esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Genetic and Ontogenetic Factors
### Impact of Phenological Stage  
The composition of essential oil in thyme undergoes significant changes based on the plant's growth stage, with higher yields and compositions observed during the blooming stage compared to the pre-flowering stage [90; 91; 92]. In _Thymus armeniacus_, Carvacrol exhibits a significantly higher amount at 100% of the flowering phase compared to 50%, with linalool, borneol, thymol, and \(\beta\)-carophyllene showing a similar trend [90]. During the flowering stage, essential oils in _Thymus numidicus_ are rich in oxygenated monoterpenes (82.11% compared to 67.2% in the pre-flowering stage), such as thymol and p-cimene-7-ol, known for their antioxidant and antimicrobial properties [93].  
The chemical composition of essential oil also varies depending on the year of harvesting and the phenological stage of the plants; thymol and carvacrol were the main compounds in the essential oil of _T. vulgaris_ studied by [PERSON] et al. [94], but their amounts varied depending on the year of harvesting and the flowering stage. Antimicrobial activity, correlated to the amount of compounds detected in the essential oil, was found to be higher during the seed set stage in particular for _Thymus fedstchenkoi_ [PERSON] [91].  
Several other studies have investigated the variability of compounds along the flowering stage. In _Thymus \(\times\) citridorus_, the highest percentage of terpinolene was observed during the flowering stage [95]. In _Thymus piperella_, the concentration of carvacrol peaks at the beginning of the flowering stage, a trend similarly observed in \(\gamma\)-terpinene but with a slightly lower rate, while the yield of p-cymene decreases during the flowering stage [96]. In Kermanian thyme (_Thymus caramanicus_ [PERSON]), thymol was the main compound of the oil in all phenological stages, with the highest essence and thymol yields obtained during the fruit set stage [97].  
In summary, thyme essential oil composition varies significantly across growth stages, with the flowering stage being particularly influential. Key compounds like carvacrol, terpinolene, and thymol show significant fluctuations as discussed. These findings underline the importance of considering phenological stages during the harvesting to optimize thyme oil quality for diverse applications.
###### Abstract  
Exopolysaccharides (EPSs) produced by _Bacillus_ species have recently emerged as promising commercial antioxidants in various industries, such as pharmaceuticals and biomedicine. However, little is known about EPS production and function from _Bacillus velexensis_ so far. In the present study, the effect of sugar sources on EPS production by _B. velexensis_ VTX20 and the genetic biosynthesis, characteristics, and antioxidant activity of the resulting EPS were evaluated. The strain VTX20 produced the maximum EPS yield of \(75.5\pm 4.8\) g/L from an initial 200 g/L of sucrose after a 48-h cultivation. Through genomic analysis, _ls-lek_0 green was found, for the first time, to be responsible for the levan-type EPS production in _B. velexensis_. Biochemical and structural characterization further confirmed the majority of levan, followed by an extremely low level of dextran biopolymer. The water solubility index and water holding capacity of the EPSs were \(81.9\pm 3.4\%\) and \(100.2\pm 3.4\%\), respectively. In vitro antioxidant activity analyses showed strong scavenging activity for 1,1-diphenyl-2-picrylhydrazyl and hydroxyl radical values of 40.1-64.0% and 16.0-40%, respectively. These findings shed light on the EPS biosynthesis of _B. velexensis_ at both structural and genetic levels and the potential application of EPS as a natural antioxidant for pharmaceutical and biomedical industries.  
Authorization activity; _Bacillus velexensis_; exopolysaccharides (EPSs); levan
# An Adaptive Symmetrical Load Balancing Scheme for Next Generation Wireless Networks
## 5 Results
### Delay  
Figure 7 displays the delays of the frames for five different schemes, including the proposed ASLB. The frame delay represents the average time taken from when the WDs contend for the channel until the AP successfully receives the frames. As the number of WDs increases, the frame delay also increases. This implies that there is a close relationship between the frame delay and the throughput. Lower throughput will result in a larger delay for the same packet generation rate. In comparison to CMAS, DASA, MPD and RSSI schemes, ASLB reduces the delay by 3%, 4%, 5% and 10%, respectively.
# On Modeling Eavesdropping Attacks in Underwater Acoustic Sensor Networks +
## 5 Empirical Results
### Eavesdropping Probability with an Array Eavesdropper  
We then conduct another group of simulations with consideration of an array eavesdropper in IUSNs and AUSNs, respectively.
# Intelligent Detection and Segmentation of Space-Borne SAR Radio Frequency Interference
## 4 Methodology
### Interference-Targeted Quick-Detection Network
The channel attention and spatial attention modules of the mentioned LCBAM module can be expressed by:  
\[M_{\mathrm{C}}(x)=\sigma(f_{1D}^{n}(\mathrm{AvgPool}(x))+f_{1D}^{n}(\mathrm{ MaxPool}(x))), \tag{5}\]  
\[M_{\mathrm{S}}(x)=\sigma\Big{(}f_{\mathrm{dilation}}^{3\times 3}\left([\mathrm{ AvgPool}(x);\mathrm{MaxPool}(x)]\right)\Big{)}, \tag{6}\]  
where \(M_{\mathrm{c}}(x)\) is the output of the channel attention module, \(M_{\mathrm{s}}(x)\) is the output of the spatial attention module, \(\sigma(\cdot)\) is the activation function, and \(f_{1D}^{n}(\cdot)\) and \(f_{dilation}^{3\times 3}(\cdot)\) denote a one-dimensional convolution operation and dilated convolution, respectively, with a kernel size of \(3\times 3\). The ghost module was proposed by [PERSON] et al. in 2020 [52]. It is mentioned in the article that although MobileNet or ShuffleNet were proposed to use depth-wise or shuffle operations, the introduced \(1\times 1\) convolution would still generate a certain amount of calculation, but the authors found when analyzing the output feature map that some feature maps were actually similar. So it is considered that similar feature maps can be obtained by simple transformation: that is, linear transformation. In the ghost module, for a certain feature layer, only a part of the real feature layer (real feature layer) is generated by the convolution operation, and the remaining feature layer (ghost feature layer) is obtained by performing a linear operation on the real feature layer; then, the real feature layer and ghost feature layer are spliced together to form a complete feature layer, as shown in Figure 10:  
The compression ratio of the computational cost of the ghost module is proportional to the number of feature maps participating in the linear transformation. The IQDN is trained on an NVIDIA GeForce RTX 3060 GPU with 12 GB memory using PyTorch [53]. Guided by the Adam solver, IQDN optimizes the parameters. The batch size is 64, the learning rate is 0.01 and descends with a step-wise strategy, and the momentum is 0.9.
# A Two-Echelon Electric Vehicle Routing Problem with Time Windows and Battery Swapping Stations
## 4 Algorithms for the 2E-EVRPTW-BSS
### Solution Evaluation  
A solution is evaluated by Formula (29):  
\[f_{eva}(S)=f(S)+\alpha p_{cap}(S)+\beta p_{tw}(S)+\gamma p_{bat}(S) \tag{29}\]  
Figure 2: Representation example of a solution of the 2E-EVRPTW-BSS.  
where \(f(S)\) is the total cost as represented by Formula (6), \(p_{\text{cap}}(S)\) shows the violation of total capacity defined by (30), \(p_{\text{tw}}(S)\) shows the time window violation at customers as indicated in (31), \(p_{\text{bat}}(S)\) measures the battery capacity violation defined by (33), and \(\alpha,\beta,\gamma\) are penalty factors of the violations. The penalty factors are large enough values, which can effectively eliminate the possibility of the solution containing time window violation being the optimal solution.  
The total capacity violation of a solution \(S\) can be calculated by  
\[p_{cap}(S)=\sum_{k=1}^{K_{2}}\max(\sum_{i\in\mathbb{C}(r_{k}^{2})}q_{i}-Q_{2},0) \tag{30}\]  
There are three cases leading to the violation of time windows, i.e., arrival time \(t_{s}^{1}>t_{s}\), \(t_{i}^{2}<e_{i}\) and \(t_{i}^{2}>l_{i}\) (\(l\in V_{C}\)). Hence, the total time window violation of a solution can be calculated by  
\[p_{\text{wt}}(S)=\sum_{k=1}^{K_{1}}\sum_{s\in S(r_{k}^{1})}\max(t_{s}^{1}-t_{ s},0)+\sum_{k=1}^{K_{2}}\sum_{i\in\mathbb{C}(r_{k}^{2})}\max(t_{i}^{2}-l_{i},0 )+\sum_{k=1}^{K_{2}}\sum_{i\in\mathbb{C}(r_{k}^{2})}\max(e_{i}-t_{i}^{2},0) \tag{31}\]  
To calculate battery capacity violations, for each route \(r_{k}^{2}\in S_{2},k=1,2,...,K_{2}\), we define the variable \(\Phi_{v_{i}}^{k}\), \(k=1,2,\ldots,K_{2}\) to represent the remaining battery power for the EV \(k\) when leaving node \(v_{i}\).  
\[\Phi_{v_{i}}^{k}=\left\{\begin{array}{cl}B&v_{i-1}\in B(r_{k}^{2})\cup\{v_{ 0}^{2}\}\\ \Phi_{v_{i-1}}^{k}-a\cdot t_{v_{i-1}v_{i}}&\text{otherwise}\end{array}\right. i=1,\ldots,m\ \ ;k=1,...,K_{2} \tag{32}\]  
Obviously, \(\Phi_{v_{i}}^{k}\) should be non-negative. The battery capacity violation of a route \(r_{k}^{2}\) can thus be defined as  
\[p_{bat}(S)=\sum_{k=0}^{K_{2}}\sum_{i\in\mathbb{C}(r_{k}^{2})}\max(-\Phi_{i}^{ k},0) \tag{33}\]  
A varying penalty scheme is adopted to accelerate the search process, which is similar to the work of [PERSON] et al. [46]. When an infeasible solution still violates the constraints after the penalty, each penalty factor will be multiplied by 10 and the local search will be conducted again. If the infeasible solutions still exist after two rounds of multiplication by 10, no further attempts will be made. Therefore, this procedure cannot ensure the elimination of all the infeasible solutions, but it can effectively reduce the number of them at a low cost.
## 2 Brief Background of USV Technology
### Applications of USVs  
USVs have emerged as versatile tools with applications across various industries. In defense and security, USVs contribute to surveillance and safeguarding offshore systems [20]. Environmental monitoring benefits from USVs' ability to collect real-time data on water quality and marine biodiversity, offering cost-effective alternatives to traditional research vessels [40]. In commercial offshore services, USVs enhance efficiency through inspections of subsea assets and are being explored for autonomous shipping [41]. USVs also play a crucial role in search and rescue missions, though this application was not explicitly mentioned in the provided abstracts. Technological advancements have improved USVs' autonomy, sensor integration, and communication systems, expanding their capabilities and reliability [20]. The integration of USVs into existing maritime operations offers a cost-effective solution with minimal risk to personnel [11].  
Thus, based on the main applications found in the market, we can broadly categorize the following USVs' uses:  
* Environmental monitoring;
* Surveillance and security;
* Scientific research;
* Asset inspection;
* Military and defense;
* Communication and navigation;
* Survey and mapping;
* Disaster response;
* Logistics and support;
* Energy and offshore.
# GNSS Signal Compression Acquisition Algorithm Based on Sensing Matrix Optimization
## 3 Sensing Matrix Optimization of Compression Acquisition
### Sensing Matrix Optimization  
For the given sparse basis \(\mathbf{\Psi}\), the optimization problem of the sensing matrix can be expressed as  
\[\mathbf{\Theta}_{\mathrm{opt}}=\mathop{\arg\min}\limits_{\mathbf{G}_{ \mathrm{t}}\in\delta_{\mathrm{e}}}\rho(\mathbf{\Theta},\mathbf{G}_{\mathrm{t}} )=\left\|\mathbf{G}_{\mathrm{t}}-\overline{\mathbf{G}}\right\|_{\mathrm{F}}^{2} \tag{16}\] \[\mathrm{s.t.}\ \ \ \overline{\mathbf{G}}=\overline{\mathbf{\Theta}}^{ \mathrm{T}}\overline{\mathbf{\Theta}}=\mathbf{\Theta}_{x}^{\mathrm{T}}\mathbf{ \Theta}^{\mathrm{T}}\mathbf{\Theta}\mathbf{\Theta}_{x}\]  
It is observed that \(\rho(\mathbf{\Theta},\mathbf{G}_{\mathrm{t}})\) is a highly non-convex function with respect to the \(\mathbf{\Theta}\) and \(\mathbf{G}_{\mathrm{t}}\). Thus, the objective function can be minimized by optimizing sensing matrix \(\mathbf{\Theta}\) and target matrix \(\mathbf{G}_{\mathrm{t}}\) alternatively.
## 4 Conclusions  
When suspended aerosols pass through the optical detection area of our multidimensional polarization analysis system one by one, their microphysical features, such as size, morphology, and refractive index, can affect the polarization status of scattered light. In this work, we employ a self-developed multi-angle Stokes vector analyzer to obtain 24-dimensional polarization indices simultaneously from 12 kinds of suspended particles. Experimental results make it clear that one or two selected polarization scattering optical indices cannot ensure that aerosols with complex properties in real atmospheric environments can be well-distinguished and identified. Different polarization indicators have respective discrimination for different aerosol categories and attributes, so a combination of multidimensional indices can be employed as a fingerprint-type optical feature to improve the refinement and specificity of aerosol recognition. If we have prior knowledge of aerosol types, such as water-soluble salts or carbonaceous particles, the mean, PFR, and Wilk's Lambda values from the multidimensional polarization index system in this paper can provide some subsidiary basis to select the optimal indicators for specific aerosol type. Moreover, based on the sensitivity of different polarization indicators to aerosol properties, we can explain the microphysical characteristics of aerosols from various emission sources or describe the microphysical feature in the process of aerosol dynamic evolution.  
Conceptualization, R.L.; methodology, R.L.; software, R.L.; validation, R.L.; formal analysis, W.G. and N.Z.; investigation, W.G.; data curation, W.G.; writing--original draft preparation, R.L. and W.G.; writing--review and editing, W.G. and N.Z.; supervision, N.Z., Y.H. and H.M.; project administration, J.G., H.D. and D.H.; funding acquisition, N.Z. and H.M. All authors have read and agreed to the published version of the manuscript.  
This work has been supported by National Key Program of Science and Technology Supporting Economy of China (2020 YFF01014500 ZL); Science and Technology Research Program of Shenzhen Grant (JCYJ20200109142820687).  
Not applicable.  
Data Availability StatementData is contained within the article.  
The authors declare no conflict of interest.  
Figure 13: (**a**) P-Pdop (60\({}^{\circ}\)) versus H-Pdop (60\({}^{\circ}\)) to identify aerosol features of previous sets of experiments: compositions (Box A, water-soluble salt), particle size (Box B, PSLs), optical absorption, and morphological characteristics (Box C, carbon samples). (**b**) R-Hdop (60\({}^{\circ}\)) versus R-Pdop (30\({}^{\circ}\)) to identify aerosol features: morphological characteristics (Box D, carbon samples) and the optical absorption effect (Box E, absorption effect from strong to weak).
## 5 Conclusions  
This article imposes the regularization of joint spatial-spectral smoothing in the minimum-volume simplex on the original CNMF to propose the JSMV-CNMF fusion method. AO and ADMM algorithms have been used to carefully design the solvers. Then, complexity reduction was conducted to solve the bottleneck of heavy computation. To evaluate the performance of the proposed algorithm, the experimental tests have been conducted based on [PERSON]'s protocol, using two real datasets collected by different sensors. Then, three different noise conditions were set to test the anti-noise performance, in which the curves of RSNR and RMSE in the low-SNR environment were drawn to show the image performance changes of the fused images with the spectral bands (or wavelengths). Simultaneously, the computational complexities of non-simplified and simplified solutions were compared, and the running time was computed as the complexity measure of the complete fusion methods. Experimental results show that the proposed JSMV-CNMF algorithm has outperformed the state-of-the-art methods, which may not only enhance the spatial resolution of reconstructed images, but also greatly improve the anti-noise performance of the original CNMF method. At the same time, it fully verifies the validity of the regularization used in this article. Planned future work includes (i) further exploring the parameter tuning of the regularization weights; (ii) further studying the iteration stopping rules for convergence and efficiency; and (iii) investigating the effect of low-rank \(\mathbf{A}\) and \(\mathbf{S}\) on the fusion method, which will improve the performance of the proposed algorithm and enhance the quality of the reconstructed images.  
[PERSON] conceived the idea; [PERSON] derived the algorithm and wrote the first draft; [PERSON] and [PERSON] designed the experiments and verified the validity; [PERSON] contributed the final draft; [PERSON] and [PERSON] reviewed and revised the paper. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Scientific Research Project of Colleges from Liaoning Department of Education (P.R.C) grant numbers LJ2017 QL014, LJ2019 QL006 and LJ2019 JL022.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Gas Absorption Feature Extraction Based on RPCA  
The classical principal component analysis (PCA) algorithm is widely used in data analysis, dimensionality reduction and denoising today. It assumes a Gaussian independent and identical distribution of small noise data, but its robustness is poor for non-Gaussian noise or noise with serious outliers. Robust PCA aims to separate a low-rank matrix \(L\) and a sparse noise matrix \(S\) from observations \(M\). Unlike the small noise required in classical PCA, RPCA is able to recover the low-rank data from arbitrarily large magnitude and sparsely noisy observations. The separation problem is solved by tractable convex optimization [24]:  
\[\begin{array}{l}\text{minimize}\left\|L\right\|_{*}+\lambda\left\|S\right\| _{1}\\ \text{subject to }L+S=M\end{array} \tag{5}\]  
where \(\left\|L\right\|_{*}=\sum_{i}\sigma_{i}(L)\) denotes the nuclear norm of the matrix of \(L\), \(\sigma_{i}(L)\) is the singular value of matrix \(L\), \(\left\|S\right\|_{1}\) denotes the \(l_{1}\) norm of \(S\), \(\lambda\) is the sparsity constraint parameter of the matrix \(S\), and \(M\in\mathbb{R}^{n_{1}\times n_{2}}\) is the observation matrix. The choice of \(\lambda\) is always \(\lambda=\sqrt{max(n_{1},n_{2})}\). For practical problems, it is possible to improve performance by choosing \(\lambda\) in accordance with prior knowledge. If \(S\) is very sparse, increasing \(\lambda\) will recover \(L\) of larger rank. Equation (5) can be solved using alternating direction method of multipliers (ADMM).  
Due to the vibration and unstable attitude of the unmanned helicopter, we cannot measure the accurate background spectrum. RPCA is able to recover the low-rank data, so we can use the RPCA algorithm to solve the background spectrum and gas absorption feature separation problem. Consider the background spectrum as a low-rank matrix and the gas absorption feature as a sparse matrix in the observed data. Specifically, before measuring the spectrum where the target gas exists, we first measure a series of background spectra \(b_{i}(i=1,2,\ldots,N)\) near the measurement site, and then the gas spectrum \(\mathbf{x}\) and the background spectrum are arranged to form the original observation data matrix \(M\):  
\[M=[b_{1},b_{2},\ldots,b_{N},x] \tag{6}\]  
The background estimation spectrum \(\mathbf{L}\) and sparse gas absorption feature spectrum \(S\) are separated by the RPCA method.
## 2 Materials and Methods
### Study Selection, Data Extraction, and Selection Criteria  
The specific inclusion criteria were: (1) studies analysing the effect of different stretching in the warm-up, on a vertical jump or lower-limb ROM; (2) studies that included assessment before and after warm-up; (3) warm-ups including an aerobic general warm-up followed by SS or DS; (4) experimental studies with a control situation; and (5) studies carried out on adult men and/or women (\(\geq\)18 years) without pathologies or health problems. Studies were excluded if they: (1) were review articles, editorials, letters to the editor, or case reports; (2) were not performed on humans; (3) were not available in English or Spanish; and (4) did not provide or detail numerical data on the specified variables.  
The studies that met the inclusion criteria were coded and stored in a spreadsheet. The data extracted were the site and country of the study, name of the first author and year of publication, and characteristics of the population with the total sample and groups (gender of participants, age (years), weight (kg), height (cm) and physical fitness). The information about the characteristic programme included total warm-up duration (min), time of aerobic warm-up (min), sets, repetitions, stretching time (sec), rest stretching (sec), and stretched muscle groups. The reference lists of included articles were screened to add possible relevant articles and, in the absence of essential data in the original studies, authors were contacted for the necessary information. Two reviewers (A and B) independently extracted data from the included studies.
# Nano Aerial Vehicles for Tree Pollination
## 2 Related Works
### Robotic Solutions for Pollination  
Using robotics for pollination has been an active area of research in recent years. Researchers have explored various types of robotic systems to pollinate crops in a variety of scenarios. To be able to detect the flowers to perform pollination, automated solutions are usually equipped with a camera. These studies illustrate the potential of using deep learning-based object detection algorithms in precision pollination:  
* A convolutional neural network (CNN) for the highly accurate real-time detection of kiwi flowers in an orchard environment using a camera on a UAV. The authorstrained a CNN on a dataset of kiwi fruit flowers. The proposed system achieved high accuracy in the real-time detection of kiwi fruit flowers, even in challenging lighting conditions [21];
* An R-CNN mask for identifying flowers on apple trees for proper pollination. The authors trained an R-CNN mask on a dataset of images of the flowers of apple trees. The proposed model detected flowers in images taken by a UAV [22];
* YOLOv5 and Euclidean distance algorithm for identifying the distribution of kiwi fruit flowers in an orchard. The authors developed a multi-class detection algorithm based on YOLOv5 to detect kiwi fruit flowers in images taken by a UAV and then used the Euclidean distance algorithm to identify the distribution of the flowers [23];
* Autonomous visual navigation for a flower-pollinating UAV. The system uses a camera mounted on a UAV to capture images of the environment and then uses image processing and machine learning algorithms to detect and locate flowers. The system also uses information from the camera to control the UAV's movement and maintain a constant pause over the flowers as it pollinates [24];
* A simulation model was used to simulate the movement of multiple UAVs in a facility agriculture environment and their interactions with plants. The authors used the simulation model to test different compensatory pollination strategies and evaluate their effectiveness. The results showed that the proposed simulation model could effectively simulate the behaviour of multiple UAVs for compensatory pollination in facility farming [25].