esa-sceva/satcom-chunk-collection huggingface dataset:
# BPDM-GCN: Backup Path Design Method Based on Graph Convolutional Neural Network
## 4 Backup Path Design Method Based on BPDM-GCN
### Theoretical and Practical Correlation of GCN and DDPG Fusion Mechanism
#### 4.3.2 DDPG-GCN Collaborative Link Weight Optimization Implementation Mechanism  
The synergy between GCN and DDPG is reflected in the following key relationships:(1) State representation and feature extraction: The network topology state is encoded as the adjacency matrix \(A\) and the node feature matrix \(X\) (including link delay, bandwidth occupancy, etc.). GCN generates the node embedding \(H\) through a graph convolution operation as shown in Equation (8).  
\[H^{(l+1)}=\sigma(\overset{\sim}{D}-\frac{1}{2}\overset{\sim}{A}\overset{\sim}{D }-\frac{1}{2}H^{(l)}W^{(l)}) \tag{8}\]  
In Equation (8), \(\overset{\sim}{A}=A+I\) is the adjacency matrix with self-connection added, \(\overset{\sim}{D}\) is the degree matrix, and \(W^{(l)}\) is the trainable weight. The embedding vector \(H\) is used as input to the actor network to guide the link weight adjustment strategy.  
(2) Action generation and strategy optimization: The Actor network adjusts action \(a_{t}\) based on the link weight output by \(H\), and the Critic network calculates the value of \(Q\) based on \(H\) and \(a_{t}\) to evaluate the long-term benefits of the action. GCN's feature propagation mechanism enables the Critic network to predict link congestion risks and avoid strategy oscillations caused by traditional DDPG ignoring the topology structure.  
(3) Dynamic weighting of the reward function: The latency, bandwidth utilization, and packet loss rate indicators in the reward value are dynamically weighted by the topological features extracted by GCN. For example, the neighbor node features of high-load links are automatically enhanced by GCN, prompting DDPG to prioritize the optimization of critical path weights and achieve load balancing.
# Integrated Silicon Photonics for Enabling Next-Generation Space Systems
## 4 Space Radiation Effects
### Integrated Germanium Photodiodes
#### 4.1.2 Photodiodes: Displacement Damage Effects  
Section 4.1.1 demonstrated that since TID mostly relies on an interface damage mechanism, it does not have much of an effect on bulk-transport Ge PDs. However, a logical question presents itself: does radiation that produces bulk damage have a significant effect? The only experiment, to date, that has been conducted to answer this question suggests that even DD effects have minimal impact on PD performance [65]. This experiment exposed several PDs to 14 MeV neutrons, up to a fluence of \(7.5\times 10^{12}\) neutrons/cm\({}^{2}\).  
These results suggest that dark current is largely unchanged by DD, as shown in Figure 6a. The lack of damage is attributed to the fact that the pre-irradiation bulk trap density is already fairly large in such devices. The high trap densities are a direct consequence of fabricating a lattice-mismatched Ge crystal onto silicon. As a result, the neutron-generated bulk trap density is not enough to overcome the pre-existing density of bulk traps.  
The responsivity and optical-to-electrical bandwidth are also shown to have no change due to DD: Figure 6b,c, respectively. Responsivity does not change significantly because the carrier collection processes in this PD are primarily drift-based, which is less sensitive to bulk traps than diffusion-based charge transport. The bandwidth was also shown to not degrade with increasing neutron dose. A change in the junction capacitance was observed as a direct result of radiation, but it is of little concern, since the PD's bandwidth is limited by the carrier transit-time, as opposed to capacitive charging.  
Figure 5: Photodiode (**a**) Dark current and (**b**) frequency response exhibit minimal to no degradation across various TID levels (after [62]).
## 2 RDFTA (Repairable Dynamic Fault Tree Analysis)
### AND Logic Gate of RDFTA  
\(S_{x(i)}(t)\) represents the normal working probability of the AND gate input event \(x_{i}(t)\) at time \(t\), \(F_{xi(t)}(t)\) is the failure state probability of the AND gate input event \(x_{i}(t)\) at time \(t\), \(\mu_{x(i)}\) is the maintenance rate of the input event \(x_{i}(t)\), and \(\lambda_{x(i)}\) is the failure rate of input event \(x_{i}\). \(S_{y}(t)\) represents the normal working probability of the AND gate output event \(y(t)\) at time \(t\), \(F_{y}(t)\) is the failure state probability of the AND gate output event \(y(t)\) at time \(t\), \(\mu_{y}(t)\) is the maintenance rate of the output event \(y(t)\), and \(\lambda_{y}(t)\) is the failure rate of the input event \(y(t)\) (Figure 2).  
Quantitative operation rule  
In the AND logic gate, the input event \(x_{i}(t)(i=1,2,\ldots,n)\) and output event \(y(t)\) have two states: state 0 is the normal working state, while state 1 is the fault state. According to the relationship between the input and output events \(x_{i}(t)\) and \(y(t)\), a list of the quantitative operation rules of the AND gate can be obtained, as shown in Table 3.  
The quantitative operation rule  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number** & \(x_{1}(t)\) & \(x_{2}(t)\) & \(\ldots\) & \(x_{n}(t)\) & \(y(t)\) \\ \hline
2 & 0 & 1 & \(\ldots\) & 1 & 1 \\
3 & 1 & 0 & \(\ldots\) & 0 & 1 \\ \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) \\ \(2^{n+1}\) & 1 & 1 & \(\ldots\) & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The quantitative operation rules of the OR gate.  
Figure 2: The RDFTA AND logic gate.  
2. The quantitative calculation formula of the AND gate is as follows: \[\left\{\begin{aligned} F_{y}(t)=\prod\limits_{i=1}^{n}\ F_{x(i)}(t)=F_{x(1 )}(t)\times F_{x(2)}(t),...,F_{x(n)}(t)\\ S_{y}(t)=1-F_{y}(t)\\ \mu_{y}=\sum\limits_{i=1}^{n}\ \mu_{x(i)}=\mu_{x(1)}+\mu_{x(2)} \ldots+\mu_{x(n)}\\ \lambda_{y}(t)=\frac{p_{y}\times F_{y}(t)}{S_{y}(t)}\end{aligned}\right.\] (2)  
When the AND and OR logic gates of the RDFTA are quantitatively calculated, the failure rate \(\lambda_{x(i)}\) and maintenance rate \(\mu_{x(i)}\) of the corresponding components are imported into Formula (3) for pretreatment:  
\[A_{T}=\frac{\mu_{x(i)}+\lambda_{x(i)}\cdot e^{-(\lambda_{x(i)}+\mu_{x(i)})t}}{ \lambda_{x(i)}+\mu_{x(i)}}. \tag{3}\]
## 2 Related Work  
In-network aggregation query processing methods used in sensor networks such as TAG [13] only send the aggregated results inside the sensor network so as to reduce the number of messages. It enables one to increase sensor network lifetime by reducing energy consumption. Some special aggregation queries such as SUM, MIN and MAX, are more effective in saving energy since they only aggregate a single aggregation value instead of all possible data. Also, DCSs process queries in a sensor network, and only send query results to a server. However, skyline queries exclude values only when data are dominated by other data, so it is difficult to find complete query results without inspecting all the data. Therefore, to process skyline queries in sensor network, it is important to establish criteria to exclude unnecessary data from the results.  
Several skyline query processing methods such as [8-11] have been proposed. Most of them focus on designing filters to exclude as much unnecessary data as possible. [PERSON] _et al._[8] dealt with a constrained skyline query problem on MANETs by devising a single point filter-based evaluation algorithm that is easily extended to sensor networks. [PERSON] _et al._[10] proposed two filter-based algorithms. One is the single point filter-based algorithm TF and another is the grid filter-based  
Figure 1: Example of GDCS.  
algorithm GI. The TF algorithm chooses the point that dominates the maximum number of points as the filter, assuming that the data distribution density is given beforehand, while the GI algorithm exploits the grid partition of data space and generates a grid filter.  
[PERSON] _et al._[9] proposed a new filter-based algorithm which consists of multiple rather than single points as the filter, whereby each sensor sends part of its local skyline points chosen by a greedy algorithm to its parent and the root broadcasts the received points as the global certificate obtained through in-network aggregation. The points that cannot pass through the certificate will be filtered out from transmission. [PERSON] _et al._[10] proposed a density function based skyline query processing algorithm. It assumes that the density function of data is known beforehand. However, in real applications it is hard to find out the density function beforehand. [PERSON] _et al._[11] proposed two algorithms for evaluating skyline queries are devised, which find the skyline points progressively. It partitions the dataset into disjoint subsets, followed by returning the skyline points through examining each subsequent subset progressively, using some found skyline points so far as a filter to filter out those unlikely skyline points in the currently processing subset from transmission.
###### Abstract  
Advancement in smartwatch sensors and connectivity features demands low latency communication with a wide bandwidth. ISM bands below 6 GHz are reaching a threshold. The millimeter-wave (mmWave) spectrum is the solution for future smartwatch applications. Therefore, a compact dual-band antenna operating at 25.5 and 38 GHz is presented here. The characteristics mode theory (CMT) aids the antenna design process by exciting Mode 1 and 2 as well as Mode 1-3 at their respective bands. In addition, the antenna structure generates two traverse modes, TM\({}_{10}\) and TM\({}_{02}\), at the lower and higher frequency bands. The antenna measured a bandwidth (BW) of 1.5 (25-26.5 GHz) and 2.5 GHz (37-39.5 GHz) with a maximum gain of 7.4 and 7.3 dB, respectively. The antenna performance within the watch case (stainless steel) showed a stable \(\mid\)S\({}_{11}\mid\) with a gain improvement of 9.9 and 10.9 dBi and a specific absorption rate (SAR) of 0.063 and 0.0206 W/kg, respectively, at the lower and higher bands. The link budget analysis for various rotation angles of the watch indicated that, for a link margin of 20 dB, the antenna can transmit/receive 1 Gbps of data. However, significant fading was noticed at certain angles due to the shadowing effect caused by the watch case itself. Nonetheless, the antenna has a workable bandwidth, a high gain, and a low SAR, making it suitable for smartwatch and IoT applications.  
Keywords:dual-band; IoT; link margin; millimeter wave antenna; SAR; smartwatch +
Footnote †: journal: _Sensors_
# A Survey of Vehicle to Everything (V2X) Testing
## 3 V2X Network Challenges
### V2X Security Threats
The data management and privacy protection systems are still in the process of being perfected. It is necessary to discuss and refine key issues such as which data can be collected, how data is used, and whether it can be shared with third parties [33].
# LoRa, Zigbee and 5G Propagation and Transmission Performance in an Indoor Environment at 868 MHz
## 1 Introduction  
Currently, the interest in new wireless technologies, such as the Internet of Things (IoT), is growing exponentially, mainly due to its potential to automate processes and optimize the use of resources. The massive deployment of sensor networks is only possible if there is some way to connect the devices without the need to wire each one of them, and that would have a very high cost [1; 2; 3]. To overcome these limitations of wired networks, wireless transmission technologies are highly important as they provide a feasible and versatile solution. For that reason, detailed knowledge regarding the propagation characteristics of signals is essential so as to predict the losses that will be introduced by the environment [4] where the sensor network, called wireless sensor networks (WSNs), is deployed.  
Today's IoT devices are deployed using wireless communication technologies such as LoRa [5] and Zigbee [6], among others, although recently, 5G technologies have also gained prominence in IoT networks [7]. These technologies are used especially in machine-to-machine (M2M) communications; that is, they are communications not intended forhumans. The use of one technology over another depends on the bandwidth, power consumption and coverage needs and the propagation characteristics of the environment. Due to the low power consumption of the devices, these networks are called Low-Power Wide-Area Networks (LPWAN) [8].
## 3 Experiments
### Results
#### 3.6.2 Experimental  
Figure 6 shows the results of the experimental testing datasets with 60 views. The ROI is indicated with the dashed square, which is enlarged and shown to obtain better visualization. The analysis of the experimental datasets was performed as the same as that of the simulation datasets. The corresponding curves and index values are presented in Figures 7 and 8 and Tables 4-6. The same conclusion can be drawn as that of the simulation datasets. The images of DD-Net and HD-Net are blurred and lose some structure. There are severe artifacts existing in the images of DLFBP. Furthermore, DDPC-Net outperforms the comparison methods. In addition, the FSIM and IW-SSIM values of the experimental datasets are significantly worse than those of the simulation datasets, since noise introduced during the experiment degrades the experimental datasets.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Evaluation** & **Methods** & \multicolumn{4}{c}{**Cases**} \\  & **60** & **90** & **120** & **180** \\ \hline \multirow{4}{*}{FSIM} & FBP & 0.5851 & 0.6387 & 0.6671 & 0.7495 \\  & DD-Net & 0.9168 & 0.9234 & 0.9333 & 0.9475 \\  & DLFBP & 0.9037 & 0.9139 & 0.9221 & 0.9432 \\  & HD-Net & 0.9234 & 0.9357 & 0.9552 & 0.9604 \\  & DDPC-Net & 0.9722 & 0.9772 & 0.9874 & 0.9884 \\ \hline \multirow{4}{*}{IW-SSIM} & FBP & 0.5879 & 0.6831 & 0.7961 & 0.9074 \\  & DD-Net & 0.9174 & 0.9254 & 0.9439 & 0.9589 \\ \cline{1-1}  & DLFBP & 0.8955 & 0.9109 & 0.9360 & 0.9528 \\ \cline{1-1}  & HD-Net & 0.9233 & 0.9318 & 0.9470 & 0.9616 \\ \cline{1-1}  & DDPC-Net & 0.9795 & 0.9876 & 0.9957 & 0.9978 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The average values of FSIM and IW-SSIM for the results of the simulated testing datasets.  
Figure 5: The _rell_ curves of the average values of FSIM and IW-SSIM of the simulated testing datasets.  
Figure 6: The reconstructed images of the results of the experimental testing datasets. This sinogram has 60 sampling views, and the reconstructed images were obtained by five methods.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Evaluation** & **FBP** & **DD-Net** & **DLFBP** & **HD-Net** & **DDPC-Net** \\ \hline FSIM & 0.7272 & 0.8957 & 0.8751 & 0.9026 & 0.9584 \\ IW-SSIM & 0.5957 & 0.8844 & 0.8750 & 0.9168 & 0.9690 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The FSIM and IW-SSIM values of the images in Figure 6.  
Figure 7: The profiles along the blue solid line in Figure 5.
## 2 Methodology
### Model Development
#### 2.4.1 Model Architecture
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model Name** & **Conv. Layers** & **Filters** & **Pooling** & **Dense Layers** & **Regularization** & **Remarks** \\ \hline C1f1k3 API\_D12 (I) & 1 & 1 & 1, Average & (113,12) & - & initial model \\ C1f1k3\_MPI\_D12 (II) & 1 & 1 & 1, Max & (113,12) & - & accuracy improves \\ C2f1k3\_API\_D12 (III) & 2 & 1 & 1, Average & (111,12) & - & accuracy improves \\ C2f1k3\_API\_D13 (not plotted) & 2 & 1 & 1, Average & (111,48,12) & - & negligible improvement \\ C2f1k3\_API\_D48\_D12 (IV) & 2 & 1 & 2, Average & (558,48,12) & - & accuracy improves \\ C2f3k3\_API\_D148\_D12 (V) & 2 & 3 & 1, Average & (333,48,12) & - & accuracy improves \\ C2f3k3\_API\_D6\_D12 (VI) & 2 & 3 & 1, Average & (333,62,12) & - & accuracy worsens \\ C1f1k3\_API\_D80\_D12 (VII) & 1 & 1 & 1, Average & (113,12) & dropout & accuracy improves \\ C1f1k3\_API\_D48\_R11\_D12 (VIII) & 1 & 1 & 1, Average & (113,48,12) & L2 & accuracy worsens \\ C2f3k3\_API\_D48\_R150\_D12 (X) & 2 & 3 & 1, Average & (333,48,12) & dropout & best accuracy, VOC-Net \\ C2f3k3\_API\_D48\_R11\_D12 (X) & 2 & 3 & 1, Average & (333,48,12) & L2 & accuracy worsens \\ C2f3k3\_API\_D48\_R11\_R50\_D12 (XI) & 2 & 3 & 1, Average & (333,48,12) & L2 + dropout & accuracy worsens \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overview of CNN model architectures. The accuracy is calculated at the 200 th epoch. The filter kernel size is three, the pool size is two, and padding is valid with stride of two. Figure 5: Accuracy on training and validation data for the considered CNN model architectures.
# Dynamic Path Planning for Unmanned Surface Vehicles with a Modified Neuronal Genetic Algorithm
## 3 Mutation  
The mutation operator operates by randomly flipping selected bits, and the mutation probability, \(p_{m}\), is typically set to be smaller than that of other operators. This operator serves to enrich the diversity within the population [29] and offers a means to break free from local optima [23]. In the context of the USVs, this operator introduces randomization by altering both the row and column of the weight matrix. It then adjusts the values by adding a random rank value ranging from \(-1\) to \(1\). This step can be thought of as an exploitation task within the genetic algorithm. Figure 10 shows the mutation process of the USV.  
Figure 10: USV performing the mutation process to reproduce mutated children. The red USVs are the original ones, the blue USVs are the children after crossover process, and the green color indicates USV children after mutation (mutated children).  
Figure 9: USV performing the crossover process to produce new children. USVs with red color are the original ones, and the blue color indicates the USV children after crossover process.  
End function  
The \"end\" function serves to conclude the iteration process or initiate a new iteration when the USV encounters difficulties in executing the pathfinding task. Specifically, we halt and restart the learning process under the following conditions:  
1. If the USV collides with obstacles or barriers in the environment;
2. When the agent produces a suboptimal or too-weak solution (determined by both the achieved fitness value and the time iteration);
3. If the USV submerges below the water's surface.  
Ultimately, the process concludes when the USV successfully reaches the target, completing more than one full round without colliding with any obstacles.
## 3 Experimental Results  
### Characterization of LNAPL Migration Rates  
#### 3.1.1 The No Lens-Shaped Aquifer Test Group  
After leaking in homogeneous fine sand, the LNAPL formed an ellipsoidal shape and diffused uniformly in all directions. The degree of vertical diffusion was greater than the horizontal, with vertical migration fronts appearing rounded and smooth. This indicates that the vertical migration speed was rapid. In contrast, some horizontal migration fronts exhibited a jagged, folded distribution, suggesting the presence of small-scale medium heterogeneity within the soil matrix. As observed from the migration profile diagram in Figure 3a, the horizontal migration velocity of the LNAPL gradually decreases over time. The vertical migration velocity can be divided into three phases. In the early stage (first 65 min), the LNAPL migrated horizontally and vertically due to gravity and capillary forces, with horizontal migration initially faster than vertical, at rates of 0.18 cm/min and 0.15 cm/min, respectively. This was due to the significant capillary force in the fine sand, favoring early horizontal spread. However, as the diesel volume increased, vertical migration rates rose to 0.17 cm/min, surpassing horizontal rates, due to the oil's weight. During the middle stage (65-160 min), horizontal migration contracted at 33 cm depth, as increased water content in the medium reduced LNAPL adsorption and decreased medium porosity, increasing capillary resistance and slowing LNAPL infiltration. In the late stage (160-315 min), vertical migration slowed and the oil front widened horizontally, as the LNAPL accumulated at the capillary fringe, favoring horizontal migration. The continuing leak intensified the LNAPL's gravitational force, surpassing capillary forces, allowing the LNAPL to displace water and continue migrating slowly until the leak stopped.  
Figure 3: _Cont._  
Figure 2: Schematic diagram of the measurement method.
# Neural Network-Based Analysis and Its Application to Spectroscopy for Mango
###### Abstract  
Sugar derived from crops is a crucial organic energy source studied in the Earth sciences, serving as a renewable and clean energy alternative. Biorluels produced from crop sugars are more environmentally friendly than traditional fossil fuel sources and contribute to solar energy storage and conversion within the Earth's cycle. Using mangoes as a case study, this research employs near-infrared spectral analysis technology to develop an algorithm for a mango brick detection device. The study investigates the relationship between brix and absorbance, as well as changes in brix levels, and their application for on-site mango brix detection. Near-infrared spectral data in the range of 1300 nm to 2300 nm were collected during the mango ripening season in summer and preprocessed using various techniques. A neural network-based least squares modeling approach was utilized to develop a mango sugar content detection model, resulting in a correlation coefficient of 0.9055 and a root-mean-square error of 0.2192. To enhance model accuracy and avoid local optimization issues, this study incorporated the simulated annealing algorithm for model optimization, leading to a correlation coefficient of 0.9854 and a root-mean-square error of 0.0431. The findings demonstrate that the non-destructive testing model of mangoes based on near-infrared spectroscopy effectively detects brix changes and storage potential post-harvest, offering valuable insights for mango quality assessment, optimal picking and selling times, and market selection.  
near-infrared spectroscopy; non-destructive testing; mango; neural networks; simulated annealing; detection method
## 3 Results and Discussion
### Time Dimension Recognition and Disease Dynamics  
Maps with a time dimension are able to represent the spread of infection over time (Figure 4) and recognize clustering phenomena. The capability of a geoportal for visualizing large amounts of information interactively is often seen as the only important function of it. This ability to create multiple perspectives enhances a viewer's ability to understand studied phenomena.  
The user interface is required to be intuitive and simple to learn, as shown in Figure 5. Generally, an interactive map application allows users to explore and recognize spatial-temporal patterns.  
Figure 4: COVID-19 infection spread presented simultaneously by three indicators: speed, intensity, and tendency. The figure shows a diagram of the weekly average infected per 100,000 citizens. Symbols present trends (increase—red and orange/decrease—green and blue) for the previous three weeks and the rate of weekly percentage infected compared to the previous period in local communities.  
Analogous to its positional and attribute components, the quality of spatial data can also be evaluated in terms of their temporal accuracy. This includes not only the accuracy and precision of the measurement time (for example, the date of examination) but also the temporal consistency of different data sets (which may be in conflict). As the geometric and attribute components of spatial data can be changed together or independently, their temporal validity must also be considered. Validity of the data set implies the actuality of the data in terms of the time difference between the currently available data and the actual state of the phenomenon they represent. The frequency of updating data has a direct impact on validity [23]. On this portal, the data is updated every working day.