load step onward. Within the region highlighted by the dashed box, the displacement difference exhibited a quadratic growth pattern, which was markedly amplified compared to the effects on the bottom slab. The impairment to the top slab was considerably more consequential, precipitating an earlier structural breakdown. Nonetheless, owing to the lagged intervention of the reinforcement, the damage to the top slab culminated in a ductile failure. This type of failure was preferable in real-world engineering scenarios as it allowed for structural evaluations before reinforcement was applied. Conversely, escalating  
Figure 11: Energy time course graph.  
the damage rate on the top slab of the springing intensified the overall degradation of the structure.
# MEC Server Sleep Strategy for Energy Efficient Operation of an MEC System
## 4 Deep Learning-Inspired Sleep Control Strategy  
In Figure 1, we show the overall procedure of our MECS sleep control strategy. Our MECS sleep control method is composed of two modules. In the first module, at the beginning of each time slot \(t\), a controller predicts \(\{\hat{\lambda}_{i}(t+1):i\in\mathcal{M}\}\) by using the spatio-temporal correlations among the recent past \(\{\{\hat{\lambda}_{i}(t)\},\{\hat{\lambda}_{i}(t-1)\},\ldots,\{\hat{\lambda}_ {i}(t-h+1)\}\}\), where \(h\) is the length of the past task arrival rate history. We denote the predicted \(\hat{\lambda}_{i}(t)\) as \(\bar{\lambda}_{i}(t)\). The second module uses the genetic algorithm to determine the MECS sleep control vector \(\Omega(t)\) by using \(\bar{\lambda}_{i}(t)\), \(Q_{l}(t+1)\), and \(F_{i}\), for all \(i\in\mathcal{M}\). We explain the operation of each module in the following subsections.
# Airborne Radar Staggered PRF Coherent Processing Method for Down-Looking Target Detection
## 4 Simulation Results
### Effectiveness Analysis of Doppler Ambiguity Resolution with Coherent Processing
Figure 10b demonstrates the outcome of coherent processing for ambiguity resolution of the multiple targets. It is observed that the correct Doppler frequency manifests as a peak, which is the target, while the ambiguous false target is suppressed. The results of coherent processing before and after multi-target's ambiguity resolution are shown in the Figure 11. It can be seen from Figure 11 that multi-target's Doppler frequencies have obtained coherent processing gains, which are 9.5 dB higher than the energy before coherent processing. The above experiments have verified the coherent processing ability of the multiple PRFs in this paper to solve Doppler ambiguity of the target, and then verify the effectiveness of the airborne down-looking target detection working mode. Figure 9: The result of de-ambiguous and coherent processing after applying a window. (**a**) The result of de-ambiguous and coherent processing after applying a window. (**b**) Comparison of coherent processing before and after (with windowing)
## 2 Methods  
### Research-to-Commercialization (R2C) Model  
We developed a research-to-commercialization (R2C) model (Figure 2) that integrates the WKID Innovation and HCD methodologies. The HCD approach engages a variety of tools and frameworks to assess user needs and market conditions. Leveraging the outcomes of interviews, we developed user stories, use cases and an ecosystem map to define the current market landscape. To find the intersection of innovation potential, we estimated the user desirability, business viability, and technical feasibility of a development based on qualitative and market research. Our HCD process brought potential end users into the design process through interviews and focus groups to define the problem set, narrow potential solution sets, and test initial solution prototypes and wireframes. The HCD process also aims to ensure end user needs are met and aligned to organizational requirements by reviewing enterprise architectures for industry-provided technologies capable of scaling to support big organizations.  
WKID Innovation uses two core tools: the Change Traceability Matrix (CTM) and a Product Traceability Matrix (PTM) [25]. The CTM organizes information to provide a comprehensive look across the decision space to identify common information needs and specifications to change the way decisions are made. It documents everything from key performance indicators (KPIs, e.g., \"# lives saved\" or \"$ saved\"), the decisions being made, by whom and for what purpose, how decisions are funded, what motivates the decision, the tools currently in use, the information needs, gaps and limitations, as well as key parties engaged and affected by the problem. The PTM provides high-level \"requirements\" on product definition. WKID Innovation does not assess market viability explicitly but rather assumes that if there is a need and it fits the decision space, it has value, which may not always be the case.
## 3 Results and Discussion
### Major and Trace Elements  
From the geochemical point of view, the samples were of a high siliceous character, reaching 30.2%, with significantly high Fe contents (max: 8.0%) in some samples (Table 2). The high Al contents indicated a high proportion of clays. The generally low concentrations of Ca confirmed the absence or scarcity of carbonates and sulfates in these soils. Trace elements showed concentrations lower than the reference values published by [PERSON] et al. (2010) [18], confirming the lithological baseline character of the values considered in this study. The reference values described by [PERSON] et al. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Variable** & **Average** & **SD** & **VC** & **Minimum** & **Maximum** \\ \hline All data & & & & & \\ \hline pH & 6.1 & 0.5 & 8.3 & 4.5 & 7.8 \\ \hline EC (\(\mu\)S cm\({}^{-1}\)) & 30.0 & 20.0 & 47.9 & 10.0 & 110.0 \\ \hline SOM (\%) & 4.9 & 1.8 & 36.9 & 1.5 & 13.3 \\ \hline Almadén syncline & & & & & \\ \hline pH & 6.0 & 0.5 & 8.2 & 4.8 & 7.0 \\ \hline EC (\(\mu\)S cm\({}^{-1}\)) & 30.0 & 20.0 & 50.4 & 20.0 & 110.0 \\ \hline SOM (\%) & 5.5 & 2.1 & 37.1 & 1.5 & 13.3 \\ \hline Guadalmez syncline & & & & & \\ \hline pH & 6.1 & 0.5 & 8.5 & 4.5 & 7.8 \\ \hline EC (\(\mu\)S cm\({}^{-1}\)) & 30.0 & 10.0 & 44.1 & 10.0 & 90.0 \\ \hline SOM (\%) & 4.4 & 1.5 & 34.4 & 1.9 & 8.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Statistical summary of pedological parameters. Abbreviations: SD: standard deviation; SOM: soil organic matter; VC: variation coefficient. Complete data available in Table S1. Figure 2: Texture of samples. ASQ: alternations of shales and quartzites. (2010) [18] belong to a more restricted soil samples dataset, in terms of sample number, but from the entire Castilla-La Mancha region, including siliceous and carbonated materials; therefore, the lower values presented in this work can be considered as baseline values for siliceous units. A group of trace elements reached concentrations greater than a hundred (Mn > P > Ba), but most were higher than ten (Pb > Zn > V > Cr > Ni > Cu > As > Co > Sr), with a third group having very low concentrations (Sb > Hg > Be > Ag) (Table 2). The trace elements of the second group were found to be common in the mineral paragenesis of the Pb-Zn-Ag vein deposits of the Alcudia Valley, mainly hosted in the Precambrian substrate, but with their presence also in Almaden and Guadalmez syncline materials. It is noteworthy that the most representative deposits of the two synclines were elements belonging to the third group (Hg and Sb), with concentrations below tens of ppm but with a very high variation coefficient (VC). As shown in Table 2, some elements showed higher concentrations than the reference values for Spanish soils [17]: Hg (2.9 mg kg\({}^{-1}\)), Pb (56.5 mg kg\({}^{-1}\)), and Sb (4.1 mg kg\({}^{-1}\)). These are elements related to ore deposits in the southwest sector of Ciudad Real Province, and their higher contents can be related to low-grade regional anomalies but also to the anthropogenic pollution related to mining. The mines in this sector have been in operation
# Stability Analysis of Lane-Keeping Assistance System for Trucks under Crosswind Conditions
## 5 Conclusions  
This study proposed an LKAS control strategy based on the LQR. To determine the appropriate front wheel steering angle and enable the vehicle to smoothly remain in the center of the lane under crosswind conditions, a path-tracking preview algorithm was used to calculate the lateral position deviation. However, this method did not yield satisfactory control results under complex conditions with boundary constraints. Therefore, an LQR algorithm was established for optimal control. However, optimization resulted in steady-state errors. Thus, a feedforward controller based on the LQR and a linear two-degrees-of-freedom vehicle model were developed to eliminate the steady-state errors. To achieve more accurate control, a sideslip angle observer based on the Kalman filter and a lateral tracking deviation model of the vehicle were used to estimate the sideslip angle and yaw rate deviations. The proposed control strategy was verified through a combination of simulations using TruckSim and Matlab/Simulink and hardware-in-the-loop experiments. The results demonstrated that the method effectively kept the vehicle in the center of the lane under crosswind conditions, indicating robust performance. However, no actual vehicle testing was conducted here, and further research is needed on the application of the LKAS control strategy in actual vehicles.  
Conceptualization, C.L. and L.H.; methodology, H.L. and C.L.; software, H.L.; validation, H.L.; investigation, H.L. and D.Z.; writing--original draft preparation, H.L.; writing--review and editing, D.Z.; supervision, C.L.; funding acquisition, L.H. and C.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Liaoning Province Higher Education Intercollegiate Cooperation Project, grant number 2021-21.  
Not applicable.  
The authors declare no conflict of interest.
# Information Fusion in Autonomous Vehicle Using Artificial Neural Group Key Synchronization
## 3 Proposed Methodology
### Assessment of Security
#### 3.3.3 Encoding of Information  
Encrypting every information sent is the best way to totally avoid snooping and the leakage of confidential data in V2X transmissions. Key sharing techniques are often used in most existing data transmission to provide verification between two transmission participants. The contents of the sent communication are then protected using symmetric encryption methods. In most data transmission techniques, however, encrypting all transactions is always computationally costly. V2X data transmission networks, as mentioned in Section 1, have limited tolerance for processing overhead. Encoding will be employed as a supplemental technique for transferring confidential data, which is a more feasible architecture.
# Recent Progress on Graphene-Functionalized Metasurfaces for Tunable Phase and Polarization Control
## 4 Polarization Modulation in Graphene Metasurfaces
### Graphene-Based Half-Wave Plate
The designs in [87; 89; 94] show a broad dynamic bandwidth around 70\(\sim\)90%. Table 1 indicates that the design with a wide static bandwidth is more likely to have a wide dynamic bandwidth. Please note that all the data with the \(\sim\) sign are estimated from the plots and without the \(\sim\) sign are the exact ones mentioned in the literature.
# Cleaning Phenolic Compounds Present in Water Using Salting-Out Effect with DCA-Based Ionic Liquids
## 3 Results and Discussion
### Effect of the Salt Concentration in the Extraction Yield  
Regarding the study carried out, three different concentrations of salt were studied at 20, 30, and 40% (\(w/v\)). Regarding the obtained results, an increase in the salt concentration slightly improved the extraction efficiency. It is possible to observe this behavior in the initial concentrations, as is shown in Figure 4, for the extraction of phenol, where at the lowest initial concentration, the extraction efficiency increased from 93.84% to 96.52%, increasing the % of the salt. This increase was higher for the results obtained using [EMim] [DCA], as can be observed in Figure 5, where the final concentration of o-cresol using both IL [EMim] [DCA] (a) and [BMim] [DCA] (b) is represented, at the three studied concentrations of salt, for all initial concentrations. For the extraction using 30% and 40% of salt, the final concentration of o-cresol was lower than 10 mg\(\cdot\)L\({}^{-1}\) for all the initial studied concentrations using [EMim] [DCA], and these results were higher for the extraction using [BMim] [DCA].  
In work published by [PERSON] et al. [7], it is explained that the presence of salts in water generally increases extraction efficiency. This behavior is explained by the lower \"free water\" available in the aqueous medium, and thus, the apparent phenolic compound concentration increases.
## 2 Materials and Methods
### Data Sources and Pre-Processing
After downloading, the data were reviewed and screened to select raster data with an appropriate spatio-temporal resolution and to handle cases of missing and incorrect data. Most of the data ranges covered China, but the NDVI data covered the whole world, so the data had to be cropped according to the vector range for the QTP. The code was written in Python v3.8.5 to crop the study area because the data volume was excessively large. Finally, to meet the research needs in terms of temporal resolution, codes were written in MATLAB v2019a to calculate the multi-year annual average, seasonal average, and monthly average snow depth, as well as the annual maximum, seasonal maximum, and monthly maximum NDVI, and multi-year annual average and monthly average temperature, precipitation, and solar radiation for lagging analysis. All data were sampled at a spatial resolution of 0.083\({}^{\circ}\) (-10 km) for correlation analysis. As shown in Figure 2, In this study, we mainly analyzed according to the following three steps. (1) MATLAB v2019a was used to calculate the multi-year mean snow depth and NDVI values, as well as the one-dimensional regression trends in snow and vegetation over a long time series of 40 years, and explored the correlations and differences in the spatial and temporal changes in snow cover and NDVI. (2) Pearson's correlation coefficients were calculated to determine the temporal correlations between multi-year mean snow cover and NDVI, meteorological elements and the changes in NDVI. We spatially analyzed the changes in snow cover and NDVI according to Pearson's correlation coefficients. (3) Using the lag analysis method, we calculated the temporal lag and spatial distribution of the seasonal lag. We also investigated the long-term lag effect on NDVI under abnormal increases in the snow depth caused by heavy snow events.