esa-sceva/satcom-chunk-collection huggingface dataset:
# Two-Level Sharing and Extraction of Sensing Information with Hybrid V2X Communications
## 3 System Functionality and Motivation  
### System Model  
The system considered in this paper consists of a set of vehicles and a BS/AP at the edge server. Each vehicle employs two kinds of wireless-communication interfaces. One is a communication interface for connection with the BS/AP. The other wireless interface is for connection with nearby vehicles. The edge servers can be a mobile edge server, such as that utilized for LTE-based wireless communication, or an RSU-based edge server.  
In the system that we studied, the communication between BS/AP and the vehicles had a longer communication range than that of the communication between the vehicles.  
We refer to the wireless links between the vehicle and edge servers as global links and refer to the wireless links between vehicles as local links. For example, the global link can be a 5G- or LTE-based wireless link with a long communication range [22]. Meanwhile, there are several optional methods for the wireless communication between vehicles, including PC5, DSRC, and Wi-Fi (ad hoc mode). This paper considers short-range and low-cost communication, such as DSRC or Wi-Fi (ad hoc mode), for the local exchange of sensing information among vehicles.  
The V2X network system is represented as a graph \(G(V,E)\), where \(V\) is the vertex set of object vehicles, the BS/AP at the edge server. The object vehicles in \(V\) are considered to be a group of vehicles in a region that is in the service scope of an edge server. Let \(E_{V2V}\) denote the edge set of those between one vehicle and another. Let \(E_{V-BS/AP}\) refer to the edges between vehicles and the BS/AP. In the system graph \(G(V,E)\), \(E\) is the edge set that consists of \(E_{V2V}\bigcup E_{V,BS/AP}\). Let the graph \(G_{v}=(V_{v},E_{v})\) be a subgraph of \(G\), where \(V_{v}\) consists of the set of vehicles, and \(E_{v}\) represents the local links between vehicles. Due to the relative short communication range between vehicles, \(G_{v}\) may have multiple components.
## 2 Materials and Methods
### Permeability Measurement  
Lucifer yellow (LY, Sigma-Aldrich) was used as a marker of paracellular transport. Experiment was performed in duplicate according to the method previously described [17], after seven days of cell culture in static and dynamic conditions in parallel.  
Briefly, both compartments were washed and filled with Ringer-HEPES buffer (150 mM NaCl, 5.2 mM KCl, 2.2 mM CaCl\({}_{2}\), 0.2 mM MgCl\({}_{2}\)-6H\({}_{2}\)O, 6 mM NaHCO\({}_{3}\), 5 mM HEPES, 2.8 mM glucose, pH 7.4). At t = 0, the upper compartment was filled with LY at 100 \(\upmu\)M. The devices were incubated at 37 \({}^{\circ}\)C for 30 min. At each time point (10, 20, and 30 min), the buffer contained in the lower compartment was collected with a syringe and immediately replaced by fresh buffer. At the end of the incubation, both compartments were sampled.  
Fluorescence intensities were analyzed by spectrofluorimetry (444 nm excitation and 538 nm emission wavelengths), and LY concentrations for each time point were extrapolated from a calibration curve. The endothelial apparent permeability (_Papp_) was calculated using the following equation [18]:  
\[Papp\left(\text{cm}\cdot\text{s}^{-1}\right)=\frac{\triangle CV}{\triangle t }\times\frac{1}{AC_{0}}\]  
where  
C: cumulative LY concentrations  
\(V\): lower compartment volume  
\(t\): incubation time  
Results are presented as mean \(\pm\) standard deviation. The percentages of recovery provide an indication about the nonspecific binding of the LY to cells and material. Recovery was obtained by dividing the sum of the LY concentrations at each time point by the initial LY concentration, and expressed as a percentage. Statistical significance was attested by Student T-test when \(p<0.05\).
## 4 A Hairy Root Platform for the Production of Bioactive Abietane Diterpenes  
HRs are adventitious roots obtained at plant-wounded sites by the infection of _Rhi-zobium rhizogenes_ (previously denominated _Agrobacteriumium rhizogenes_), a Gram-negative soil bacterium. HRs are differentiated organs, which grow under hormone-free culture conditions, and are considered a valuable and stable source of plant bioactive compounds more than plant cell cultures, which are frequently biochemically variable and unable to produce sufficient quantities of bioactive secondary metabolites, [23].  
Stable massive cultures of _S. sclarea_ HRs were obtained in our laboratory by customizing the growth media and conditions [9], in which the metabolic flux was modified toward a higher synthesis of aethiopinone and other abietane diterpenoids, as schematized in Figure 3.  
Figure 3: Enhancing abietane diterpene content of _S. sclarea_ by coupling hairy root technology and different elicitation or metabolic engineering approaches. (**a**) Stable hairy root cultures obtained by transformation with _R. rhizogenes_; (**b**) elicitation with methyl jasmonate or coronatine and different metabolic engineering approaches used to increase the accumulation of abietane diterpenes, as phenotypically visible from the red color of the hairy roots and the methanol extracts; (**c**) representative HPLC-DAD chromatograms of the main abietane diterpenes identified in control _S. sclarea_ HRs (upper panel) and HRs elicited with MeJA for 1 week (lower panel) (**d**). The photos and the chromatograms are original and used by the authors for this review.  
Boosting the Biosynthesis of Abietane Diterpenes in _S. sclarea_ HRs by Elicitation and Metabolic Engineering  
In the last decade, our efforts were focused on trying to elucidate the metabolic pathway of abietane diterpenes in _S. sclarea_ and to identify potential limiting enzymatic reactions and precursors/intermediates that might affect their accumulation in roots. This information was gathered by designing complementary strategies of elicitation and metabolic engineering to modify the expression of genes encoding known, unknown limiting or competitive enzymatic reactions of the MEP-pathway to boost the metabolic flux toward a higher synthesis of aethiopinone and other abietane diterpenoids, as recapitulated in Table 2.
Contrastive learning explores numerous views of input images through the utilization of data augmentation techniques. It subsequently learns inherent representations by maximizing the similarity between views originating from the same sample while minimizing the similarity between views from distinct samples. This technique is widely employed in healthcare data analysis, visual comprehension, and natural language processing [24; 29; 30], but it has been underexplored for remote sensing time-series analysis [31]. This research aims to develop a general framework for inherent time-series feature representation from incomplete satellite image sequences to improve crop classification. This method was implemented by combining contrastive-learning-based feature representation with machine-learning-based classifications. Compared to previous approaches, this study combines three principal contributions. The first contribution involves a contrastive-learning-based framework for time-series feature representation from incomplete satellite image sequences. The second is developing a type-wise consistency augmentation and type-wise contrastive loss to enhance contrastive learning for supervised time-series classification. The third is an in-depth analysis of the effect of contrastive-learning-based feature representation. The proposed method is further discussed and validated through parcel-based time-series crop classifications in two study areas (one in Dijon of France and the other in Zhaosu of China) with Sentinel-2 image sequences in comparison to existing methods.
# Ground Target Detection and Damage Assessment by Patrol Missiles Based on YOLO-VGGNet
## 4 Target Damaged Components Analysis Based on Convolutional Feature Clustering
### Convolutional Feature Clustering for Target-Damaged Components
#### 4.3.1 Guided Backpropagation for Target-Damaged Components  
The radar image is divided into 14 \(\times\) 14 grids corresponding to 14 \(\times\) 14 units of the feature map to generate a mask for the radar antenna, as shown in Figure 9. The units in the mask are summed as the activation intensity of the radar antenna component. As shown in (11), the activation intensity of the 512 masks in this layer are sorted to derive the clustered convolution kernels for the radar antenna \(K_{a}=[443,232,12,384,341,409,300,371,293,498]\). The visualization results are shown in Figure 10. Twenty radar images were tested, and the frequency distribution of the radar antenna and bracket is shown in Figure 11. The clustered convolution kernels, for the radar bracket, are \(K_{b}=[378,31,214,115,268,22,211,299,125,148]\). The test results are consistent with the data in Table 1.  
\[\mathcal{G}_{d}=(\mathcal{V}_{d},\mathcal{M})=\mathcal{V}_{d}*\mathcal{M}, \tag{11}\]  
\begin{table}
\begin{tabular}{c c} \hline
**Types** & **Feature Maps of the Top 10 Activation Intensity (from Large to Small)** \\ \hline Radar & 31, 115, 125, 22, 214, 148, 378, 374, 421, 268 \\ Tent & 374, 101, 23, 443, 470, 384, 448, 202, 414, 371 \\ Tank & 31, 22, 125, 115, 378, 421, 420, 148, 268, 174 \\ Supply vehicle & 31, 125, 115, 22, 378, 214, 421, 148, 174, 3 \\ \hline \end{tabular}
\end{table}
Table 1: Activation intensity ranking of feature maps for different types of targets.  
Figure 8: The frequency distribution of different radar targets. (**a**) The frequency distribution of 512 convolution kernels. (**b**) The frequency of clustered convolution kernels.  
where \(\mathcal{M}\) indicates the mask of the radar antenna. Only the units in the mask area are 1, and the rest are 0. \(\mathcal{V}_{d}\) is the d-th feature map. \(\mathcal{G}_{d}\) is the sum of the masked area units in the d-th feature map. The deconvolution visualization of the target's components is shown in (12).  
\[Deconv(\delta(W^{c}_{|I+1}|k_{i}|\mathcal{G}_{k_{i}}))\quad(i=1,2,3,4,5,6,7,8,9, 10), \tag{12}\]  
where \(k_{i}\in K\), and \(K\) is the clustered convolution kernel vector.  
We also test the degree of feature entanglement between a target's different components and find that this layer's feature entanglement is relatively tight. The relevant components can contribute to each other's activation intensity in the feature extraction process.  
Figure 10: Visualization of the extracted features by the radar antenna clustered convolution kernels.  
Figure 9: Radar antenna mask.
## Abbreviations  
\begin{tabular}{l l} \(A_{m}\) & area of membrane (m\({}^{2}\)) \\ \(b\) & width of masked edge (m) \\ \(d\) & distance between the absorbent and membrane (m) \\ \(D\) & diffusion coefficient (-) \\ \(J_{\text{H}_{2}\text{O}}\) & mass flux of H\({}_{2}\)O (g/m\({}^{2}\)s) \\ \(J_{\text{N}_{2}}\) & mass flux of N\({}_{2}\) (g/m\({}^{2}\)s) \\ \(\Delta m_{\text{CaC}_{2}}\) & mass variation of CaCl\({}_{2}\) (g) \\ \(\Delta m_{\text{N}_{2}}\) & mass variation of N\({}_{2}\) (g) \\ \(M_{\text{H}_{2}\text{O}}\) & molar mass of H\({}_{2}\)O (g/mol) \\ \(M_{\text{N}_{2}}\) & molar mass of N\({}_{2}\) (g/mol) \\ \(p\) & pressure of test cell (kPa) \\ \(p_{atm}\) & atmospheric pressure (kPa) \\ \(p_{s}\) & water saturation pressure (kPa) \\ \(p_{v}\) & water vapor pressure (kPa) \\ \(\Delta p\) & pressure difference (kPa) \\ \(P_{var}\) & corrected water vapor permeance (mol/m\({}^{2}\)sPa) \\ \(P_{\text{H}_{2}\text{O}}\) & water vapor permeance (mol/m\({}^{2}\)sPa) \\ \(P_{\text{N}_{2}}\) & nitrogen permeance (mol/m\({}^{2}\)sPa) \\ \(P_{still~{}air}\) & resistance due to still air (mol/m\({}^{2}\)sPa) \\ \(R_{\text{H}_{2}\text{O}}\) & ideal gas constant of H\({}_{2}\)O (kJ/kgK) \\ \(R_{\text{N}_{2}}\) & ideal gas constant of N\({}_{2}\) (kJ/kgK) \\ \(S\) & parameter by four times the test area divided by the perimeter (m) \\ \(\Delta t\) & time variation (s) \\ \(T\) & temperature (\({}^{\circ}\)C) \\ \(V\) & volume of the cell (m\({}^{3}\)) \\ \(\alpha\) & selectivity (-) \\ \(\delta\) & specimen thickness (m) \\ \(\sigma\) & error (\%) \\ \end{tabular}
# Resource Allocation in Downlink VLC-NOMA Systems for Factory Automation Scenario
## 4 Evaluation and Analysis  
In this section, to evaluate DBPA in the downlink VLC-NOMA, simulation and numerical results are discussed. In the first subsection, the BER performance of the downlink VLC-NOMA system is evaluated to create datasets correctly. In the second subsection, DBPA is evaluated in terms of the average BER compared with other power allocation algorithms.
###### Abstract  
Thermal control coating is an important means of ensuring that a spacecraft remains operational at high temperatures. Due to limitations regarding preparation technology and material properties, the mechanical properties of the conventional thermal control coatings still need to be improved. To solve this problem, nanostructured alumina coatings (NCs) and conventional alumina coatings (CCs) were prepared using plasma-spraying technology. The microscopic morphology, phase structure, hardness, and thermal control properties (solar absorptance (\(\alpha_{\mathrm{s}}\)) and emissivity (\(\epsilon\))) of the nanostructured alumina coatings were investigated and compared with those of conventional alumina coatings. The results show that the NC has a higher hardness value (1168.8 HV) and that its reflectivity exceeds 75% in the wavelength range of 446-1586 nm, while a high degree of emissivity of 0.863-0.87 is still maintained at 300-393 K. Furthermore, the results show that these highly reflective properties are related to the phase composition and internal micromorphology of the NC, whereby the solar absorption of the coating is reduced due to the increase in the alpha phase content (21.4%), the high porosity (5.21%) and the nanoparticles favoring the internal scattering. All these properties can improve the performance of this CC coating with low solar absorptance (\(\alpha_{\mathrm{s}}\)) and high emissivity (\(\epsilon\)).  
plasma spraying: alumina; nanostructured; thermal control property +
Footnote †: journal: Physics Letters B
# A Semi-Random Elliptical Movement Model for Relay Nodes in Flying Ad Hoc Networks
## 6 Conclusions  
This study introduced the SREM model, a mobility framework tailored to enhance relay-centric communication in FANETs. By aligning the major axis of an ellipse with the intended communication path and guiding UAVs to move between randomly selected points along the elliptical boundary, SREM achieves an effective balance between randomness and structural alignment. A Monte Carlo-based empirical analysis confirmed that this approach naturally concentrates nodes along the relay path, forming stable communication routes.  
Figure 9: Sensitivity analysis of the semi-major axis parameter \(a\) in the SREM mobility model.  
Simulation results conducted in NS-3 demonstrated that SREM consistently outperformed existing mobility models across key performance metrics, including PDR, EED, and CT. These findings underscore SREM's suitability for structured relay-based communication scenarios and its value in repeatable and controlled simulation experiments.  
However, the structured nature of SREM also introduces inherent limitations. The model's trajectory rigidity restricts its adaptability in dynamic environments, making it less appropriate for swarm-based cooperative tasks or scenarios requiring precise inter-UAV synchronization. Moreover, SREM does not incorporate essential real-world UAV constraints such as collision avoidance, flight dynamics, or acceleration limits--factors critical for practical deployment. As a result, while SREM serves as a powerful tool for evaluating relay communication under idealized conditions, its direct applicability to real-world operations remains constrained.  
Recent advancements in edge computing and lightweight deep learning models have enabled real-time visual-based trajectory adaptation and anomaly detection using UAV-mounted sensors [38]. Future research should focus on integrating such edge-intelligent control capabilities into the SREM framework, allowing UAVs to dynamically adjust their paths in response to changing environments. Furthermore, in swarm-based network scenarios involving cooperative UAV operations, extending SREM to support distributed decision-making and collaborative control could establish it as a foundational mobility framework for next-generation FANET systems [39].  
Conceptualization, H.C.; methodology, H.C.; software, H.C.; validation, H.C. and D.K.; writing, H.C. and D.K.; visualization, H.C.; supervision, D.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.  
The authors declare no conflicts of interest.
## 4 Results and Discussion
### EEG Data Results
#### 4.2.1 Motor Imagery Results
Since the G. II includes all the subjects with good classification performances, we can conclude that the information that allows to satisfactorily classify the left and right hand MI tasks from TE\({}_{\text{xx}}^{\theta}\) features corresponds mostly to the incoming and outgoing information flow coded in the phases of the oscillatory activity in the centro-parietal region. These results are in line, in terms of spatial location, with those we found in [10], and with physiological interpretations that argue that MI activates motor representations in the parietal areal and the premotor cortex [64]. Figure 8: (**A**) Two-dimensional representation of the relevance vectors for each subject in the MI database obtained after applying t-SNE on \(\mathbf{q}_{n}\). (**B**) Groups identified by k-means. For the TE\({}_{\text{xx}}^{\theta}\)-based classifier the subjects grouped in G. I have an average accuracy of \(0.59\pm 0.05\), while those in G. II have an average accuracy of \(0.80\pm 0.09\).
## 3 Results and Discussion
### Heavy Metals Content in Raspberry Leaves  
The content of heavy metals (As, Cd, Pb, Al, Cr, Zn, Co, Mo, Cu, Mn, V, B, Sr, Sb, Ti, Tl) in raspberry leaves from conventional, organic and wild crops and two years of cultivation are presented in Table 2.  
The raspberry leaves from the investigated crops were characterised by various heavy metals contents. The results obtained showed that of the analysed samples, leaves of wild-growing raspberries had the highest amounts of cadmium, lead, zinc, manganese and vanadium. The As content in the leaves was lower for all crops in the second harvest year, and these results are statistically significant compared to the previous harvest year. As for arsenic content, the results obtained by other authors were higher [28] compared to our findings. The Cd content in wild-growing raspberry leaves was considerably higher than that of other crops. The raspberry leaves from the organic farming, harvested in 2021, also had more cadmium, by 211% compared to the previous year. This is congruent with the findings of other authors [41], who have also observed higher contents of this element. The levels of lead and cadmium determined by [PERSON] et al. [42] were significantly higher than our results and were 5.76 and 2.91 mg kg\({}^{-1}\), respectively. In [PERSON] et al. [43], the cadmium content was 1.44 mg kg\({}^{-1}\) DW in raspberry leaves (_Rubus ideas_ L.), which is a value similar to the value obtained in this study in wild raspberry leaves. Low results were obtained in the study of [44], which ranged within 0.0007-0.0029 mg kg\({}^{-1}\) DW. Wild-growing and organic raspberry leaves harvested in 2021 contained much more lead, which was statistically significant--compared to specific crops and varieties. Compared to our results, the authors reported a higher Pb content in raspberry leaves (1.6 mg kg\({}^{-1}\)) than the highest result obtained [28,41]. Other authors also obtained lower results [43,44]. As for zinc, its content in the leaves of wild-growing raspberry was significant. The content of zinc, determined in a control sample by [PERSON] et al. [33], was 43.69 mg kg\({}^{-1}\), which means that the result was close to that of conventional raspberries harvested in 2020. A higher content (90.3 mg kg\({}^{-1}\) DM) was reported by other authors [28], which was an approximate result for wild-growing raspberry from the 2021 harvest.
# Planning of Optical Connections in 5G Packet-Optical xHaul Access Network
## 5 Numerical Results
Eventually, in Figure 7, we show the results of the number of transmission paths (left chart) and the relative performance gain versus reference scenario noOADM (right chart) in a function of traffic load (\(\rho\)) in MESH38 for different MUX loss values (\(A_{\text{MUX}}\)) and assuming \(k=7\) candidate paths. We can see that, under low traffic loads (\(\rho\leq 1.5\)), the use of MUXs with lower attenuation results in a lower number of required transmission paths. In this case, higher transmission distances are allowable and more OADMs can be used on the paths (as for \(A_{\text{MUX}}=1.44\) dB), which, under a low demand for wavelengths (at  
Figure 5: The number of transmission paths (**left**) and overall length of paths (**right**) in a function of traffic load (\(\rho\)) in WRO17; relative gain versus the network without OADMs is shown as lines. Figure 6: The number of transmission paths (**left**) and relative difference versus a single-path scenario (**right**) in a function of traffic load (\(\rho\)) and the number of candidate routing paths (\(k\)) for different MUX loss values in MESH38. most 2 wavelengths per remote node), allows for higher aggregation of demands, as also discussed in the above remarks concerning Figure 6. At higher traffic loads (\(\rho\geq 2.0\)), the gains from using low-loss MUXes are either none or negligible in the 4-channel WDM system considered, due to its saturation with the carried wavelengths. The gains from using OADMs are between 52% and 65% for \(\rho=1.0\), depending on the MUX scenario, and are decreasing up to 0%, which is reached at a high load for \(\rho=3.5\).