esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Fucoxanthin Content and Stability by UVA and Heat Exposure
In addition, the degradation pattern of the fucoxanthin in the ethanol extract and solvent fractions did not show a significant difference when heated at 55 \({}^{\circ}\)C and 75 \({}^{\circ}\)C, except for the chloroform fraction. The degradation of fucoxanthin was much more acute at 75 \({}^{\circ}\)C than at 55 \({}^{\circ}\)C. The thermal dynamics of fucoxanthin showed different patterns from different studies. Some showed rapid degradation after a few hours of thermal treatment, and some showed slight degradation even after more prolonged exposure [19; 21; 24]. Given the different sources of fucoxanthin and other solvents used in the preparations, different coexisting compounds present in the samples may play a critical role in the different degradation dynamics. In our study, the degradation of fucoxanthin in the ethanol extract and hexane fraction at 55 \({}^{\circ}\)C and 75 \({}^{\circ}\)C was gradual and reached plateau levels, whereas a much more rapid degradation of fucoxanthin was observed in the chloroform fraction. These findings suggested that certain compounds in the ethanol extract or fractions might discourage or promote the degradation of fucoxanthin. To the best of our knowledge, the effect of UVA, the major UV light in our daily life, on the stability of fucoxanthin had not been studied. Our study found that all-trans-fucoxanthin was unstable upon UVA exposure, and its stability was affected by the compounds being presented together. The absorbance of UVA by fucoxanthin suggests the potential in fucoxanthin for depigmentation. Figure 1: Standard curve of fucoxanthin (**A**), spectrum of fucoxanthin (**B**), and fucoxanthin structure and chromatogram of the chloroform fraction at 450 nm (**C**). Figure 3: The concentration of fucoxanthin in the (**A**) ethanol extract. (**B**) hexane fraction, (**C**) chloroform fraction, and (**D**) ethyl acetate fraction at 55 \({}^{\circ}\)C.  
Figure 2: UVA degradation kinetics of fucoxanthin in the (**A**) ethanol extract, (**B**) hexane fraction, (**C**) chloroform fraction, and (**D**) ethyl acetate fraction.
# Insights into the Issue of Deploying a Private LoRaWAN
## 4 Experimental and Analytical Results
### Deployment Ease Evaluation
#### 4.3.2 Methodology of Deployment
This software uses the Irregular Terrain Model (ITS) based on the Longley-Rice model, which is a method for predicting the attenuation of radio signals for a telecommunication link in the frequency range of 20 MHz to 20 GHz. Based on the results, the position of the gateways should be established with reference to the simulation and also the node density. However, the best position may not be always available, and the location of already owned infrastructure should also be considered). 2. Make a noise analysis and select a frequency plan. Deploy the gateways based on the estimated plan. 3. Select power levels and data rates (if adaptive data rate is not in use) for the nodes. Use the tester device to characterize the quality of the signal from the most remote and difficult points. 4. Optimize the network by replacing or adding gateways and device antennas. 5. Deploy the first devices and test the long-term parameters of the network by monitoring the main parameters, i.e., availability and latency. 6. Scale the network up by continuously monitoring the main metrics. 7. The network parameters will change continuously over time, and the network needs to be continuously optimized to preserve the required parameters. \(\bullet\)**Public Network**  
1. To ensure the performance metrics, agree with the public operator on the parameters via SLA. 2. Select power levels and data rates (if adaptive data rate is not in use) for the nodes. 3. Deploy the first devices and test the long-term parameters of the network by monitoring the main parameters, i.e., availability or latency. 4. Scale the network up by continuously monitoring the main parameters
5. The network parameters will change continuously over time, and it is necessary to continuously control the SLA from the operator.
# Advanced Technologies for Geosite Visualization and Valorization: A Review
## 2 Innovative Technologies for Geoheritage Visualization
### Virtual Reality (VR)  
Thanks to the recent developments in VR technology and functionalities, scientists have at their disposal additional options for visualizing spatial information. VR functionalities represent a way by which the viewer is immersed in a study area [89]. Digital viewers can navigate through an area through their smartphones or by means of VR ad-hoc equipment [90]. Fostering the development of VR technologies can provide added value with the goal of fostering touristic activities [91], and also as a way to create added educational value for geoplatforms. [PERSON] et al. [92] have used VR for data collection, complemented with analyses performed in Metaxa Mine, a renowned volcanological area in Santorini, Greece. They have come up with new maps in a GIS environment through data derived from the VR experience; finally, they have presented new results that highlight the role of Metaxa Mine as a crucial volcanic geosite particularly suitable for geotourism.
## Materials and Methods
### Queue Length Estimation Algorithm Design
#### 2.2.4 Weight Calculation of Combined Model  
As shown in Figure 1, the queue length computing will be done in the cloud server. It depends on the results of connected vehicles and edge server. To take advantage of shockwave-based model and camera-based model, they are combined by a weight to remedy the error induced by different vehicles length and the error induced by calculation delay. A weight determined by a ratio of last connected vehicles' stop time to the red-light period in this intersection is used to combine the shockwave-based model and camera-based model. The ratio is denoted as Equation (8), where \(t_{\text{s}}\) is the stop time of the last connected vehicle. \(t_{f}\) and \(t_{r}\) is defined in Equations (1) and (2) in Section 2.2.2. Equation (8) implies that weight \(\alpha\) is related to the queue length variety and the distribution condition of connected vehicles. \[\alpha=\frac{t_{\text{s}}-t_{r}}{t_{f}-t_{r}} \tag{8}\]  
Based on the models of shockwave and DNN, the queue length can be calculated by Equation (9):  
\[L=\alpha l+(1-\alpha)l_{2} \tag{9}\]  
where \(L\) is the integrated queue length, \(l\) is the queue length estimation based on shockwave, which is defined in Equation (4). \(l_{2}\) is the queue length estimation based on DNN, which is defined in Equation (7). In order to assess the accuracy of the proposed integrated queue length estimation model, performance indexes including absolute error \(A_{E}\) defined as Equation (10) and relative error \(R_{E}\) defined as Equation (11) are shown as follows:  
\[A_{E}=\left|L_{c}^{*}-L_{c}\right| \tag{10}\]  
\[R_{E}=\frac{\left|L_{c}^{*}-L_{c}\right|}{L_{c}^{*}}\times 100\% \tag{11}\]where \(L_{c}^{*}\) is defined as the actual queue length measured at the intersection in a cycle \(c\). \(L_{c}\) is defined as the estimation queue length determined by Equation (9) in a cycle \(c\). The integrated vehicle queue length estimate algorithm depending on the combined computing model is summarized in Algorithm 1. ```
Input:\(l\), the predicted queue length based on shockwave; \(l_{2}\), the predicted queue length from DNN based on image, defined by (7); \(\alpha\), the weight of shockwave model, defined by (8); \(v_{n,j}\), velocity of shockwave for the n-th connected vehicle in lane \(j\); \(n\) is the number of connected vehicles in the queue; \(j\) is the number of lanes at the intersection; \(l_{p,j}^{i}\), the queue length before the connected vehicle numbered \(i\) in the lane \(j\); \(t_{f}\), the closing time of red light cycle; \(t_{r}\), the beginning time of a red light cycle; \(t_{ij}\), the stop time of the connected vehicle numbered \(i\) in the lane \(j\); \(k\), the number of a connected vehicle that crossing the inductive loop at the upstream; \(k_{c}\), the total number of vehicle types; \(L_{i_{c}}\), the length of type \(i_{c}\) of vehicle; \(Q_{i_{c}}\), the number of type \(i_{c}\) vehicle in the image; \(L_{0}\), the space between adjacent vehicles; \(m_{\text{v}}\), the sum of all vehicles in the traffic image; \(m\), the number of lanes at the intersection.
# Advanced Security Framework for 6G Networks: Integrating Deep Learning and Physical Layer Security
## 3 Results and Discussion
At 20 dB SNR, the accuracy of ML models is approximately 88%, while non-ML models achieve around 68%. Figure 2: Evaluation of the BER (**a**–**d**) and accuracy (**e**–**h**) of different approaches of non-ML, ML (LSTM), and hybrid. The training and validation loss graphs for Alice and Bob indicate that both the ML and hybrid models converge well, with the hybrid model showing faster convergence, as shown in Figure 3. This demonstrates the effectiveness of combining feature extraction with sequential learning in the hybrid model. For instance, the loss for the hybrid model drops to nearly zero within the first 10 epochs, whereas the ML model takes slightly longer, and the non-ML model shows slower convergence and higher final loss values. The integration of deep learning and PLS in 6G networks offers several advantages, such as enhanced accuracy in detecting physical layer attacks and improved resilience against common threats such as spoofing, jamming, and eavesdropping. The use of machine learning models such as LSTM and RF allows for better pattern recognition and anomaly detection, contributing to more secure communications. However, the high computational power required for these models can strain the energy efficiency of devices, particularly in large-scale 6G deployments. Moreover, the potential latency introduced by these techniques may be problematic in real-time applications, such as autonomous systems and vehicle-to-vehicle communication, where ultra-low latency is critical. Furthermore, the scalability of these techniques to handle the massive data throughput expected in 6G networks, alongside the need for efficient resource management to maintain quality of service, poses a significant challenge.
## 2 Models and Methods
### Calculation Methods  
The adsorption of CH3 Cl, CS2, C2H6S, and C6H6 on activated carbon was simulated using Gaussian software [31]. The B3 LYP method was employed for structural optimization and wavefunction generation at the 6-311+G**[24] basis set level. To address the inadequate description of dispersion interactions using functional methods, DFT-D3 (BJ) [25] dispersion correction was incorporated into the simulation. In order to enhance computational accuracy, DFT-D3 [32] dispersion correction was performed at the M062X/jun-cc-pVTZ [33; 34; 35] level for single-point energy calculations after geometry optimization. The optimized wavefunctions were analyzed using Multiwfn software [36] for van der Waals (vdW) surface ESP and IGMH analysis, while isosurface plots were generated using VMD software [37]. The physical adsorption energy \(E_{Ads}\) was defined as follows:  
\[E_{Ads}=E_{Pro}-(E_{AC}+E_{VOC}) \tag{1}\]  
where \(E_{Pro}\), \(E_{AC}\), and \(E_{VOC}\) represent the single-point energies of the physical adsorbate, activated carbon, and small molecule VOCs (CH3 Cl, CS2, C2H6S and C6H6), respectively.  
Figure 1: Molecular models of activated carbon and volatile organic compounds (VCCs): (**a**) NF, (**b**) PD, (**c**) PR, (**d**) methyl chloride (CH3 Cl), (**e**) carbon disulfide (CS2), (**f**) dimethyl sulfide (C2H6S), and (**g**) benzene (C6H6). NF is a comparative model devoid of any functional groups, while PD and PR denote activated carbon models featuring the pyridine and pyrrole functional groups, respectively.  
In the IGMH analysis, [PERSON] et al. proposed using \(\delta\)g\({}^{\text{inter}}\) iso-surfaces to visualize the regions of interaction. Furthermore, they employed a color-scale bar of the sign(\(\lambda_{2}\)) \(\rho\) function onto these surfaces to demonstrate both the type and strength of the interactions, as depicted in Figure 2. Here, \(\lambda_{2}\) represents the second largest eigenvalue of the electron density (\(\rho\)) Hessian matrix, and sign(\(\lambda_{2}\)) represents taking a sign of \(\lambda_{2}\). The \(\delta\)G\({}^{\text{atom}}\) defined by [PERSON] et al. was utilized for quantitatively measuring atomic contributions to the interactions between fragments.
# UAV Dynamic Object Tracking with Lightweight Deep Vision Reinforcement Learning
## 4 Research Methodology
### Dataset  
DRL agents must be trained through extensive trial and error before they can acquire robust policies to complete tasks. In practical applications such as dynamic object tracking, creating training experiences can be particularly challenging, time consuming, and costly due to the unique properties of each environment. When training a DRL agent using an existing dataset, the environment and the features of the environment, such as the state transition function, should be the same in order to maximize the performance. As an alternative, we can allow the agent to learn from the experiences generated while interacting with our custom environment. We use Experience Replay (explained in Section 4.2), which is a crucial component of off-policy learning, to store those experiences. An experience stored in the Experience Replay consists of four elements \((s,a,r,s^{\prime})\), in which:  
* State (\(s\)): this is the representation of the current state, before any action is taken by the agent.
* Action (\(a\)): the action the agent decides to take based on its policy \(\pi\) and the current state \(s\). This directly affects the next state \(s^{\prime}\) and reward \(r\).
* Reward (\(r\)): the reward returned from the environment for taking action \(a\) in state \(s\).
* Next state (\(s^{\prime}\)): the results state that the agent gets in after taking action \(a\) in state \(s\).
## 4 Parametric Study
### Pipeline Design Size Effect
#### 4.2.2 Diameter-Thick Ratio Effect  
The effect of pipeline diameter-thick ratio on lateral buckling is analyzed at a constant trigger length (\(L_{5}\) = 200 m). As shown in Figure 9, the increase in the diameter-thick ratio leads to increasing bending stress, but the global distribution of bending stress remains unchanged. With the same wall thickness, the increase in pipeline external diameter would result in increasing liquid flow volume in the pipeline, as a result, the axial force and the bending stress of the pipeline increase. Since the effective axial force provided by the overall thermal expansion of the pipeline is evenly distributed along the pipeline, the increase in the flow volume does not change the distribution state of the axial force. At a constant temperature of \(\Delta\)T = 70 \({}^{\circ}\)C and a constant wall thickness of \(t\) = 30 mm, the external diameter \(D\) affects the maximum axial compressive stress, as shown in Figure 10. The maximum axial stress increases linearly with the pipeline diameter-thick ratio. As shown in Figure 11, as the feeding-in section length increases, the received effective inhibitory tensile stress increases, and the anchoring effect of the pipeline is more obvious. The growth rate of tensile stress increases with the diameter-thick ratio. Therefore, large diameter pipeline are more likely to fail compared to small diameter pipeline during lateral buckling.  
Figure 10: Effect of the external diameter \(D\) on the maximum axial force.  
Figure 9: Effect of the external diameter \(D\) on bending stress.
## 4 Discussion
### Limitations and Future Work  
This study was designed to compare the theoretical performance of various multispectral vegetation indices against the results from a hyperspectral mixture model. Deriving simulated Planet SuperDove reflectance directly from atmospherically corrected AVIRIS-ng spectra controls for differences in sun-sensor geometry, sensor-to-sensor radiometric miscalibration, image coregistration, and atmospheric correction that would bias a comparison of independently collected observations. We note that radiometric and BRDF differences may be especially significant for Planet data, given the known limitations of the Dove/SuperDove fleet and the constantly evolving remedies [8; 50; 51]. Additionally, by convolving AVIRIS-ng reflectance spectra to SuperDove spectral response functions after applying ISOFIT, it is likely that the simulated SuperDove spectra exhibit substantially more accurate atmospheric correction than would be possible using SuperDove radiance spectra directly. This is most likely to impact results for EVI, given its use of the visible blue band. Moving forward, a comparative analysis of EVI and EVI2 computed from a comparably diverse compilation of SuperDove-observed reflectance spectra might provide a constraint on the effectiveness of atmospheric correction and could be an interesting avenue for future work.
## 4 Computer Simulation Results
### Frequency Conversion to Radiation with the Wavelength to 24 \(\mu\)m
For comparison, the computer simulation results are presented in Figure 6.  
It should be noted that both the up-chirping (\(c_{3}=1\), Figure 6a,d,g) and the down-chirping (\(c_{3}=-1\), Figure 6b,e,h) of the HFW pulse lead to sufficient improvements of the frequency down-conversion efficiency--about 70% is achieved in both cases (compared with the 34% conversion efficiency in Figure 5g). However, there are some differences between these two cases of the LFW generation. The mentioned conversion efficiency is achieved for the down-chirped HFW pulse in a two-times smaller distance (\(z=5\)) than the corresponding value occurring for the up-chirped pulse of the HFW (\(z=10\)). If the pulse chirp is \(c_{3}=1\), then the pulse shapes are smoother (Figure 6a) and do not possess the local minima, which can be observed in Figure 6b for IFW and HFW. However, the LFW pulse spectrum is broadened if the incident pulse possesses down-chirp (\(c_{3}=-1\)): the remarkable response of a medium can be observed in the pulse spectrum up to dimensionless frequencies \(\omega=\pm 6\) in Figure 6e, meanwhile, the LFW pulse spectrum almost vanishes at the frequency \(\omega=\pm 3\) in Figure 6d. The effective LFW amplification also occurs if both the LFW and HFW possess down-chirps \(c_{1}=-1\), \(c_{3}=-1\) in the input section \(z=0\) (Figure 6c,f,i). In general, the frequency conversion process in this case is similar to those occurring for the pulse chirps \(c_{1}=0\), \(c_{3}=-1\) (Figure 6b,gh). Only a slightly longer distance \(z=6\) is required to achieve the high efficiency of the frequency conversion (Figure 6h) and a more pronounced spectral response occurs at a range of the dimensionless frequencies \(\omega=\pm(2.5-6.5)\) (Figure 6f).
# Patient Mobility Support for Indoor Non-Directed Optical Body Area Networks
## 6 Conclusions  
The OBAN system with mobility support using non-directed optical wireless links has been investigated. The OBAN mobility scenario was defined by the optical coordinator on the patient body. The analytical expression for the indoor channel model incorporating multiple reflection points for a non-directed optical link was derived and validated through simulation. As a proof-of-concept, the ECG signal was employed to analyze the reliability of the proposed mobility support model. The experimental results demonstrated that the transmit diversity (scenario 3) presented very efficient user mobility performance achieving a BER value of 1.2 \(\times\) 10\({}^{-6}\) over a distance of 1.25 m.  
**Author Contributions:** [PERSON] and [PERSON] designed and performed the experiments; [PERSON] contributed to experiments and analysis tools for evaluating the results; the manuscript was written through contributions of all authors; all authors have given approval to the final version of the manuscript.  
**Funding:** This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2018R1D1A3B07049858).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Conclusions  
Sample preparation and analysis of hydrogen cyanide in the blood samples of fire victims based on the kinetics of the ninhydrin reaction in the presence of cyanide following the microdistillation of free and metabolized cyanide was investigated. The cyanide ion captured in alkaline absorbent after distillation at 40 \({}^{\circ}\)C in the presence of 20% phosphoric acid was determined spectrophotometrically at 493 nm. HCN metabolized to thiocyanate ions was recovered using a potassium permanganate solution. The kinetics of cyanide reaction with ninhydrin have not previously been fully investigated. We studied the specificity of kinetic curves and reached the conclusion that kinetic measurements may improve cyanide determination in blood. A close relationship between the slope of the kinetic curve and the cyanide concentration in blood samples was calculated. The concentration dependence on the slope values was linear in the range from 0.04 \(\upmu\)g mL\({}^{-1}\) to 1.08 \(\upmu\)g mL\({}^{-1}\) HCN. A relatively high recovery that varied between 75% and 85%, depending on the experimental conditions, demonstrated the efficiency of microdistillation. The results obtained using the slope values were in agreement with those of the method of determining cyanides at a fixed time of 15 min as well as with those of the chloramine-T/barbituric acid-pyridine method with no statistically significant difference at 95% confidence level. The developed analytical method is simple, selective, and sensitive enough for the rapid determination of blood HCN in clinical and forensic toxicology. This analytical protocol was used in clinical studies for measuring HCN free and total concentrations.  
Conceptualization and methodology, G.D., D.T. and D.B.I; software, validation, and formal analysis, G.D. and D.T.; investigation, resources, and data curation, G.D., D.T., D.B.I. and M.B.; writing--original draft preparation, G.D. and D.T.; writing--review and editing, G.D., D.B.I., M.B. and D.T.; visualization, supervision, project administration, and funding acquisition, M.M.D., S.I.T., D.B.I. and M.M.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of Transilvania University of Brasov (4/26.02.2020) and \"[PERSON]\" University of Medicine and Pharmacy Iasi (2182/27.01.2020).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: Not applicable.  
**Acknowledgments:** We thank our colleagues and their partner institutions for providing information, resources, and expertise that have greatly helped our research. The authors are grateful to Transilvania University of Brasov, Romania, for the financial support given for publication.  
**Conflicts of Interest:** The authors declare no conflict of interest.