esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions
### Discussions
#### 4.2.1 Accuracy in Proximity Detection  
The implementation of the Haversine formula provided precise proximity detection, as evidenced by average response times of 131 ms. This efficiency validates the effectiveness of our approach in delivering real-time promotions. [PERSON] et al. [5] demonstrated that effective location-tracking algorithms can significantly enhance proximity detection in mobile applications by reducing data redundancy and improving system responsiveness. Our findings align with these results, underscoring the importance of accurate localization algorithms.
# Spatio-Temporal Changes in Water Use Efficiency and Its Driving Factors in Central Asia (2001-2021)
## 5 Conclusions  
This study calculated the WUE of Central Asia for 2001-2021 using the GEE platform based on the MODIS products GPP and ET. The driving mechanisms of atmospheric, hydrological, and biological factors on the WUE of Central Asia were analyzed using a geographic detector. The results show that the mean value of WUE is 3.011 gCkg\({}^{-1}\)H\({}_{2}\)O, with a weak inter-annual variation and an overall decreasing trend of 0.0198 gCkg\({}^{-1}\)H\({}_{2}\)O per year on average, and a clear inverted \"V\" shape of intra-annual variation. The spatial distribution of WUE varies significantly, with significant decreases in WUE on the southern edge of Turkmenistan, oases in eastern Uzbekistan, southwestern Tajikistan, and oases around the Gurubnungut and Taklamakan deserts, and significant increases in WUE on the Ustyurt Plateau, around the Aral Sea to the Lake Balkhash Basin, in the Iii River Basin, and in central and western Tajikistan. The atmospheric factors LST, DEF, VPD, RN, and TEM, and the hydrological factors PRE and VSW1 together explain more than 45% of the WUE in Central Asia, with LST being the strongest driver of WUE. The interaction of atmospheric factors and vegetation factors is the greatest driver of changes in WUE in Central Asia, and vegetation factors are important in both bilinearly enhanced and non-linearly enhanced types of interactions. In Central Asia, vegetation cover is inversely proportional to WUE, and FVC is the best performing vegetation index among the vegetation factors. This study also found that VAP, AOD, and GWS can enhance the drive of vegetation factors on WUE in Central Asia under the interaction of drivers. Therefore, we recommend that more attention be paid to climate change in Central Asia in the future, and advise against blind greening projects that could lead to a rapid depletion of available water resources in Central Asia.  
Conceptualization, S.Q. and J.D.; methodology, X.G.; software, X.G.; validation, J.W., R.W. and J.Z.; formal analysis, J.T.; investigation, L.H.; resources, J.D.; data curation, R.W.; writing--original draft preparation, S.Q.; writing--review and editing, X.G.; visualization, S.Q.; supervision, J.D.; project administration, J.W.; funding acquisition, J.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 42171269), the Xinjiang Academician Workstation Cooperative Research Project (No. 2020.B-001), and the Social Science Foundation of Xinjiang Autonomous Region (No. 19 BJL030).  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
We are sincerely grateful to the reviewers and editors for their constructive comments towards the improvement of the manuscript. We are sincerely grateful to [PERSON] for his networking support.  
The authors declare no conflict of interest.
# A DFTB-Based Molecular Dynamics Investigation of an Explicitly Solvated Anatase Nanoparticle
## 2 Materials and Methods  
The details of the simulation are, thus, as follows: SCC DFTB-MD was performed using the matsci 0.3 parameter set [10] and the DFTB+ software, version 20.1 [11]. The system consisted of an anatase nanoparticle in an orthogonal unit cell, shpwn in Figure 1; the number of particles and cell parameters are outlined in Table 1 (the number of TiO2 units in the nanoparticle is non-stoichiometric; there are 163 Ti and 324 O atoms). The simulation cell was chosen so that there would be at least 20 A between periodic images of the nanoparticle--cf. Figure 1. This spacing leads to a very high virtual concentration of nanoparticles, but keeps separation between slabs, as usually employed in DFT simulations. The nanoparticle itself was constructed using a simulation cell from the Materials Project [12] and the Wulffpack Python library [13]. The ELPA solver from the ELSI library was used for electronic structure solvation [14]. The Extended Lagrangian Born-Oppeheimer Molecular Dynamics (XLBOMD) scheme, as implemented in DFTB+, was used to speed up the SCC convergence of the system [15].  
Figure 1: The anatase nanoparticle surrounded by water molecules. Oxygen atoms are red, titanium is pink and hydrogen is white.  
The system was equilibrated at a temperature of 300 K for 1000 steps of 1.0 fs in the NVT ensemble using non-SCC DFTB-MD, followed by a data gathering simulation of 2048 steps of 1.0 fs in the NVE ensemble using SCC DFTB-MD. However, it is possible that the equilibration was not sufficient, as the system temperature quickly rose to approximately 650 K. Increased system temperature is also a symptom of poor SCC convergence, which could be attributed to the approximations introduced by the XLBOMD algorithm. Regardless, the total system energy was checked and found to be conserved to within 0.03% throughout the trajectory, validating the NVE ensemble and indicating that the increase in temperature is not due to energy leakage from poorly convergent SCC cycles.  
For comparative purposes, smaller planar systems of anatase (101) and rutile (110) interfaces were also simulated using SCC DFTB-MD, employing the same parameter set, simulation settings, and software. The number of particles and cell parameters for these systems are outlined in Table 1. The scale of these systems was chosen to be suitable for calculation using periodic DFT, and thus provide a benchmark for direct comparison between DFT and DFTB, which is discussed by the authors in an upcoming publication. The anatase and rutile unit cells were again sourced from Materials Project, and the surface slabs were constructed using the ASE Python library [16]. These simulations were conducted for 5 ps in order to gather a meaningful number of reference data, and because far fewer computational resources were required for these simulations.
## 3 Results and Discussion
### Correlation Analysis  
In this study, a correlation analysis was carried out between the total polyphenols content, total flavonoids content, total phenolic acids content, total carotenoids content, and ascorbic acid content of organic rosehips flesh and seeds (Table 4). The results of correlations indicated a very strong positive relationship between total polyphenols content and total phenolic acids content in rosehip flesh and seeds (r = 0.96 and r = 0.98, respectively). Negative correlations were observed between total polyphenols content, total carotenoids content, and ascorbic acid content. In addition, total carotenoids content significantly negatively correlated with total phenolic acids content in the flesh and seed samples (r= -0.86 and r = -0.91, respectively).
# English Speech Emotion Classification Based on Multi-Objective Differential Evolution
## 3 Materials and Method
### Pre-Process  
Pre-emphasis, framing, and windowing are important pre-process steps often applied to raw speech audio data. These steps ensure that speech data are cleaned, transformed, and organized in a way that allows machine learning algorithms to effectively learn and recognize emotional patterns.  
Pre-emphasis is a filtering technique that is applied to raw speech signals before further analysis. It accentuates high frequencies in signals and improves the signal-to-noise ratio. Speech signals tend to have more energy in low frequencies, and pre-emphasis can balance this by boosting high-frequency components.  
Framing involves dividing continuous speech signals into shorter overlapping segments (frames). The reason for this is that speech characteristics, such as pitch and spectral content, can change rapidly in short time intervals. By analyzing these frames individually, we capture variations more accurately. Each frame typically contains about 20-30 ms of speech data.  
After framing, a windowing function is applied to each frame. Windowing reduces sudden changes at the edges of frames and prevents artifacts during the subsequent analysis, such as the Fourier transform. Common windowing functions include the Hamming, Hanning, and Blackman windows. These functions smoothly taper signals within frames, and decrease the effects of spectral leakage.
## 6 Conclusions  
Semiconductor optical amplifier-based fiber laser sources provide potential solutions for current optical technology because they are very compact, cost effective and user-friendly compared to their counterparts that utilize rare-earth ytterbium doped fiber amplifiers (YDFAs), erbium doped fiber amplifiers (EDFAs), stimulated Raman scattering (SRS) amplifiers or stimulated Brillouin scattering (SBS) amplifiers as well as free-space solid state laser sources.  
In addition to using semiconductor optical amplifiers, the proposed compound-ring cavity does not contain any optical isolators or optical isolators, which can lead to the development of a simple and very high compact (i.e., on-chip scale) power scalable, adjustable and switchable laser source.  
We successfully proposed and experimentally demonstrated very high coherent beam combining efficiency of two SOAs used as gain media in all-single-mode fiber compound-ring cavities with two switchable and power adjustable output couplers formed by using two Sagnac loop mirrors. The use of Sagnac loop mirrors eliminates the need of utilizing variable optical attenuators in order to adjust and control the laser output power. Two Sagnac loop mirrors have been used to switch the mode of operation of the proposed fiber laser from single output port to dual-output port operation as well as to adjust the output power on each output coupler. We have achieved more than 98% coherent beam combining efficiency of two parallel nested fiber ring resonators (i.e., compound-ring cavity) over the C-band tuning range of 30 nm. Optical signal-to -noise ratio (OSNR) of +45 dB, and optical power fluctuation of less than \(\pm\)0.02 dB were measured over the course of three hours at room temperature.  
Finally, we discuss on how N \(\times\) N fused fiber couplers can be used to achieve all-single-mode-fiber compound-ring laser structure with (N >> 1) number of SOAs that can be used for power scaling to achieve a high power laser source that simply uses low power optical components. Due to the advanced technologies of semiconductor optical amplifiers and tunable filters covering wide range of wavelength spectrum in different electromagnetic spectrum bands, the proposed fiber compound-ring laser can be used to build compact laser systems covering different optical wavelength-bands.  
This research was support by Graduate Research Technology Initiative (GRTI) which is a CUNY internal grant.  
**Author Contributions:** [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] contributed reagents/materials/analysis tools; [PERSON], [PERSON] and [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 11: (N >> 1) number of SOA-based dual-output port fiber compound-ring laser structures.
# Ramjet Nozzle Analysis for Transport Aircraft Configuration for Sustained Hypersonic Flight
## 4 CFD Analysis of Ramjet Nozzles
### CFD Setup
#### 4.1.3 Physical Model  
The Density-Based Numerical Solver (DBNS) was selected as supersonic conditions at the nozzle exit lead to changes in density as it passes through expansion fans and shocks [15]. The solver option is suitable for sharp shock structures, which were analysed later in the study. The \"K-Omega\" turbulence model, a two-equation model, was selected as this model is suitable for transonic/supersonic flow and aerodynamic operations with accurate solving capabilities. The working fluid selected was ideal gas.
## 2 Proposed Action Recognition Method
### Custinized Convolutional LSTM Model  
We used the LSTM model for action classification in aerial videos. The LSTM model is a type of RNN that can effectively encapsulate the dependencies of the sequential data. In the proposed approach, we first extract the temporal features from the aerial videos using the YOLOv8 pose extractor and then use the LSTM model to classify the actions based on these features. The tensor P in Equation (1) represents the spatiotemporal features of the person over time.  
The LSTM model contains a memory cell and three gates, including an input gate, output gate, and forget gate [30; 31; 32], defined as follows:  
_Input gate:_  
\[i_{t}=\sigma(W_{i}x_{t}+U_{i}h_{t-1}+b_{i}) \tag{2}\]  
_Forget gate:_  
\[f_{t}=\sigma\Big{(}W_{f}x_{t}+U_{f}h_{t-1}+b_{f}\Big{)} \tag{3}\]  
Figure 2: Results of Yolov8 pose estimator on Drone Action dataset [22]: stabbing (**top left**), hitting stick (**top right**), waving hands (**bottom left**), and clapping (**bottom right**).  
Output gate: \[o_{t}=\sigma(W_{o}x_{t}+U_{o}h_{t-1}+b_{o})\] (4) Memory cell: \[C_{t}=f_{t}.c_{t-1}+i_{t}.\text{tanh}\left(W_{c}x_{t}+U_{c}h_{t-1}+b_{c}\right)\] (5) Output: \[h_{t}=o_{t}.\tanh(c_{t})\] (6)  
where \(x_{t}\)\(h_{t}\), and \(c_{t}\) denote the input, the output, and the cell state \(t\), respectively. \(i_{t}\), \(f_{t}\), and \(o_{t}\) are the input, the forget, and the output gates, respectively. \(W_{i}\), \(W_{f}\), \(W_{o}\), and \(W_{c}\) refer to the collection of weight matrices used to transform the input data at each time step, whereas \(U_{i}\), \(U_{f}\), \(U_{o}\), and \(U_{c}\) are the weight matrices to transform the hidden state from the previous time step. \(b_{i}\), \(b_{f}\), \(b_{o}\), and \(b_{f}\) represent the bias terms.  
The output H is a sequence of hidden states that captures the temporal dependencies in the spatiotemporal features. We can then use the final hidden state of the LSTM as input to a fully connected layer with softmax activation to obtain the probability distribution across the different action classes:  
\[P=Softmax(W_{h}H+b) \tag{7}\]  
We designed the custom sequential LSTM model by stacking three ConvLSTM 1D layers, such that each layer is followed by a batch normalization layer, with decreasing filter sizes of 128, 32, and 16, respectively. We added a dropout layer after the third ConvLSTM1D layer to prevent overfitting. Next, we flattened the output and added two fully connected layers with ReLU activation and a dropout layer after each. Finally, we added a dense output layer with the softmax activation function. The LSTM model applied in this research is convolutional LSTM (Figure 3), which combines convolutional layers with LSTM to model spatiotemporal data.
## 3 Results and Discussion  
### WPG and WLR Analysis  
The results of the WPG and WLR are presented in Figure 3. The WPGs were positively correlated with the MA concentration in four treatments (Figure 3a). In general, the WPG of the samples in ZC-103 was higher than that of the other three groups at the same MA concentration. The additionof ZnCl\({}_{2}\) in ZC-120 also increased the WPG. The results proved that the addition of the hardener ZnCl\({}_{2}\) increased the content of the DM/MA system modifier in the interior of the wood. This may have occurred because ZnCl\({}_{2}\) increased the permeability of the cell wall and wood samples were more conducive to immersion under pressure. It is also possible that ZnCl\({}_{2}\) acted as a modifier for cross-linking or grafting with the cell wall material to promote condensation of the molecules in the cell cavity to form macromolecules, which were immobilized in the wood. The WPGs of group C were the lowest, which may have been attributable to the high evaporation rate of the modifier at the high temperature of 120 \({}^{\circ}\)C, which resulted in a low fixation rate of the modifier.  
As shown in Figure 3b, the addition of 5% MA resulted in a reduction in the WLR of the samples compared to 0% MA of 3.87%, 14.99%, 8.42%, and 7.64% in the 103, ZC-103, 120, and ZC-120 groups, respectively: as the MA concentration increased, the WLR increased. The order of the WLR was 103 \(>\) ZC-103 \(>\) ZC-120 \(>\) 120. The curing efficiency of 120 was the highest, and the WLR was in the range of 17.18% to 28.40%. The reaction efficiency between DM/MA and the cell wall was better at 120 \({}^{\circ}\)C and remained permanently in cell cavities. In contrast, ZC-103 had a higher WPG, but the WLR was not satisfactory, and the WLR increased from 36.45% to 47.78%. This occurred because the fixation of the chemicals was limited at 103 \({}^{\circ}\)C and ZnCl\({}_{2}\) promoted the permeability of the wood in the capillaries, which resulted in the accumulation of chemicals that were encapsulated in macromolecules in the cell cavity after being washed with water. The standard deviation (STDVA) of the WPGs was in the range of 2.52% to 6.80%, indicating that the impregnation of the chemicals in the 20 replicate specimens was relatively uniform. The STDVA of the WLR was in the range of 1.03%-4.48%, demonstrating that the samples were saturated after immersion in water and that the differences between the groups were small.  
In summary, the effective fixation rate of ZC-103 was the highest, and in the treatment of 10% DM + 10% MA, the WPG of ZC-103 increased by 15.33%, 20.69%, and 13.23% more than in 103, 120, and ZC-120, respectively. During the leaching test, 120 had the highest WLR, which was reduced by 27.08%, 19.25%, and 9.14% compared to the 103, ZC-103, and ZC-120 treatments, respectively.
# A Lightweight Detection Method for Meretirix Based on an Improved YOLOv8 Algorithm
## 2 Materials and Methods  
### Dataset Preparation  
The images of clams were captured using a 12-megapixel camera. The data collection scenarios cover four typical distribution patterns: single target with dispersed placement, single target with clustered placement, multiple targets with dispersed placement, and multiple targets with clustered placement (Figure 1). This setup is designed to simulate the actual working environment of sorting machine systems, focusing primarily on challenges such as occlusion caused by stacked targets, interference from impure shellfish, and object detection under varying densities. A total of 225 images were collected for each scenario, resulting in an initial dataset of 900 images. All images were saved in the \"jpg\" format, and each batch of samples was randomly arranged to ensure data diversity and representativeness.  
To enhance the model's performance and generalization ability, data augmentation techniques were employed to expand the sample set and mitigate the risk of overfitting caused by insufficient training data. In this study, random pixel dropout, image sharpening, affine transformation, brightness adjustment, hue adjustment, and horizontal flipping were randomly combined as data augmentation methods [13] to enrich the dataset. Using these techniques, five new augmented images were generated from each original image, with some examples shown in Figure 2. As a result, a total of 3000 image samples were produced through data augmentation. The dataset was then randomly divided into training, validation, and test sets in a ratio of 8:1:1.
# An Energy-Efficient Routing Protocol for Reliable Data Transmission in Wireless Body Area Networks
## 4 Energy-Efficient Routing Protocol for Reliable Data Transmission in WBAN
### Routing Process
#### 4.2.1 Network Initialization  
In this phase of the proposed protocol, the sink node broadcasts a Hello message to the whole network, which includes the location of the sink node. After receiving the message, all the nodes update the location of the sink immediately, and then reply with a Hello message to the network. The message includes the nodes' ID, location, residual energy, transmission efficiency, available bandwidth, hops to sink and so on. At this time, all nodes know the specific location of the sink and its neighbors. After the initialization phase is completed, each node will establish a neighbor nodes information table \(NT\) (Neighbors Table), and the best next hop will be generated in \(NT\).
## 3 Results
### Percent Fertilizer Loss as NH\({}_{3}\)-N  
The results in units of percent fertilizer loss as NH\({}_{3}\)-N are like those presented in units of kg/ha and confirm that over the 24 h period following application, the conventional urea-N fertilizer treatment lost significantly more of the applied fertilizer N than the two alternative sources (Table 2). The significant interaction of fertilizer treatment and experiment run was again identified, and the simple effect determined to be most responsible for it was the conventional urea treatment (Figure 2b). However, on a percent conventional urea loss as NH\({}_{3}\)-N basis, both experiment runs featuring a high N rate only significantly differed from the 2020 low N rate experiment run (Exp. 4). Perhaps not coincidentally,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Source (All Fixed Vars)** & **Degrees of Freedom** & \
\begin{tabular}{c} **Fertilizer N** \\ **Volatilized** \\ \end{tabular} &
\begin{tabular}{c} **Percent Fertilizer** \\ **Loss as NH\({}_{3}\)-N** \\ \end{tabular} \\ \hline \hline  & num & den & & \(p\)-value \\ Experiment & 3 & 12 & ns & ns \\ Treatment (TRT) & 2 & 48 & \textless{0.0001} & \textless{0.0001} \\ TRT \(\times\) Experiment & 6 & 48 & 0.0016 & 0.0386 \\ TRT contrasts & & & & \\ Urea +UF RXN products vs. Urea +DCD +NBPT & 1 & 48 & ns & ns \\ Urea vs. other two TRTs & 1 & 48 & \textless{0.0001} & \textless{0.0001} \\ TRT levels & \(n\) & & TRT Means, kg/ha & TRT Means, \% \\ Urea & 24 & & 0.521 & 5.87 \\ Urea +UF RXN products & 24 & & 0.079 & 0.93 \\ Urea +DCD +NBPT & 24 & & 0.103 & 1.24 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Analysis of variance (ANOVA) of 0- to 24 h fertilizer N volatilized (kg/ha) and percent fertilizer loss as NH\({}_{3}\)-N (%), by source.  
the 24 h mean air temperature observed during Experiment 4 was the lowest of the four. In accordance with the literature [8,13], pooled mean fertilizer loss as NH\({}_{3}\)-N was highly correlated with mean temperature observed over the respective experimental periods (\(n=4\), \(r=0.81\), \(p=0.189\)).  
Studies describing creeping bentgrass putting green recovery of folily applied N report statistically less recovery over rising urea-N rates [5,29]. However, field studies reporting flux-chamber measures of NH\({}_{3}\) emission from at least two rates of folily applied urea-N conclude that loss as a percentage of urea-N applied was not significantly influenced by fertilization rate [30,31]. A similar response was observed here. Relative to experiment runs 3 and 4, the higher rate by conventional urea-N treatment consistently increased  
Figure 2: Comparisons of fertilizer N volatilized in mass per unit area (**a**) or percent fertilizer loss as NH\({}_{3}\)-N (**b**) by fertilizer source and experiment. Means overlapping error bar are not significantly different from conventional urea at a 5% alpha level.  
fertilizer N volatilized in mass per area units (Figure 2a), but only half the time when analyzed as a percentage of the fertilizer N applied (Figure 2b).