esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
To deploy our proposed Hybrid MARL framework in real-world urban traffic systems, we would need to integrate real-time traffic data from sensors and traffic cameras. This could be achieved using existing infrastructure, such as smart traffic lights and vehicle detection systems. However, challenges such as data sparsity, sensor inaccuracies, and integration with existing traffic management platforms need to be addressed for successful deployment. Moreover, collaboration with local traffic authorities would be necessary to adapt our model to the specific requirements and constraints of the city's road network.  
In this study, we proposed a Hybrid Multi-Agent Reinforcement Learning (Hybrid MARL) framework for large-scale traffic signal control, integrating spatio-temporal attention networks, hierarchical reinforcement learning, and cooperative decision-making.  
Figure 7: Ablation study results.  
The framework effectively captures dynamic traffic dependencies by leveraging Graph Attention Networks (GATs) for spatial feature extraction and Long Short-Term Memory (LSTM) for temporal modeling. Furthermore, the introduction of a hierarchical learning structure with sub-region agents and a global coordinating agent significantly improves the scalability and stability of multi-agent reinforcement learning in complex traffic networks.  
Extensive experiments conducted on SUMO simulations demonstrate the effectiveness of the proposed framework. Hybrid MARL outperforms traditional rule-based methods and deep reinforcement learning baselines in key performance indicators, including average vehicle waiting time, throughput, and traffic flow stability. The results show that Hybrid MARL achieves a 25% reduction in waiting time compared to state-of-the-art reinforcement learning methods, while maintaining robust performance across different traffic densities. Additionally, the ablation study confirms the critical role of spatio-temporal attention and hierarchical coordination, showing that removing these components significantly degrades performance.  
The findings of this study suggest that Hybrid MARL has significant potential for real-world deployment in smart urban transportation systems. Future research directions include extending the framework to multi-modal transportation environments, integrating real-world sensor data, and exploring federated learning techniques to enhance decentralized optimization in large-scale intelligent traffic management.  
Writing--original draft, W.J. and M.J.; writing--review and editing, W.J. and M.J. All authors will be updated at each stage of manuscript processing, including submission, revision, and revision reminder, via emails from our system or the assigned Assistant Editor. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# An Advanced SAR Image Despeckling Method by Bernoulli-Sampling-Based Self-Supervised Deep Learning
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Data** & **Image** & **Index** & **PPB** & **SAR-BM3D** & **ID-CNN** & **SAR-DRN** & **SAR-RDCP** & **SSD-SAR-BS** \\ \hline \multirow{4}{*}{Sentinel-1} & & ENL & 17.6246 \(\pm\) 1.1437 & 22.9777 \(\pm\) 3.0547 & 17.8801 \(\pm\) 1.1527 & 17.7781 \(\pm\) 1.4860 & 15.4355 \(\pm\) 1.1983 & **35.2740**\(\pm\) 2.6669 \\  & \#1 & Cx & 0.2822 \(\pm\) 0.0081 & 0.2086 \(\pm\) 0.0154 & 0.2365 \(\pm\) 0.0080 & 0.2372 \(\pm\) 0.2267 & 0.2545 \(\pm\) 0.0105 & **0.1684**\(\pm\) **0.0067** \\  & & MoR & 0.9880 \(\pm\) 0.0004 & 1.0095 \(\pm\) 0.0006 & 0.9844 \(\pm\) 0.0012 & 0.9996 \(\pm\) 0.0008 & 0.9947 \(\pm\) 0.0009 & **1.0003**\(\pm\) 0.0006 \\  & & ENL & 31.3902 \(\pm\) 1.8247 & 35.2162 \(\pm\) 3.1990 & 18.5404 \(\pm\) 1.3791 & 18.6595 \(\pm\) 1.4495 & 14.3300 \(\pm\) 1.2413 & **42.0500**\(\pm\) **3.5104** \\  & \#2 & Cx & 0.1785 \(\pm\) 0.0054 & 0.1685 \(\pm\) 0.0082 & 0.2322 \(\pm\) 0.0092 & 0.2315 \(\pm\) 0.2219 & 0.2642 \(\pm\) 0.0122 & **0.1542**\(\pm\) **0.0069** \\  & & MoR & 0.9687 \(\pm\) 0.0006 & 0.9959 \(\pm\) 0.0019 & 0.9842 \(\pm\) 0.0003 & 0.9898 \(\pm\) 0.0009 & 0.9819 \(\pm\) 0.0007 & **1.0021**\(\pm\) **0.0003** \\ \hline \multirow{4}{*}{TerraSAR-X} & & ENL & 59.3150 \(\pm\) 3.1966 & 45.4120 \(\pm\) 3.9193 & 11.6108 \(\pm\) 0.8807 & 8.2538 \(\pm\) 0.5720 & 12.4337 \(\pm\) 0.8996 & **68.9492**\(\pm\) **4.8888** \\  & \#1 & Cx & 0.1298 \(\pm\) 0.0037 & 0.1484 \(\pm\) 0.0068 & 0.2936 \(\pm\) 0.0118 & 0.3481 \(\pm\) 0.3354 & 0.2836 \(\pm\) 0.0108 & **0.1204**\(\pm\) **0.0045** \\ \cline{1-1}  & & MoR & 0.9608 \(\pm\) 0.0014 & 0.9803 \(\pm\) 0.0013 & 0.9618 \(\pm\) 0.0012 & 0.9514 \(\pm\) 0.0002 & 0.9282 \(\pm\) 0.0010 & **0.9828**\(\pm\) **0.0008** \\ \cline{1-1}  & & ENL & 57.7479 \(\pm\) 2.9501 & 47.5173 \(\pm\) 5.0632 & 15.6736 \(\pm\) 0.9855 & 11.5313 \(\pm\) 0.8143 & 10.9757 \(\pm\) 0.8569 & **82.3089**\(\pm\) **5.6541** \\ \cline{1-1}  & \#2 & Cx & 0.1316 \(\pm\) 0.0035 & 0.1451 \(\pm\) 0.0084 & 0.2526 \(\pm\) 0.0083 & 0.2945 \(\pm\) 0.2835 & 0.3018 \(\pm\) 0.0126 & **0.1102**\(\pm\) **0.0040** \\ \cline{1-1}  & & MoR & 0.9736 \(\pm\) 0.0018 & 0.9880 \(\pm\) 0.0016 & 0.9600 \(\pm\) 0.0005 & 0.9625 \(\pm\) 0.0004 & 0.9590 \(\pm\) 0.0011 & **1.0119**\(\pm\) **0.0006** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Quantitative evaluation results on real SAR data. number of the average times \(K\) reduces, the runtime reduces significantly. Specifically, when \(K=40\), the runtimes of images with 64 \(\times\) 64 and 128 \(\times\) 128 pixels were reduced to about 0.25 and 0.75 s, respectively. The time consumption of this fast model was superior to those of the traditional methods (i.e., PPB and SAR-BM3D). This was similar to other deep-learning-based methods. In other words, after much time was needed to train the deep neural network, the testing process was speedy in the deep-learning-based methods, which is another advantage of our proposed SSD-SAR-BS.
# A Novel Drone Design Based on a Reconfigurable Unmanned Aerial Vehicle for Wildfire Management
## 2 PULSAR Unmanned Aerial System (UAS) Overview  
The UAV features a modular and reconfigurable design with options for quadcopter, octocopter, and quadcopter co-axial configurations (Figure 1). With eight telescopic arms, the UAV allows for manual and automated adjustments, offering flexibility for different missions. Various configurations provide options for mission profiles, offering advantages like improved battery life and smaller size for confined spaces. Special embedded sensors detect configuration changes, ensuring automated adjustments to the flight controller software, thus eliminating potential human error. The system supports user-selectable propeller sizes, adjustable UAV dimensions, and a companion computer for advanced AI algorithms.
## 4 Loss Analysis
### Switching Losses  
Switching losses, caused by the overlapping of current and voltage during the switching state change, can be calculated [9; 20]. During turn-on, the power loss is given by  
\[P_{S,j,lon}=\frac{1}{6}(f_{sw}V_{sw,j}I_{on,}\,i_{on}) \tag{12}\]  
Similarly, the switching loss due to turn-off can be calculated as  
\[P_{SW,j,toff}=\frac{1}{6}(f_{sw}V_{sw,j}I_{off,}\,i_{off}) \tag{13}\]  
where \(I_{on,\,i}\) and \(I_{off,\,i}\) signify the current flowing through the switches after the \(i\)th switch is turned on and before it is turned off, respectively. \(f_{sw,\,\,i}\) is the switching frequency, and \(V_{sw}\) is the reverse voltage across the switch.  
Thus, the total switching losses (\(P_{sw}\)) ofthe PT are calculated as follows:  
\[P_{sw}=\sum_{n=1}^{5}\sum_{i=1}^{12}\left(P_{sw,j,lon}+P_{sw,j,toff}\right) \tag{14}\]  
As a result, the PT's overall losses are composed of conduction and switching losses, as well as voltage ripple losses.  
\[P_{total\,\,loss}=P_{c}+P_{sw}+P_{rip} \tag{15}\]  
The PT's overall efficiency can be calculated as follows:  
\[\%\text{Efficiency}(\eta)=(P_{output}/P_{input})\times 100 \tag{16}\]  
\[\%\eta=\left(\frac{P_{output}}{P_{output}+P_{total\,\,loss}}\right)\times 100 \tag{17}\]  
where \(P_{output\text{ and }P_{input}}\) represent the power output and input of the proposed inverter, respectively.
## 3 Characterization and Performance Testing
### Property Test of the Asphal
#### 3.2.2 Dynamic Shear Rheometer Test (DSR)  
In order to characterize the fundamental rheological properties of asphalt film after different F-T cycles, the dynamic shear rheometer test was performed according to ASTM-D7175 standard test method. The DSR test can properly describe the elastic and viscous behaviors of asphalt film after different F-T cycles. In this paper, a Bohlin automatic dynamic shear (ADS) rheometer (DSRII, Malvern, United Kingdom) was used to investigate the rheological properties of the modified asphalt binder under freeze-thaw aging procedure. Complex shear modulus (G*) and phase angle (\(\delta\)) were measured at temperatures ranging from 58 \({}^{\circ}\)C to 76 \({}^{\circ}\)C at 6 \({}^{\circ}\)C increments for both asphalt binders, while the frequency equaled 1.59 Hz. The parameter G* provides information about the resistance of asphalt sample to deformation when it is subjected to shear loading. The parameter (\(\delta\)) shows time lag between the applied shear stresses and shear strain responses. The parameter G*/Sin \(\delta\) which is called the rut factor represents the rutting resistance of asphalt sample under a freeze-thaw aging procedure.
## 5 Performance Analysis and Optimization
### Performance of the Improved DDAR Scheme vs. the Common DDAR Scheme  
In this subsection, we propose a strategy to improve the service guarantee rate, lifetime and energy efficiency of DDAR.  
The value of \(N_{t}\) and \(T_{t}\) are set according to predetermined \(N_{t}(\overline{N_{t}})\) and predetermined \(T_{t}(\overline{T_{t}})\) calculated by the configuration formula in DDAR scheme. We design a formula to achieve the configuration of \(N_{t}\) and \(T_{t}\) for aggregators which need \(i\) hops to the sink, the formula has shown below (the formula to \(T_{t}\) is the same):  
\[N_{t}=\left\{\begin{array}{ll}\overline{N_{t}}\frac{L-i\left(\log_{2}\frac{ 1}{\lambda}\right)^{3}}{L},\;\lambda<0.5\\ \overline{N_{t}}\frac{L-i}{L}\log_{2}\frac{1}{\lambda},\;\lambda\geq 0.5 \end{array}\right. \tag{9}\]  
Service guarantee rate, lifetime and energy efficiency of the networks adopting the improved DDAR scheme are compared with those of the networks adopting the common DDAR. The results are listed in Table 11. With the 3% increase in maximum energy consumption, date efficiency increases 33.97% and service guarantee rate increases 10.11%.  
Figure 17 shows that the optimization effect slightly decreases as the value of \(\eta\) increases. The service guarantee rate and energy efficiency are compared. As the results are shown in Figures 18 and 19. The ratio of the rate decreases slightly as \(\eta\) increases and the ratio of energy efficiency increases linearly with the increase of \(\eta\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scenario** & **Energy Efficiency (\%)** & **Service Guarantee Rate (\%)** & **Energy Consumption (\%)** \\ \hline \(\mathcal{Q}=3\) & 134.97 & 110.54 & 102.88 \\ \(\mathcal{Q}=5\) & 134.03 & 109.95 & 103.23 \\ \(\mathcal{Q}=7\) & 132.90 & 109.83 & 103.44 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Optimization of energy efficiency.  
Figure 16: Average energy consumption and the ratio vs. \(\lambda\).  
Figure 19: The ratio of service guarantee rate vs. \(P_{\rm{a}}\).  
Figure 17: The ratio of service guarantee rate vs. \(\eta\).  
Figure 18: The ratio of energy efficiency vs. \(\eta\).  
Regarding the effect of \(P_{\alpha}\), Figure 20 shows that the optimization of service guarantee rate decreases sharply and then is saturated. Figure 21 reveals that the optimization of energy efficiency increases slightly and then is saturated.  
Here is the effect of the aggregation ratio. The optimization effect regarding energy efficiency is improved faster than that of service guarantee rate (as seen in Figures 21 and 22).  
Figure 21: The ratio of service guarantee rate vs. \(\lambda\).  
Figure 20: The ratio of energy efficiency vs. \(P_{\alpha}\).
## 5 The Second-Round Optimal Design of Crucial Structural Dimensions
### Optimal Design of the Structural Dimensions of the Aerostatic Spindle  
Figure 7 presents the axial stiffness and the first-order natural frequency of the aerostatic spindle with varying thrust plate thickness and air film thickness considering the FSI effect. It can be derived from Figure 7a that with the increase of thrust plate thickness, the maximum stiffness increases sharply in the range of 5-15 mm and then increases slowly in the range of 15-30 mm. This phenomenon can be explained as follows. The structural rigidity of the thrust plate is directly related to its thickness. By increasing its thickness, its structural rigidity will be improved, and the deformation of the thrust plate will be smaller. When the thickness of the thrust plate is larger than 15 mm, the stiffness trends to steady gradually with the increase of the thrust plate. It is because that the structural rigidity of the thrust plate is high enough to resist the air film force. The stiffness is improved slightly by further increasing its thickness. Therefore, the range of 15-30 mm is recommended for the thrust plate thickness for higher stiffness.  
However, the mass of the thrust plate is also directly related to its thickness. To increase the natural frequency, the designer generally hopes to reduce the mass of the rotating parts in the design stage. It means that the thickness of the thrust plate cannot be increased blindly. As shown in Figure 7b, with the increase of the thrust plate thickness, the first-order natural frequency first increases sharply and then declines quickly. A maximum region for the first-order natural frequency can be observed around the thrust plate thickness of 10-15 mm. This phenomenon can be explained as follows. According to the calculation result shown in Figure 7a, the stiffness first increases sharply and then trends to steady, while the mass increases linearly with the increase of the thrust plate thickness. Generally, the natural frequency is proportional to the stiffness and is inversely proportional to the mass. In the region where the stiffness increases sharply, the natural frequency increases with the increase of the thrust plate thickness. In the region where the stiffness increases slowly, the natural frequency decreases with the increase of the thrust plate thickness.  
Considering the stiffness and natural frequency comprehensively, a thrust plate thickness of 20 mm is selected in this paper. Comparing with the initial design, the first-order natural frequency of the aerostatic spindle increases by 45.3%, from 167.4 Hz to 243.3 Hz.
## 3 Results and Discussion
### Effect of Applied Amendments on Trace-Element Accumulation in Earthworms  
The accumulation of various trace elements in earthworm tissue has been evaluated using biota-soil accumulation factor (BSAF) according to the literature data [8; 12; 24; 28; 13; 21] and the formula presented in Materials and Methods Section 2.2.2. The results indicated a variation in the metal accumulation by earthworms caused by the type of element and the type of applied soil amendment (Table 2). Generally, the earthworms living in soil P absorbed metals proportionally to their concentration level in the soil, whereas the applied soil amendments significantly modified their bioavailability. The BSAF index indicated that the greatest susceptibility to accumulation in unchanged soil was exhibited by the Cd (Cd: 0.77), followed by the other measured elements (Cu: 0.55, Pb: 0.19, Zn: 0.07, Ni: 0.07, and Cr: 0.05). The results imply that the availability of metals depends on the chemical properties of the applied soil amendments, which significantly affect the risk of uptake of the harmful elements from the soil environment. After four weeks of experiments, the TE accumulation factor for these elements was observed in the amended soils within the following ranges: Cd: 0.35-0.65, Cu: 0.66-0.71, Pb: 0.10-0.17, Zn: 0.07-0.10, Ni: 0.07-0.15, and Cr: 0.01-0.23. The BSAF for the Cd decreased to the greatest extent after the addition of ZC (by 57%), GC (55%), CP (41%), and IO (37%), while it was statistically insignificant after the addition of BE (26%), RW (17%), and CC (16%). A similarly positive effect was noted for the Pb after the addition of IO (45% decrease). The other amendments somewhat lowered the Pb concentration values, but the average values were not statistically different. The amendments did not significantly affect the Zn and Cu accumulation in the earthworms; however, the numbers observed suggested that in soils treated with organic amendments, Zn and Cu might tend to accumulate more than in untreated soil. The increased accumulation of Zn and Cr does not diminish the beneficial effect of composts, since a major risk for the trophic chain is related to Cd. Furthermore, greater Zn supply reduces the accumulation of Cd in animal bodies [49]. Moreover, no pronounced effects of amendments were observed for the Ni bioaccumulation. The Cr bioaccumulation was reduced by the RW and CC amendments, while it was slightly stimulated by the ZC, GC, and CP. It is worth noting, however, that these differences were observed at low levels of both Cr concentration and BSAF. Therefore,
# Research on Secure Debugging Interaction of Sensor Nodes Based on Visible Light Communication
## 1 Introduction
Obviously, although both uplink and downlink are realized by VLC, the different signal receivers make the difference in processing light signals. In the system downlink, the ambient light sensor of the sensor node has a sensing latency. This latency limits the minimum width of the light pulse and causes a low sensing resolution which decreases data transmission efficiency. For this, we optimize system downlink transmission from the source and channel coding, respectively. With respect to source coding, we analyze the debugging codes and find a part of the operand (such as a base address) that can be compactly represented by a bitmask. With respect to channel coding, we propose an overlap dual-header pulse interval modulation (ODH-PIM) optimized for smartphone's flashlight. Like DH-PIM, ODH-PIM also adopts the width of the low power to represent a symbol and introduce a flashlight half-on state as an overlapping mark. The overlapping mark enhances the representation ability of a symbol and further improves transmission efficiency. In system uplink, we adopt the optical camera communication (OCC) for the debugging information transmission. The previous studies have applied DH-PIM in OCC. Due to the poor performance of COTS cameras of smartphones, directly applying DH-PIM in the camera increases the bit error rate (BER). Therefore, we modify the symbolic representation of DH-PIM and optimize the demodulation procedure to improve transmission stability. Finally, we conducted a series of experiments in HCDS and verified our methods above from compression rate, bit error rate, and transmission time. The experiment results illustrate that our compression method reduces 80% of redundant codes. Simultaneously,the ODH-PIM owns less transmission time than the original DH-PIM at the cost of an extra 17.36% energy overhead. Moreover, the modified DH-PIM can effectively reduce the average 29% BER for the uplink transmission. The rest of this paper is structured as follows: Section 2 shows related work; Section 3 gives the overview of the VLC-based HCDS; Section 4 presents the optimization in transmission efficiency of the system downlink; Section 5 presents the optimization in transmission stability of the system uplink; Section 6 describes experiments and evaluates system performance, and Section 7 closes our work with a conclusion.
## 2 Materials and Methods
### Tube-Based Growth Experiments  
Size structure responses were assessed through tube-based growth experiments, which were conducted in 20 mL glass test tubes. Each test tube received 15 mL of salinity-adjusted Zarrouk medium with 1.5 mL of inoculum added. The test tubes were covered with autoclaved cotton plugs to prevent contamination while maintaining gas exchange and reducing evaporative losses. The experiments were conducted with three replicates per salinity treatment.  
The cultures were incubated at different salinity levels for 20 days. After the incubation period, about 10 mL volume from each test tube was sampled and run through a Plankto-Scope for image acquisition [20; 21]. The lighting, focus, ISO, shutter speed, and white balance settings were optimized before image acquisition to ensure image clarity. A peristalic pump was used to move the sample incrementally between image captures, minimizing the re-imaging of the same organisms. Approximately 100 images were captured per run, with each image containing an estimated 20-30 organisms, targeting a total of 1000-2000 organisms per sample. All images and metadata were stored locally and subsequently transferred to a computer using FileZilla for further processing. Then, image segmentation was performed using the PlanktoScope's built-in segmentation tool. Segmented images were uploaded to the EcoTaxa platform ([[https://ecotaxa.obs-vlfr.fr/](https://ecotaxa.obs-vlfr.fr/)]([https://ecotaxa.obs-vlfr.fr/](https://ecotaxa.obs-vlfr.fr/)) (accessed on 1 February 2023)) for automated classification and assessment of morphological parameters (i.e., length and width). Initial classifications were reviewed and manually validated.  
For each of the validated images of the _Spirulina_ strain, the diagonal length was calculated to represent the organism size using Equation (6). Based on the computed diagonal length, the organisms were categorized into size classes: <200 \(\upmu\)m, 200-399 \(\upmu\)m, 400-599 \(\upmu\)m, 600-799 \(\upmu\)m, 800-999 \(\upmu\)m, and >1000 \(\upmu\)m.  
\[L_{D}=\sqrt{L^{2}\times W^{2}} \tag{6}\]  
where \(L_{D}\) is the diagonal length, \(L\) is the length, and \(W\) is the width.  
The density (\(D\)) of filaments per size category in each treatment was computed using Equation (7).  
\[Density\ (D)=\frac{n}{V_{sample\ imaged}} \tag{7}\]  
where \(n\) represents the number of filaments from a given size category and \(V_{sample\ imaged}\) refers to the volume of the water sample (i.e., 0.8 \(\upmu\)L) captured using the PlanktoScope.