esa-sceva/satcom-chunk-collection huggingface dataset:
# Yeast Starter Culture Identification to Produce of Red Wines with Enhanced Antioxidant Content
## 3 Results

Table 1: Analysis of Total Phenolic Content (TPC) and Trolox Equivalent Antioxidant Capacity (TEAC) of wines obtained from selected yeasts based on the least reduction of the polyphenonic component. The results of the nine best-performing strains were reported in bold. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Strain**} & \multirow{2}{*}{**Ethanol**} & \multirow{2}{*}{**Sugars**} & \multirow{2}{*}{**TA**} & \multirow{2}{*}{**VA**} & \multirow{2}{*}{**Acid**} & \multirow{2}{*}{**Lactic**} & \multirow{2}{*}{**Glycerol**} \\  & & & & & & & \\
6993 & 11.45 \(\pm\) 0.023 & 1.6 \(\pm\) 0.034 & 7.11 \(\pm\) 0.008 & 0.14 \(\pm\) 0.013 & 2.58 \(\pm\) 0.005 & 0.15 \(\pm\) 0.004 & 6.38 \(\pm\) 0.055 \\
6920 & 11.58 \(\pm\) 0.004 & 1.34 \(\pm\) 0.059 & 7.14 \(\pm\) 0.056 & 0.07 \(\pm\) 0.15 & 2.64 \(\pm\) 0.002 & 0.08 \(\pm\) 0.001 & 6.54 \(\pm\) 0.088 \\
8766 & 11.43 \(\pm\) 0.061 & 1  
Table 4 reports the mean values and standard deviations of phenolic molecules identified and quantified in wines obtained by the nine selected yeast strains. Among the hydroxybenzoic acids, gallic acid was detected, while in the hydroxycinnamic acid derivatives, caffeoyl tartaric (caftaric) and \(p\)-coumaroyl tartaric (coutaric) acids were detected in the wine samples. Gallic acid showed higher amounts in wine produced by the 6993 strain. Caftaric acid was more concentrated in wine obtained from the 6993, 6920, 8766, 9507, and 9508 strains compared to the control strain. Coutaric acid showed a higher content in wines fermented by all strains, except 9501 and 9530, when compared to the control. The identified flavonoid compound quercetin showed higher content in all samples in comparison to the control strain, except for the wine fermented by the 9530 strain. Catechin, belonging to the flavanol group of flavonoids, had a significantly higher amount only in wine fermented by 6993 strain. Oenin was the non-acylated anthocyanin more abundant in all samples; its content was higher than control in wines fermented by the 8766 and 9500 strains. Trans-resveratrol, belonging to the stilbene class of polyphenols, showed a higher amount in wines fermented by the 6920, 9500, 9501, and 9507 strains compared to the control. The obtained data (Table 4) indicated that wines produced using the 9530 yeast strain starter contains significantly lower or comparable amounts of all identified polyphenol
## 3 Results and Discussions
### Ballistic Performances of the 3D Warp Interlock Fabrics
#### 3.1.1 Analysis of Backface Signature Depth and Diameter Values.
On the contrary, sample 3D-40-8B:0S revealed the highest BFS diameter compared to its entire counterpart 3D warp interlock fabric sample, except sample 3D-40-4B:8S where all of its target position were penetrated. Except for its penetration at shot point 6, sample made with 2D plain weave fabric (2D-35) records lower BFS diameter at shot 2 and 5, but higher at shot points 1 and 4 as compared to samples made of 3D warp interlock structures. Sample 2D-35 revealed lower BFS values compared to 3D-40-8B:0S and 3D-40-4B:8S for shot point except shot 6. In contrary, Sample 2D-35 revealed higher values than sample 3D-40-8B:4S followed by 3D-40-8B:8S in the majority of the target shot. Moreover, among all the 2D and 3D warp interlock fabric samples, 3D-40-8B:4S revealed much lower BFS diameter in the majority of the target shot. The conditions of the sample (molded and non-molded target situations) could also reduce the ballistic performances and increased the BFS depth of the final panels at the specified target points.
## 5 Conclusions  
Evidence of drought-induced VOCs impacting on \(\mathrm{WW_{r}}\) was shown as \(\alpha\)-pinene decreased in both co-located vines. Although isoprene emission was not altered in the receivers, a significant increase in the compound's emission in \(\mathrm{DS_{e}}\) compared to \(\mathrm{DS_{c}}\) was noted, suggesting an effect of \(\mathrm{WW_{r}}\) on \(\mathrm{DS_{e}}\) responses. Evidence of the effect of \(\mathrm{WW_{r}}\) on the emitters was also shown by the general trend of the gene expression of targeted genes related to MeJA, MeSA, and \(\alpha\)-pinene, i.e., AOS, CHORS, and VvPNaPin1, respectively, which was towards an upregulation in \(\mathrm{DS_{e}}\) only. This trend, together with the differential responses in the VOC emissions of isoprene and \(\alpha\)-pinene between \(\mathrm{DS_{e}}\) and \(\mathrm{DS_{c}}\) vines, suggested a bi-directional interaction between the co-located treatments.  
This study provides further evidence to that of [PERSON] [40] of the effect of well-wated plants on drought-stressed plants in grapevine and provides a novel view of bi-directional plant-plant communication between stressed and non-stressed plants, which is worth exploring for other environmental stressors and plant systems. The outcomes also suggest that isoprene and \(\alpha\)-pinene may be among the signalling molecules that mediate interplant interactions under abiotic stress. Future studies should investigate the potential of terpenoids to act as priming agents to increase water use efficiency and drought resilience.  
Conceptualisation, J.M., D.W.J., U.B., S.Y.R. and V.P.; investigation, visualisation, writing--original draft preparation, J.M. and V.P.; methodology, J.M., D.W.J., D.L.C. and V.P.; supervision, writing--review and editing, D.W.J., U.B., S.Y.R. and V.P.; project administration, funding acquisition, D.W.J., S.Y.R. and V.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Australian Research Council Training Centre for Innovative Wine Production (www.ARcwinecentre.org.au; project number IC170100008) and funded by the Australian Government with additional support from Wine Australia, Waite Research Institute and industry partners. The University of Adelaide is a member of the Wine Innovation Cluster. [PERSON] is a recipient of the Wine Australia supplementary scholarship (WA Ph2104) with levies from Australia's grape growers and winemakers and matching funds from the Australian Government.  
The data presented in this study are available on request from the corresponding author.  
The authors thank [PERSON] and [PERSON] for encouragement and useful discussion.  
The authors declare no conflict of interest.
# Asymmetric Protocols for Mode Pairing Quantum Key Distribution with Finite-Key Analysis
## 4 Conclusions  
In this paper, we demonstrate the performance of the asymmetric MP-QKD protocol with finite-key analysis. By combining universal composability security analysis and practical decoy state methods, our work aligns more closely with real-world experimental conditions. In the PSO process for asymmetric MP-QKD, deploying an asymmetric intensity strategy results in a noticeable improvement in the SKR compared to adding extra attenuation and is more practical for real-world deployment. However, the SKR still remains lower than in the symmetric case. Simulation results indicate that the decoy state \(v_{a}\) (\(v_{b}\)) is more susceptible to HOM interference, closely aligning with \(\eta_{a}v_{a}\approx\eta_{b}v_{b}\). However, the signal state \(\mu_{a}\) (\(\mu_{b}\)), influenced by multiple factors, significantly deviates from this assumption. Increasing the maximum pairing interval \(l\) can enhance the SKR in asymmetric MP-QKD, but it still falls significantly short compared to the symmetric case and remains unable to surpass the PLOB bound. Our research advances MP-QKD towards more practical network configurations and provides theoretical support for future MP-QKD quantum communication networks.  
Conceptualization, Z.L. and T.D.; methodology, Y.X.; validation, Y.L., H.M. and J.T.; formal analysis, W.K.; investigation, Y.X.; writing--original draft preparation, Z.L.; writing--review and editing, T.D. and W.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Innovation Program for Quantum Science and Technology (2021 ZD0301300), the State Key Laboratory of Information Photonics and Optical Communications (No. IPOC2024 ZT10).  
Not applicable.  
**Data Availability Statement:** Dataset available on request from the authors.  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
Figure 5: Optimized SKR with asymmetric intensity strategy versus the total distance (\(L_{A}+L_{B}\)) with different numbers of total pulses \(N\), where \(l=2000\) and \(\Delta L=100\) km.
# A Simple and Stable Atmospheric Pressure Electrodeless Water Vapor Microwave Plasma Torch
###### Abstract  
An atmospheric pressure microwave plasma source operating on water vapor has many potential applications. To avoid the corrosion of metal electrodes in a traditional water vapor microwave plasma system, we propose a simple water vapor electrodeless microwave plasma device. By introducing a ceramic tube, the device can work directly with liquid water without complex evaporation equipment. This study examined the relationship between microwave power and water vapor torch plasma duration. When the microwave power is greater than 800 W, the plasma torch can be excited permanently and stably without the loss of ceramic. The excitation of the oxygen atom, hydroxyl radical, and hydrogen atom was found using optical spectroscopy, confirming the water vapor's decomposition. In addition, it was also found that the crystallinity of the ceramic was improved after microwave discharge. This work enriches the microwave plasma techniques for water vapor for various applications, such as electric propulsion, hydrogen production, and surface treatment.  
water vapor plasma torch; ceramic tube; microwave discharge; electrodeless Article  
A Simple and Stable Atmospheric Pressure Electrodeless Water Vapor Microwave Plasma Torch  
1 School of Artificial Intelligence, Anhui University of Science and Technology, Huainan 232000, China; [EMAIL_ADDRESS]  
2
## Materials and Methods
### Development of Three-Axis Accelerometer Sensing System for Safety Helmet Wearing Management  
#### 2.2.1 Platform for Saving and Analyzing Sensing Data  
As shown in Figure 4, the proposed system consists of a three-axis accelerometer sensor module attached to the safety helmet, a smartphone app for the worker wearing the helmet, and an on-site PC-based database platform that stores the data.  
The data collected by the three-axis accelerometer sensor attached to the safety helmet are first transmitted to the smartphone of the worker who is wearing the helmet. The data are stored in the developed smartphone app, and Bluetooth is used for communication, the proposed system performs transmission by using Serial Port Profile (SPP) based on Bluetooth V 3.0.  
These data are then sent to the on-site PC-based database platform via 3G/LTE and uploaded as shown in Figure 5, where the X, Y, and Z outputs of the three-axis accelerometer sensor are listed sequentially, and the data collection times and dates are entered.  
The worker data are transmitted to the on-site PC-based database platform and analyzed in real time. The data transmission frequency was set up to 4 Hz between smart helmet and PC-based database platform. If the helmet is not being worn or is being worn improperly, a warning is sent to the smartphone app of a designated site manager, such as a task leader.  
Figure 4: Data transmission in the three-axis accelerometer safety helmet sensing system.  
Figure 5: On-site PC web platform for storing data (Acc X, Y, Z—Magnitude).
# Simulation and Design of Three 5G Antennas
## 4 Metal Frame Mobile Terminal Dual-Element \"Loop-Groove\" MIMO Antenna Design
### Optimization of the \"Ring-Slot\" Structure Parameters
#### 4.3.2 Optimization of Ground Slot Length  
The resonant frequency of the slot antenna is closely related to the electrical length of the rectangular ground slot. Consequently, variations in the ground slot length (L) have a significant impact on the slot antenna while the impact on the ring antenna can be neglected. We shall now explore the changes in the reflection coefficient of the slot antenna in the dual-element \"ring-slot\" structure with respect to the length (L) of the rectangular ground slot, as illustrated in Figure 36.  
Figure 34: Schematic diagram of the dual-element “ring-slot” antenna structure.  
Figure 35: The impact of metal frame height (h) variations on the reflection coefficient of the ring antenna.  
As the rectangular ground slot length (L) increases from 30 mm to 32 mm (in 1 mm increments), the resonant frequency of the slot antenna decreases from 3.54 GHz to 3.45 GHz. When L = 31 mm, the center frequency is around 3.5 GHz and fully covers the LTE band 42 frequency range. Therefore, the optimized ground slot length is determined to be 31 mm.
# Combining Dilution of Precision and Kalman Filtering for UWB Positioning in a Narrow Space
## 3 Experiments and Results
### Experimental Protocol
#### 3.1.3 Experimental Scheme  
To systematically explore the DOP distribution characteristics under different narrow space scenes and base station networking and to verify the proposed positioning algorithm, this paper designed the following experimental scheme.  
Based on the equations of the DOP numerical analysis model, the model simulation was carried out to explore the geometric configuration relationship between the node to be tested and the base station in the narrow space. Moreover, the DOP values in the wide space were also compared with the narrow space.  
Figure 4: Hardware architecture of the positioning module.  
Figure 3: Experimental scene: (**a**) corridor; (**b**) connection of corridors.  
Figure 5: DOP values in wide simulation space: **(a)** HDOP values; **(b)** PDOP values.  
Figure 6: DOP values in corridor simulation space: **(a)** HDOP values; **(b)** PDOP values.  
Figure 7: DOP values in passage simulation space: **(a)** HDOP values; **(b)** PDOP values.
## 2 Signal Model
At a medium signal-to-noise ratio (SNR), the ICI is very small if compared to the AWGN. Since DVB-C2 4096-QAM needs to have at least a 40-dB SNR [11; 12], we cannot ignore the ICI term in our analysis. For typical small values of \(\varepsilon\) and \(\xi\), \(\alpha(k)\approx 1\) and \(\sigma_{C}^{2}\approx E_{X}\sigma_{H}^{2}\varepsilon^{2}\pi^{2}/3\)[16].
## 1 Introduction
The mathematical model contributes significantly to several vital aspects. It facilitates more efficient planning and scheduling of hauling operations, allowing for dynamic adjustments based on changing conditions. Additionally, it enables a comparative analysis between costs and completion times, optimizing economic and productivity decisions. By reducing downtime and improving resource utilization, the model effectively addresses the logistical and financial challenges in mining operations and adapts to various operational scenarios, increasing its applicability and relevance in the sector. The remainder of this paper is structured as follows: Section 2 presents the related work. Section 3 details the methodology. Computational experiments and results analysis are discussed in Section 4. Finally, conclusions drawn from the study are presented in Section 5.
## 3 Results  
### Accuracy of the Paramo Land Use Classification Algorithm and Future Land Use Prediction Model  
Six models (Table 3) with different sets of variables were developed from Multilayer Perception (MLP); the accuracy of each model was different in the determination of coverages. Model 1, selected because its accuracy was the highest, has all the variables which indicates that each variable makes a different contribution to the model fit. The accuracy of model 1 was increased by entering the variables in the following order: NDVI, DEM, VARI, GSOC, BSI, and NDMI. The hierarchical ranking of the variables could be associated with their relevance in the categorization and performance of the model. The Normalized Difference Vegetation Index (NDVI) was essential to differentiate vegetation from other land cover types and to determine its general condition, the altitude (DEM) allowed to recognize the influence of altitudinal floors on the development of land covers in the paramo, the Visible Green Atmosphere Resistant Vegetation Index (VARI) contributed to the quantitative estimation of the fraction of vegetation covers. Through the Bare Soil Index (BSI) data, it was possible to determine the areas of bare soil in places with scarce vegetation and reduce the incidence of humidity, the Normalized Difference Moisture Index (NDMI) made it possible to detect the conductivity of the land cover, and the soil organic carbon (SOC) variable was essential in the study of the correlation between carbon and the different classes of land use at different altitudes. Table 4 shows the land use accuracy data for the years 2000, 2010, and 2020, while Table 5 shows the validation of the future prediction model for the analyzed land covers. \begin{table}
\begin{tabular}{c c c c c} \hline  & **Overall** & **Overall** & **Kappa** & **Kappa** \\  & **Accuracy (L)\%** & **Accuracy (V)\%** & **Coefficient (L)\%** & **Coefficient (V)\%** \\ \hline
2000 & 81 & 79 & 81 & 79 \\ \hline
2010 & 84 & 80 & 85 & 80 \\ \hline
2020 & 85 & 82 & 84 & 81 \\ \hline \end{tabular}
\end{table} 
Table 4: Overall accuracy and Kappa coefficient. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Model** & **Variables** & **Accuracy (\%)** \\ \hline
1 & NDVI, DEM, VARI, SOC, BSI, and NDMI. & 81 \\ \hline
2 & NDVI, VARI, SOC, BSI, and NDMI. & 71 \\ \hline
3 & NDVI, DEM, VARI, and SOC. & 65 \\ \hline
4 & NDVI, DEM, and VARI. & 58 \\ \hline
5 & NDVI and DEM. & 50 \\ \hline
6 & NDVI. & 49 \\ \hline \end{tabular}
\end{table} 
Table 3: Accuracy of models.
## 3 Modified Current Phase Angle Calculation  
A modified current phase angle calculation can avert an improper current phase angle caused by a fixed \(\Delta\) because it is performed without the assumption of the fixed \(\Delta\). The principle of the modified current phase angle calculation is explained using the phasor diagram in Figure 1. Before the modified current phase angle calculation is applied, the total fault current amplitude at point F is higher than the grid current amplitude. When the current phase angle of IBDGs is tuned to the summation of the phase angle of grid current (\(\theta_{S}\)) and the \(\beta\) angle, the total fault current amplitude is reduced to the grid current amplitude at point D. The modified current phase angle can be expressed as follows:  
\[\theta_{IBDG}=\theta_{S}+\beta, \tag{6}\]  
\[\beta=cos^{-1}\left[\frac{l_{S}^{2}-y^{2}-z^{2}}{2 yz}\right]-sin^{-1}\left[ \frac{l_{S}}{y}\cdot sin(\delta)\right]+\delta, \tag{7}\]  
where \(y\) is the summation of the grid current and fault current from the SDG, \(z\) is the result of the summation of all currents fed by IBDGs, and \(\delta\) is the angular difference between the grid current and fault current from the SDG.  
Initially, the modified current phase angle calculation is examined based on symmetrical fault. When the fault is detected, the current amplitude is set to the constant that is the maximum allowable current level of the inverter, and the calculation of the current phase angle reference is based on  
Figure 1: Phasor diagram of the modified current phase angle calculation.  
Equation (6). The current phase angle difference between three phases is 120\({}^{\circ}\). This adjustment is called the conventional current control.  
Because the \(V_{dc}\) is controlled via an active current (\(l_{d}\)) proportional to the active power, the calculation of the current reference has to take \(V_{dc}\) into account. However, for conventional current control, the three-phase current references rely on Equation (6) due to constant current amplitude. Since Equation (6) is not a function of \(V_{dc}\), the conventional current control cannot regulate \(V_{dc}\). In reality, the excessive \(V_{dc}\) possibly occurs during a fault. It causes an IBDG disconnection from the grid. The IBDGs fail to remove the effects of fault current contribution from the SDG. Consequently, the coordination then no longer exists in the recloser-fuse.
# Motion Target Localization Method for Step Vibration Signals Based on Deep Learning
## 4 Experimental Results and Analysis
### Ablation Study  
To validate the effectiveness of the components in the network model, this study conducted ablation experiments by respectively removing the CAM, 1D-ResNet, and Bi-LSTM modules to train different models. In order to comprehensively assess the performance of the models, this study also supplemented the evaluation metric of multiplications and additions to measure the computational complexity and efficiency of the models. As shown in Table 3, compared to the baseline ResNet18 model, although ResNet50 slightly improved system performance by increasing the number of convolutional layers, the computational complexity significantly increased, and the localization error was still higher than in other models. This indicates that relying solely on convolutional networks makes it difficult to extract rich features from vibration signals. The proposed model in this paper achieved superior positioning performance on the FVS dataset compared to Cam + ResNet18 and ResNet18 + Bi-LSTM. The positioning error RMSE was only 0.441 m, and the RTE value was 1.3%. When CAM was removed, with other modules unchanged, the positioning error of ResNet18 + BiLSTM increased by 0.098 compared to the proposed model, and the RTE increased by 0.9%. This was mainly because the spatial correlation between different sensor data was not combined at the same time, resulting in a decrease in positioning performance. When the Bi-LSTM module was removed, the positioning error and RTE of CAM + ResNet18 increased by 0.169 and 1.2%, respectively. The reason was that the temporal correlation of data in the time dimension was not considered when extracting features from the signal. Compared to the basic ResNet18 network, the positioning error of ResNet18 + BiLSTM was reduced by 0.222 m, and the RTE was reduced by 1.078%. The positioning error and RTE of CAM + ResNet18 were reduced by 0.111 m and 0.778%,  
Figure 11: Test set experimental results.  
Figure 10: Part of the L-path test results. (**a**) The test set results of experimenter A. (**b**) The test set results of experimenter B. (**c**) The test set results of experimenter C.  
respectively. This result demonstrates that by introducing CAM and Bi-LSTM, the model can simultaneously consider the spatial correlation between different sensor data and the temporal correlation of single sensor data in the time dimension, thereby capturing more crucial information and achieving more precise ground personnel trajectory prediction.