esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Data Transmission Tests  
The performances of the LoRaWAN network infrastructure for the _SeaFactory_ project were validated by sorting out a measurements campaign hosted by the Agroittica Toscana SRL aquaculture firm. In particular, the Gateways were installed in the ashore plant owned by that company while the end node was placed on board of a boat with which the closeness of the offshore cages was reached. There, several packets were transmitted, by employing six different SFs (i.e., \(7,8,\ldots,12\)), and their data and metadata were sampled. Moreover, the Coding Rate (CR) was set to \(4/5\) and a bandwidth of 125 kHz was exploited.
## 5 Discussion and Implications
### Key Insights from Experimental Results  
The comparative evaluation reveals the distinct strengths and limitations of the tested models:  
* XCeption: This model consistently outperformed others across metrics, achieving high accuracy (89.2% on DFDC and 85.7% on FaceForensics++) and robust generalization. With a relatively low inference time (85 ms per frame), XCeption is well suited for real-time applications such as social media monitoring or live video analysis. Its depthwise separable convolutions effectively capture subtle artifacts, providing a strong balance of speed and accuracy. Similar trends were reported in other studies [11], where XCeption often topped the benchmarks on manipulated-face datasets, confirming its efficacy in recognizing fine-grained inconsistencies.
* VGG16: Despite its slower inference speed (1020 ms per frame), VGG16 demonstrated strong precision and recall, maintaining an F1-score of 87.0% on DFDC. These attributes make it ideal for forensic and investigative settings where accuracy outweighs speed. However, its high computational and memory requirements limit its scalability for dynamic, resource-constrained scenarios. Comparable accuracy ranges for VGG16 (above 85%) were also observed by [PERSON] et al. [19], reinforcing that it remains a robust model for deepfake detection, albeit at the cost of efficiency.
* ResNet-50: Offering faster inference (270 ms per frame), ResNet-50 represents a viable option for environments with limited computational resources. However, its lower accuracy (72.8% on DFDC) and poor generalization under zero-shot conditions indicate limited suitability for high-precision tasks without fine tuning. Nevertheless, targeted retraining or ensemble integration could alleviate these deficits, as seen in prior work [20], where ResNet-based ensembles yielded better detection performance than standalone models.
* MobileNet V2: Optimized for edge devices, MobileNet V2 achieved the lowest memory usage (800 MB) and inference time (60 ms per frame). While its accuracy (80.2%) is acceptable for lightweight applications, its heightened susceptibility to adversarial attacks reduces reliability in security-sensitive contexts. This vulnerability also aligns with findings in [19] indicating that smaller architectures, while efficient, can be more prone to targeted adversarial perturbations.
## 3 Results and Discussion
### Phytochemical Characterization  
Plant polyphenols present at least 8000 distinct known structures, being the most important class of natural bioactive compounds, which exhibit various biological activities [22]. Honey presents three classes of flavonoids with analogous structure: flavonols, flavones, and flavanones. Flavonoids are responsible for the color, taste, and flavor of the honey and they also improve its beneficial health effects [22]. Furthermore, the floral sources used by bees to produce honey, whose predominance depends on seasonal and environmental issues, influences the phenolic composition and antioxidant activity of honey.  
The results of the phytochemical characterization of the samples regarding total phenolics and flavonoids contents are presented in Table 1.  
Figure 1: FTIR spectra of the samples.  
Total phenolic compounds content of honey samples ranged from 0.029 to 0.107 g GAE/100 g sample, the values observed for propolisik extracts ranged from 21.747 to 28.947 g GAE/100 g sample, and finally for the mixtures of honey with propolisis ranged from 1.219 to 3.506 g GAE/100 g sample. The honey that presented the highest content in phenolic compounds is Honey 1 and the lowest content can be found in Honey 3. These differences may be related with the different seasons in which the honeys were produced, as mentioned above.  
Propolis extracts showed a much higher concentration of phenolic compounds than honey samples, with the highest content found in Propolis Extract 1 followed by Propolis Extract 3 and Propolis Extract 2. An increase in phenolic content was observed with the addition of higher concentrations of propolis extracts to honey, and the highest value was obtained with H1 PE3 at 0.5%.  
Flavonoids were almost absent from honey samples. The only one that presents flavonoids in its composition is Honey 1, but even this sample has a very low content. In contrast, the flavonoids determined in the propolisik extracts ranged from 1.786 to 5.494 g QE/100 g sample, and in the mixtures ranged from 0.054 to 0.452 g QE/100 g sample. Propolis extracts showed a higher concentration of flavonoids than honey samples, with the highest content in Propolis Extract 1 followed by Propolis Extract 3 and Propolis Extract 2. An increase in flavonoid content was observed in all samples with the addition of higher concentrations of propolisis extract to honey, as expected.  
The values of total phenolic compounds and flavonoids determined in the present work are very similar to the ones obtained for selected Czech honeys [23].
## 4 Results and Discussion
### CEEMDAN Decomposition Results
The prediction accuracy of the model for each component decreased progressively from the residual component to IMF\({}_{3}\), IMF\({}_{2}\), and IMF\({}_{1}\). The shorter the data fluctuation cycle, the more pronounced the lag effect in the model's predictions. The IMF\({}_{1}\) component exhibited intense fluctuations with a cycle of 2-3 steps, which increased the difficulty of predictions for deep learning models. However, since the main fluctuation range of this component was between \(-3.5\) mm and \(3\) mm, its impact proportion relative to the maximum predicted subsidence of \(-84.07\) mm was less than \(4.2\%\). The IMF\({}_{2}\) component had an average fluctuation cycle of nine steps, with most of the predicted results fitting well with the observed data. The IMF\({}_{3}\) component had a fluctuation cycle of about \(30\) steps, and its data characteristics, similar to a quadratic function, were more conducive to model prediction. Based on the value ranges of the IMF\({}_{2}\) and IMF\({}_{3}\) components, their impact on the final prediction results was within \(3.5\%\). The residual component exhibited a near-linear trend, and its simple data characteristics allowed the prediction model to more easily capture the trend in data changes. This component's value range was between \(-61.38\) mm and \(-81.69\) mm, impacting more than \(90\%\) of the final prediction results. Thus, in the application of the proposed model, the residual component played a dominant role in influencing the final prediction accuracy, while the importance of the three IMF components was relatively weaker.
## 3 Experimental Verification  
We demonstrated the graffiti detection technique by applying it to a real-world structure exhibiting various types of graffiti. A target structure, the Church of Agios Nikolaos, Kantza, Greece, is a cultural heritage building. Since we noticed that its exterior condition is one of the major interests for maintenance, we regarded the Church of Agios Nikolaos as the TOC for verification of the proposed method through this case study. As a preprocessing step, a graffiti detector was trained using a graffiti data set established within the STORM project. Then, the technique was demonstrated on our TOC, where several graffiti markings had already been placed. For this implementation, visual data were collected twice within an eight-month interval to highlight the capability of structure monitoring.
# An Energy Efficient Modulation Scheme for Body-Centric Terahertz (THz) Nanonetworks +
## 3 Trade-Off between Energy Consumption and Data Rate  
Next, we compare the performance of the new modulation scheme to TS-OOK, in terms of energy efficiency and data rate, taking into account the modulation order of SPVD.
## 3 Results and Discussion
### Bread Characterization
#### 3.2.3 Texture  
As color, bread texture is a determinant attribute to define the quality of bread and the acceptance of this product. The hardness of control, B50 and B100 bread samples is represented in Figure 5. For the three samples, no significant difference was observed in comparison with the control bread. This finding agreed with the results of [PERSON] et al. [23] where no significant difference of hardness was observed in crackers with incorporation up to 10% of _S. perennis_ in comparison with the control cracker. However, [PERSON] et al. [29] referred that the hardness value decreased in bread samples prepared with the high level of Salicornia powder (1.8%), as compared with the hardness of bread with 1.8% sodium chloride. This different behavior may be due to the presence of other factors, such as the amount of powder halophyte, baking time, temperature.  
Figure 4: Color parameters lightness (L*), greenness (a*), yellowness (b*) of bread without _S. perennis_. CB—control bread (1.2% NaCl/0.47% sodium, flour basis); B50—0.235% sodium (flour basis) obtained from _S. perennis_ powder and B100—0.47% sodium (flour basis) obtained from _S. perennis_ powder. Sample breaks with different letters on the same color parameter are significantly different (one-way ANOVA, \(p\) < 0.05).  
Figure 5: _Cont._  
Springiness, cohesiveness and resilience of bread samples were not affected by the replacement of sodium chloride by _S. perennis_ powder (Figure 4). Similarly, [PERSON] et al. [29] observed that these texture parameters were not affected by salt amount or NaCl/Salicornia ratio in bread preparation.  
However, NaCl reduction with chloride potassium resulted in bread with higher firmness [38].
# Joint Radar-Communication Optimization of Distributed Airborne Radar for AOA Localization
###### Abstract  
Compared to the distributed ground-based radar (DGBR), the distributed airborne radar (DAR) has been widely applied due to its stronger anti-damage ability, more degrees of freedom, and better detection view of targets. However, unlike DGBR, the premise for the normal operation of DAR is to maintain stable wireless communication between unmanned aerial vehicles (UAVs). This requires each UAV to make reasonable use of its electromagnetic domain resources. That is, to maximize radar detection performance while ensuring communication performance constraints. However, current research in the field of radar resource allocation has not taken this into account, which greatly limits the practical application of optimization algorithms. Moreover, the current research tends to adopt centralized optimization algorithms. When the baseline of the UAV swarm is long, applying multi-relay methods directly results in heavy communications overhead and long-time delay. Based on the above background, this article aimed to develop a fully distributed algorithm for the joint optimization of radar detection performance and communication transmission performance. This study first took the measurement angle of arrival (AOA) as an example to provide a system model with communication constraints. This model considers the impact of factors such as the UAV location error, UAV communication coverage, and dynamic communication topology of the UAV on joint optimization. A formal representation of the joint optimization is presented. Then, we proposed a joint radar-communication optimization (RACO) algorithm to fully utilize the electromagnetic domain resources of each UAV. Finally, numerical simulations verified the effectiveness of the proposed JRCO algorithm to traditional radar resource allocation methods.  
distributed airborne radar (DAR); angle of arrival (AOA); unmanned aerial vehicle (UAV); joint radar-communication optimization (JRCO) Article
# A Web of Things-Based Emerging Sensor Network Architecture for Smart Control Systems
## 5 Performance Evaluation
### Simulation Setup  
A smart home architecture along with RESTful framework, MS, smart gateway, and a web server is simulated as shown in Figure 1. The MS and the smart gateway are installed on a Core I3 CPU 3.60 GHz computer. The RESTful framework and web server is placed on a separate computer equipped with the Windows Server 2008 operating system. The entire smart home scenario is tested in C# programming language. Each household appliance is attached with a ZigBee sensor. A sensor is a programmingfunction which follows the IEEE 802.15.4 protocol architecture for communication. The coordinator node follows the structure of the Full-Function Device (FFD) that serve as the coordinator of a personal area network. The rest of the nodes are considered as Reduced-Function Devices (RFD) that are capable of modest resources and communication requirements. A star topology is followed by considering one FDD node in each room and kitchen attached to multiple sensors. The IEEE 802.15.4 does not define a network layer, therefore, we made the modification for routing the data following a multi-hop environment. Initially, each sensor node immediately connects with the coordinator and the coordinator is responsible of collecting and sending data to the MS. Similarly, the MS is enabled to follow a multi-hop routing strategy of IEEE 802.11n standard. An automatic topology adaptation is introduced to take care of the changes made in the network. For example, if a new node joins the network, the topology will change automatically to adapt to the arrival or depart of a node. Thus, it makes the system more dynamic and adaptive and it is able to handle a large number of nodes joining and leaving the network at once. Similarly, the sensing and controlling appliances functionality are added with different functions, i.e., collecting the data after a specific interval of time. In addition, we are not interested in the energy consumption of the sensors. Moreover, the RESTful framework operates as a separate program instead of installing it on each sensor. Therefore, the sensor nodes require energy only for sensing and transmitting information to the coordinator available at the shortest distance.
## 4 Numerical Simulation
### Effect of Stiffness Ratio on the Output Power  
Figure 6 depicts the effect of stiffness ratio \(\alpha\) on the output power of the HEH. For a given \(\mu\), the first resonant frequency increases as \(\alpha\) increases, while the magnitude of the first peak initially decreases, then increases, and finally reduces to a smaller value. It also shows that \(\alpha\) has almost no effect on the second resonance. When \(\alpha\) remains unchanged, the larger \(\mu\) is, the more the magnitude of the first peak increases. This conclusion is identical with that obtained before. Therefore, the stiffness ratio can also be used to alter the performance of the HEH.
## 3 Sensing Per Crop
### Sensing Citrus Diseases
#### 3.3.1 Sensors and Algorithms
In studies of general vegetation spectral reflectance, the near-infrared (IR) band (0.75 \(\upmu\)m-1.35 \(\upmu\)m) is important in detecting healthy and stressed trees based on leaf air content; healthy leaves with more air and a thicker mesophyll increase near-IR scatter [183]. In the visible region of the spectrum, especially at 0.45 \(\upmu\)m, reflectance of citrus (Valencia orange) leaves was found to be influenced by leaf water content, chlorophyll content and leaf air volume [184], more than by leaf thickness (Table 4). These positive forecasts of citrus production in Florida, however, face threats from virus and insect infestations. The 2002 Commercial Citrus Inventory showed a 4.2% decline in total citrus area coverage from 2000 due in part to diseases such as citrus canker (_Xanthomonas axonopodis pv. citri_), tristeza (Citrus Tristeza Virus). Little leaf notcher (_Artipus floridanus_), and the Diappepes root weevil (Diappepes abbreviatus) are of economic significance to citrus growers [184].
## 3 Results
### Annual-Scale Contrast of GPP Estimations  
Annual GPP_EC in this alpine wetland was range from the minimum 755.02 g C m\({}^{-2}\) in 2011 to the maximum 901.37 g C m\({}^{-2}\) in 2010 (Figure 5a). The GPP_MOD had strong underestimation in annual GPP accumulation that was only 27.98% in 2010 to 34.69% in 2011 of GPP_EC measurements. The GPP_MODR1 not only had no improvement in GPP estimation but showed a slight decrease with mean 6.6% of GPP_EC compared with GPP_MOD (Figure 5b). The GPP_MODR1 improved 7.2% predictive capacity compared to the GPP_MOD, actually the impact was not significant (Figure 5b). In contrast with the GPP_MOD, the GPP_MODR4 had a significant, 31.3% of GPP_EC, promotion in GPP estimation, but there still existed significant difference between GPP_MODR4 and GPP_EC (Figure 5b). Statistical results showed that there was no significant difference between GPP_MODR3 and GPP_EC, which indicated that the GPP_MODR3 was the most instrumental estimation methods in annual GPP accumulation.  
Figure 4: Linear regression between EC-based GPP estimation (GPP_EC) and MODIS-based GPP estimation. GPP estimations based on the MOD17A2 algorithm are showed in Table 1. (**a**), GPP_MOD; (**b**), GPP_MODR1; (**c**), GPP_MODR2; (**d**), GPP_MODR3; (**e**), GPP_MODR4. Data are the 8 days composited GPP values. The solid lines are the regression curves, the coverages are 95% confidence interval of linear regressions. Dash lines are the 1:1 reference lines.
# Performance of Ship-Based QKD Under the Influence of Sea-Surface Atmospheric Turbulence
###### Abstract  
There are numerous studies on the effects of ocean turbulence on wireless optical communications and on satellite-ground quantum key distribution (QKD); however, there are few studies on the impact of near sea-surface turbulence on ship-based QKD performance. This study investigates the influence of sea-surface atmospheric turbulence on laser beam propagation, atmospheric scintillation, and aiming error. It focuses on the impact of sea-surface turbulence on the performance of satellite-to-ship QKD and ship-to-ship QKD systems. The results show that strong turbulence severely degrades QKD system performance; horizontal transmission distances change from 32 km (weak turbulence) to 3 km (strong turbulence) in ship-to-ship QKD.  
sea-surface; atmospheric turbulence; quantum key distribution; security keys +
Footnote †: journal: _photonics_  
_Article_
###### Abstract  
The penetration of optical clearing agents (OCAs) is restricted by the natural barrier function of the stratum corneum (SC) of the skin, which can be breached by physical and chemical methods to enhance the transcutaneous delivery of OCAs. To breach the barrier function of SC, we carried out the in vivo experimental study to enhance the optical clearing effect of PEG-400 by laser irradiation in conjunction with a chemical penetration enhancer thiazone. We compared the reflectance spectra of skin without laser irradiation or thiazone. Mono-treatment of thiazone could not significantly enhance the optical clearing efficacy of the skin. After 60 min, the reflectance spectrum decreased by only approximately 10%. With the combined treatment, the reflectance spectrum decreased by approximately 30% after 10 min. Subsequently, the effect of laser dose on the enhancement of optical clearing efficacy was studied. The optimal irradiation dose was determined. The reflectance of skins irradiated by a laser dose at 0.7 J/cm\({}^{2}\) decreased by approximately 10% and were 20% lower than those at 0.5 and 0.9 J/cm\({}^{2}\). The laser at 0.5 J/cm\({}^{2}\) could not damage the SC completely, whereas the laser at 0.9 J/cm\({}^{2}\) influenced the epidermis and dermis; thus, the reflectance of skin samples irradiated by 0.9 J/cm\({}^{2}\) did not decrease.  
optical clearing; tissue optical clearing technique; laser irradiation; thiazone; in vivo skin; combined penetration enhancement +
Footnote †: journal: Applied Physics Letters
## 4 Discussion  
To make an exhaustive comparison in terms of behavior factors, an evaluation of q was carried out according to the Italian design code NTC18 as:  
\[q=q_{0}\cdot K_{R}=2.975\ \text{-}0.8=2.38 \tag{3}\]  
where \(q_{0}\) is the initial value of the behavior factor, which depends on the ductility class and the type of the structure, and \(K_{R}\) is a coefficient which considers the regularity of the structure. The obtained value is valid for both the X and Y directions.  
Table 10 reports all values of \(q\) obtained with the different approaches, while Table 11 shows a comparison in terms of collapse scale factor. Figure 15 shows a comparison in terms of the capacity curves for the modal load profile.  
The FE model that better approximates the structure of the tower is clearly the solid model, which is associated to the highest computational cost and whose results will be considered as a reference in the comparison, since it represents the closest solution to reality.  
The comparison in terms of the behavior factor shows that Italian Technical Standards (NTC18) tends to significantly overestimate the ductility of the tower. The values obtained with the fiber FE model are instead lower than the solid ones in both directions. This is due  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Approach** & \multicolumn{3}{c}{\(\boldsymbol{q}\)} \\ \cline{2-3}  & \(\mathbf{X}\) & \(\mathbf{Y}\) \\ \hline NTC18 & 2.38 & 2.38 \\ Fiber FEM & 1.37 & 1.39 \\ Solid FEM & 1.67 & 1.83 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison of the behavior factors evaluated with the different methods.  
Figure 15: Comparison of the capacity curves obtained from the fiber and solid FE models in both the X (**a**) and Y (**b**) loading directions.  
to the main assumptions underlying the fiber approach, which approximates the geometry (e.g., openings are not modelled) and, more important, neglects the shear behavior. This last hypothesis makes the fiber models more suitable for tower with high slenderness, where the flexural behavior is predominant.  
In the solid FE model, a slight difference is observed for q in the X and Y directions due to the asymmetry of thicknesses and openings distribution in the perimetral walls. This difference is less evident in the case of the fiber model, since the approach does not allow the modelling of openings.  
The scale factors of the equivalent static lateral loads \(\lambda\) obtained with the shell and solid models are comparable and, in both cases, a damage distributed near the openings or on the vaults was observed.  
The comparison in terms of capacity curves shows a maximum value of base shear of about 5000 kN for both X and Y directions and in both the solid and fiber models. However, the solid model shows a higher ductility with respect to the fiber one, which is due to the main simplification hypotheses made by the fiber approach, as previously described.