esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions and Future Perspectives  
Elicitation is a promising strategy to over-produce industrially important flavonoids. Amongst different types of elicitors, NPs as elicitors are effective in lower concentrations to enhance the productivity of flavonoids. Despite immense research on NPs as novel elicitors of flavonoids, there are still some knowledge gaps which need to be filled. Firstly, a deep understanding of the underlying mechanism of nano-elicitation is important for its successful industrial adoption. Moreover, an understanding of plant response to the nanomaterials applied as elicitors either individually or in combination with other classes of NPs or with other types of elicitors is essential for developing strategies for enhanced flavonoid production [31].  
Negative effects on plant primary metabolism such as lower chlorophyll content, a decrease in cell viability, a decrease in sugar concentration and suppressed seed germination have been reported during nano-elicitation experiments [25]. To ensure the success of elicitation, addressing nano-toxicity in plants is important. Furthermore, the targeted delivery of NPs enhances the efficacy of elicitation using lower concentration of NPs and prevents phyto-toxicity. This can be achieved by the surface functionalization of NPs with bio-molecules, tags or magnetic fields [96].  
Moreover, in most of the studies, an enhancement in the production of flavonoids is reported only in terms of total flavonoid content. This gives an overall picture of NPs' role as an elicitor of flavonoids. However, from an industrial viewpoint it is important to characterize and report the effect of NPs on specific industrially important flavonoids. There are some plant species naturally enriched with flavonoids, such as _Allium_ species, which have not been treated with NPs to elicit flavonoid production [97]. Further analysis of different plant species will allow the discovery of novel flavonoids which can be nano-elicited in in vitro cultures for the enhanced production of flavonoids for industrial use.  
Conceptualization, writing and editing, A.K.K., S.K., D.T., C.H., B.H.A. and S.A. All authors have read and agreed to the published version of the manuscript.  
44926 WK). D.T. gratefully acknowledges the support of the French government via the French Embassy in Thailand in the form of Junior Research Fellowship Program 2018.  
The authors declare no conflict of interest.
## 2 Methodology  
### Visibility Graph (VG)  
[PERSON] et al. introduced the first visibility graph method, known as the natural visibility graph (NVG), or simply named the visibility graph (VG) in most of the literature [10]. In the visibility graph algorithm, values of the time series are represented using vertical bars. Each data point in the time series is treated as a node in the network. If two nodes can \"see\" each other, there is an edge between them in the network. The visibility criterion is described as follows:  
For any \((t_{c},x_{c})\) between two points, \((t_{a},x_{a})\) and \((t_{b},x_{b})\), here, \(t_{a}<t_{c}<t_{b}\) if it fulfills the following relationship:  
\[x_{c}<x_{b}+(x_{a}-x_{b})\frac{t_{b}-t_{c}}{t_{b}-t_{a}} \tag{1}\]  
Then, there is an edge between the nodes corresponding to data points \((t_{a},x_{a})\) and \((t_{b},x_{b})\) after they are mapped onto the network.  
The visibility graph algorithm is a method for transforming a time series into a network graph based on visibility criteria. In this algorithm, each data point in the time series is represented as a vertical bar, with the height of the bar corresponding to the value of the data point. The core principle of the algorithm is to connect nodes (bars) with an edge if they can \"see\" each other. Specifically, two nodes are connected by an edge if the straight line drawn between their tops does not intersect the top of any other bar that lies between them.  
This visibility criterion ensures that each node is connected to all other nodes it can see, resulting in a fully connected network where each node typically has edges to at least two neighboring nodes. The visibility graph created is invariant to affine transformations, such as scaling or translation, meaning the structure of the network remains unchanged regardless of how the bars are scaled or shifted. This property makes the visibility graph a robust representation of the inherent structure of the time series data.  
To better illustrate this algorithm, consider a time series with 10 data points: 0.2, 0.5, 0.1, 0.8, 0.6, 0.75, 0.9, 0.3, 0.7, and 0.5, sampled as a nonlinear time series, as shown in Figure 1.  
Figure 1: Schematic of the visibility graph algorithm.  
From Figure 1, it can be seen that each data point is abstracted as a vertical bar, with the height of the bar corresponding to the value of the data point. If the bars can see each other, there is an edge between the corresponding data points.
## 7 Discussion
### Industrial Implications of the Findings  
The results of the industrial application of the solution space management are structured according to the two steps of the approach, as presented in Section 5. Thus, the results and the industrial implications for step 1 are followed by the results and implications for step 2.  
The industrial application of step 1, solution space structuring, has shown that all necessary input data types for simulation, according to VDI3633-1:2014-12 [(43)], can already be addressed during strategic network design. Furthermore, a set of sub-solution spaces can be derived for operational decisions during strategic network design, representing independent planning problems. These results suggest that simulation can be applied during strategic network design and that the large solution space can be structured into independent planning problems. Based on this, planning experts can develop alternatives for each sub-solution space, which can be combined, leading to alternative network solutions.  
The industrial application of step 2, solution space planning, led to the discovery that the alternatives of each sub-solution space can be multiplied to derive an experiment design that covers the solution space. Furthermore, generating alternatives for each sub-solution space builds on different operational decisions and can be structured for each sub-solution space. The results of step 2 suggest that creating alternative solutions for strategic network design can be improved by generating alternatives for each sub-solution space, which are then multiplied for a final experiment design.  
In summary, the results of the successful industrial application of solution space management in strategic network design suggest that this approach significantly supports the objective construction of alternative network solutions.
# Communication System Based on Magnetic Coils for Underwater Vehicles
## 1 Introduction  
Underwater robotics has become a research area of great interest in recent years; its applications extend to different disciplines such as monitoring marine biology [1], reconstruction of the seabed [2], inspection of underwater pipelines [3], and continuous monitoring of marine species [4], among others.  
One of the main challenges of underwater robotics is communication systems. The attenuation that electromagnetic signals suffer underwater makes it very difficult to use communication and global positioning systems that are commonly used on the surface [5], making it difficult to plan optimal routes in the navigation of autonomous mobile robots [6]. Electromagnetic waves have an electrical and a magnetic component, which are exposed to optical phenomena such as refraction, reflection, dispersion, and diffraction which modify the propagation pattern of the waves, causing a distortion in the received signal relative to the emitted signal.  
Due to these difficulties, current submarine communication systems are very expensive, consume large amounts of energy, are relatively large, send and receive information at very low speeds, and have a limited range.  
Underwater communication systems can be classified according to the technology on which they are based. The main characteristics of each technology are presented below:
## 5 Simulation Verification
### The Influence of Initial Fault Conditions
The tests employed varying resistance values (10 \(\Omega\), 100 \(\Omega\), 500 \(\Omega\), and 1000 \(\Omega\)) with a fixed initial fault phase angle of 90\({}^{\circ}\). These findings are presented in Table 7.  
To examine the effect of fault distance \(d_{a}\), single-phase ground faults were implemented at multiple locations: along line \(l_{3}\) (0.4, 2.5, 3.3, and 6.2 km from section M) and line \(l_{5}\) (0.4, 2.5, 3.3, and 5.2 km from section M). These tests maintained consistent parameters of 10 \(\Omega\) transition resistance and 90\({}^{\circ}\) initial phase angle. The results are compiled in Table 8.  
It can be seen from Tables 4 and 6-8 that the proposed method has high ranging accuracy under different neutral point treatments, fault initial phase angles, transition resistances, and fault distances. The maximum absolute error of the ranging result is 88 m. The size of the fault initial phase angle and fault distance only affect the amplitude of the wave head. The influence of the fault distance within the line length of the small current grounding system on the shape of the wave head is also very limited. Therefore, the accuracy of the positioning method in this paper is basically not affected by the initial conditions of the fault. However, it is mainly affected by the accuracy of the MVMD results. Due to the complexity of the decomposition process, the accuracy of the positioning method in this paper shows the law of uncertainty, which can also be seen from Tables 4-8. In addition, it can be seen from Table 5 that the proposed method can accurately locate the fault location under single-phase grounding fault and two-phase grounding fault. However, for three-phase symmetrical grounding fault, three-phase fault, and two-phase fault, due to the absence of zero-mode component in the system, the method in this paper is invalid, and the scene is still worthy of further study.
## 3 Enhancing Security Through Joint Precoding and Artificial Noise Design  
The precoding matrix \(\mathbf{P}\) is responsible for controlling multiple beams, while the power-control factor \(\zeta\) governs both the AN power and beam gain. These two parameters are tightly coupled and jointly contribute to secure transmission. Optimizing only one of them in isolation would be insufficient to enhance security. Specifically, through joint optimization of these parameters, the multi-beam parameters and artificial noise power (i.e., the interference caused to [PERSON]) can be controlled under different positions and signal-to-noise ratios (SNRs), thereby maximizing the enhancement of transmission security. In this section, we take the secrecy rate as the objective for security enhancement and conduct a thorough analysis and optimization.
## Acronyms  
AE Acoustic Emission or Autoencoder (depending on context)  
AI Artificial Intelligence  
AL Active Learning  
ANN Artificial Neural Network  
ARIMA AutoRegressive Integrated Moving Average  
BDL Bayesian Deep Learning  
BN Bayesian Network  
CAD Computer-Aided Design  
CBM Condition-Based Maintenance  
CFD Computational Fluid Dynamics  
CLAHE Contrast-Limited Adaptive Histogram Equalization  
CNN Convolutional Neural Network  
COT Computed Order Tracking  
CWT Continuous Wavelet Transform  
DT Digital Twin or Decision Tree (depending on context)  
DTW Dynamic Time Warping  
DWT Discrete Wavelet Transform  
EDA Encoder-Decoder with Attention  
EMD Empirical Mode Decomposition  
FDR Frequency-Domain Reflectometry  
FFT Fast Fourier Transform  
FMECA Failure Mode, Effects, and Criticality Analysis  
GA Genetic Algorithm  
GB Gradient Boosting  
GP Gaussian Process  
GRU Gated Recurrent Unit  
HDBSCAN Hierarchical Density-Based Spatial Clustering  
HI Health Index  
HSA Hierarchical Symbolic Analysis  
IoT Internet of Things  
JL-CNN Joint Learning Convolutional Neural Network  
kNN k-Nearest Neighbours  
KPCA Kernel Principal Component Analysis  
LDA Linear Discriminant Analysis  
LLM Large Language Model  
LMD Local Mean Decomposition  
LSTM Long Short-Term Memory  
MC Monte Carlo (e.g., MC Dropout)  
MFCC Mel-Frequency Cepstral Coefficient  
ML Machine Learning  
MLP Multi-Layer Perceptron  
MODWPT Maximal Overlap Discrete Wavelet Packet Transform  
MVN Multi-Valued Neuron
# Online Distributed User Association for Heterogeneous Radio Access Network
## 6 Performance Evaluation
### Loads among Different BSs  
Along with the traditional _max-SINR_, we also consider the BS-centric algorithm (_BS-centric_), proposed in Ref. [19], as our benchmark scheme. The UE share in uniform and non-uniform deployment of UEs is shown in Figure 5(a),b, respectively. In the uniform UE deployment scenario, as expected, the traditional _max-SINR_ shows an uneven UE share. However, we can see that a considerable amount of UEs are being offloaded to Pico and Femto BSs in the case of _Prob. scheme 1_, _Prob. scheme 2_, and _BS-centric_. UEs associated with macro BS are being offloaded to both Pico and Femto BSs, which verifies the effectiveness of our proposed _probability scheme_s. The _d-choices_ schemes, on the other hand, focus on tier-wise balanced UE allocation. Figure 5(a) clearly shows the balanced allocation of UEs for _d-choices 1_ and _d-choices 2_ schemes. A similar trend can be seen in non-uniform deployment. More UEs are offloaded to pico and femto BSs.
## 3 Thermal Transfer Modelling of BPB-CFEH
### Model Validation  
The 10 th set of test data from the indoor modeling tests, which had the most significant effect on the internal temperature field, was selected as the control group for the simulation data [29]. The duration of this group of test ice melting is 8 h, the ambient temperature is constant at \(-15\,^{\circ}\)C, and the wind speed is 4.5 m/s. The test is carried out at the same time. The initial internal temperature of the BPB-CFEH was measured at \(-9.99\,^{\circ}\)C using a Keysight thermometer before testing. A carbon fiber heating wire was used to heat the crossbeam during the experiment. Real-time monitoring of temperature data was conducted at three measurement stations labeled A, B, C, and D. The same test conditions as in group 10 were set up in the simulation. However, the internal initial temperature of the BPB-CFEH in the indoor modeling test was only determined based on the average of 12 temperature measurement points, which does not accurately measure its internal initial temperature. The initial temperature of the internal concrete part set by BPB-CFEH in the simulation is consistent with the ambient temperature of \(-15\,^{\circ}\)C, and the initial temperature of the carbon fiber heating cable and polyethylene is \(-9.99\,^{\circ}\)C.  
The difference between the average value of twelve temperature measurement points on heating cables A, B, C, and D and the finite element simulation results was selected as the error evaluation criterion. According to the Root Mean Square Error (RMSE) Equation (5), the Root Mean Square Error (RMSE) of the average value of the experimental data and simulation data is 0.243, which indicates that the simulation accuracy is high and can be used as a reference:  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{N}(X_{sim,i}-X_{mod,i})^{2}}{N}} \tag{5}\]  
Figure 5 shows the simulated values were consistently lower than the experimental values until 4 h. This is due to the fact that the average temperature of the concrete portion of the model modeled in the tests under the initial conditions is higher than the initial temperature set in the simulation. After 4 h, the simulated value gradually exceeded the test value. This is because, during the ice-melting process in the test, the upper part of the beam side where the ice melts into water does not directly enter the ground. Instead, it slowly flows towards the bottom of the model and remains attached to the bottom for a certain period before finally flowing into the ground. Throughout the procedure, the water would persistently assimilate the internal temperature of the model, resulting in the occurrence above.  
Figure 5: Validation of test values against simulated values.
# On-Orbit Calibration Method for Correction Microwave Radiometer of the HY-2 Satellite Constellation
## 4 Calibration in the HY-2 Satellite Constellation
For the 23.8 GHz channel, BT differences between the HY-2C and HY-2D satellites and the HY-2B satellite before and after on-orbit calibration are shown in Figure 7. It can be seen that after on-orbit calibration, the overall trend of BT differences between them and the HY-2B satellite in the 23.8 GHz channel is close to zero. Similarly, BT values from 18.7 GHz and 37 GHz can be also corrected using this method. The plots in Figures 8 and 9 illustrate the comparison between the HY-2B and HY-2C satellites as well as the HY-2D satellites, showing the differences in their BT before and after on-orbit calibration. Furthermore, the on-orbit calibration equations can be derived from these comparisons:  
\[T_{RC\_187}=0.9562\cdot T_{C\_187}+3.4183 \tag{3}\]  
\[T_{RD\_187}=0.9306\cdot T_{D\_187}+11.065 \tag{4}\]  
Figure 6.0 Comparing BT differences for 23.8 GHz in crossover: (a) BT differences, which fall from about 5 K to 0 K between HY-2B and HY-2C; (b) BT differences, which fall from about 10 K to 0 K between HY-2B and HY-2D. Figure 6.1 Figure 6.2  
For the CMRs of the HY-2C and HY-2D satellites, the corrected three-channel BT data may be used to retrieve new AWV and WPD values, which differ from uncalibrated products and must be checked for accuracy. Figure 6.3 Comparing BT differences for 18.7 GHz in crossovers. Figure 6.4 Comparing BT differences for 37 GHz in crossovers.
## 3 Results
### Diagnostic Test Performances  
The expert panel correctly identified 286 of 330 scenes (86.7%; Tables 3 and 4). False positive (FP) assignments (motion detection in no motion scenes) occurred in 37 of 150 scenes with no motions (FP rate 24.7%; specificity 75.3%). This finding occurred more often in men than women: in total, 26/60 (43%) vs. 11/90 (12%), \(p\) < 0.001 (Table 4). Further, FP occurred more often in scenes with than without a mobile phone: 6/30 (53%) vs. 21/120 (17.5%), \(p\) < 0.001 (Table 5). The breathing intensity by contrast (shallow, normal, strong) showed no reliable differences in the number of false assignments (number of FP: 2.2 vs. 2.0 vs. 3.0 FP). False negative (FN) assignments (no motion detection in motion scenes) occurred mainly in low-intensity motion scenes (scenes #2, #10, #18, #20). In total, 7/180 motion scenes were not recognized correctly (FN rate 3.9%; sensitivity 96%). There was no relevant difference between men and women concerning the number of FN test results: 3/72 (4.2%) vs. 4/108 (3.7%), \(p=0.88\). The overall [PERSON]'s kappa coefficient between the panel and the actual scene was 0.73 (95% confidence interval 0.65; 0.80), indicating substantial agreement. If scenes including a mobile phone were excluded from the analysis, kappa increased to 0.80 (95% CI 0.73; 0.87). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Motion Detected**} \\  & & \multicolumn{2}{c}{**Yes**} & \multicolumn{2}{c}{**No**} \\ \cline{2-7}  & & Yes & 173 & 7 & 180 \\  & (Motion scenes) & (true positives) & (false negatives) & & 150 \\  & & No & 37 & 113 & \\  & (No Motion scenes) & (false positives) & (true negatives) & & 150 \\ \hline \multicolumn{7}{c}{210} & 210 & 120 & 330 \\ \hline \multicolumn{7}{c}{Scenes identified correctly by the panel (N) (motion assignments as referenced in Table 1). Sensitivity was 96\%, specificity 75\%, positive predictive value 82\%, negative predicted value 94.2\%, and [PERSON]’s kappa (95\% confidence interval) 0.73 (0.65; 0.80).} \\ \end{tabular}
\end{table} 
Table 3: Diagnostic test performances in the total cohort.
## 4 Technical Review and Discussion
### Evaluation Metrics
#### 4.1.2 Evaluation Metrics for Robustness  
Robustness refers to the watermark's resilience to various types of attacks, such as noise addition, compression, or transformations. Traditional robustness metrics often focus on common image processing attacks; however, LDM-based watermarking needs to account for the diffusion model's generative process and potential adversarial manipulation in the latent space.  
* **Bit error rate (BER):** BER is used to quantify the robustness of the watermark by measuring how many bits differ between the original and extracted watermark [46]. A low BER implies that the watermark is resilient against attacks like cropping, rotation, and compression.
* **Normalized correlation (NC):** NC measures the similarity between the embedded and extracted watermarks after the image has undergone attacks [46]. It helps in determining the extent to which the watermark remains intact despite modifications. A high NC value indicates that the watermark is successfully retrieved and is resistant to degradation.
* **Attack simulation tests:** The watermark robustness is tested under several attack scenarios, including JPEG compression, Gaussian noise addition, cropping, and brightness adjustment [46]. These tests ensure that the embedded watermark can withstand transformations that are likely to occur in real-world applications. Different studies use various image attacks, including destructive, constructive, and reconstructive attacks, to evaluate the resilience of the watermark.
# Evaluation of Different Drying Methods on the Quality Parameters of _Acanthopanax semicosus_ Fruits
## 3 Results
### Analysis of Amino Acid Composition  
Amino acids are crucial for plant metabolism, serving as precursors for proteins, secondary metabolites, and stress-responsive compounds [41; 42]. Their composition is significantly influenced by external factors, including drying methods [43]. As shown in Table 3, 17 amino acids were identified in AS fruits across different drying treatments, with total content ranging from 3.934 to 11.590 mg/g. Arg was the most abundant, contributing 27.1-31.1% of total free amino acids, with concentrations varying from 1.070 to 3.610 mg/g. VFD retained the highest Arg content (3.610 mg/g), followed by VD (2.900 mg/g), HD (2.070 mg/g), and MD (1.070 mg/g). Notably, Arg levels in VFD samples were approximately 3.4-times higher than in MD samples, highlighting the superior preservation of amino acids under low-temperature drying conditions.  
VFD preserved the highest levels of Asp, Thr, Ser, Glu, Cys, Met, Tyr, His, and Arg, whereas VD retained higher concentrations of Gly, Val, Ile, Leu, Phe, and Lys. HD was most effective in preserving Ala, while ND resulted in the lowest levels of Asp, Thr, Ser, Gly, Leu, Phe, and Tyr. High-temperature drying methods, such as HD, may induce thermal degradation of heat-sensitive amino acids like methionine and glutamic acid, primarily due to the Maillard reaction. This reaction, which accelerates at elevated temperatures, leads to interactions between amino acids and reducing sugars, causing structural modifications and reduced amino acid content [44]. In contrast, VFD's low-temperature conditions effectively mitigate thermal degradation, preserving amino acid integrity [44].  
The vacuum environment in VD may alter the redox state of amino acids, potentially affecting substance transfer and reaction dynamics, as indicated by the higher arginine content. In contrast, MD's electromagnetic fields can disrupt molecular structures and weaken the chemical bond stability of amino acids. Microwave heating can induce protein denaturation, leading to the release of amino acids. However, excessive microwave heating may result in the degradation of amino acids, particularly heat-sensitive ones such as lysine.