esa-sceva/satcom-chunk-collection huggingface dataset:
# Edge-Guided Dual-Stream U-Net for Secure Image Steganography
## 3 The Proposed Method
### Iterative Optimisation  
To enhance the quality and effectiveness of the stego image, we adopt the iterative learning optimization method proposed by [PERSON] et al. [19]. This method incrementally adjusts the perturbation vector to ensure that the stego image remains stable on the natural image manifold. Unlike traditional one-shot optimization methods, this approach leverages GRU units to iteratively optimize the perturbation by integrating feedback from the loss function. This approach quickly reduces errors, improves hidden data accuracy, and enhances the stego image's natural appearance.  
The input for each iteration \(\kappa_{t}\) includes three components:  
\[x_{t}=concat\big{(}\delta_{t-1},\
abla_{\delta_{t-1}}\mathrm{L}_{t},features( F)\big{)} \tag{10}\]Current perturbation \(\delta_{t-1}\): Represents the modification applied to the original image in the previous iteration.  
Gradient of the loss function \(\
abla\delta_{t-1}L\): the gradient of the current perturbation on the total loss. Guides the adjustment of the perturbation to minimize the loss.  
Original image features \(F\): Features extracted from carrier images after dual stream U-Net results.  
GRU-based hidden state updates: The GRU (Gated Recurrent Unit) updates the hidden state \(\mathrm{h_{t}}\) based on the previous hidden state \(\mathrm{h_{t-1}}\) and the current input \(\mathrm{x_{t}}\). The update equations are as follows:  
\[\left\{\begin{array}{l}z_{t}=\sigma(Conv_{z}([h_{t-1},x_{t}]))\\ r_{t}=\sigma(Conv_{r}([h_{t-1},x_{t}]))\\ \widetilde{h}_{t}=\tanh(Conv_{h}([r_{t}\odot h_{t-1},x_{t}]))\\ h_{t}=(1-z_{t})\odot h_{t-1}+z_{t}\odot\widetilde{h}_{t}\end{array}\right. \tag{11}\]  
Here, \(\sigma\) represents the activation function, \(z_{t}\) is the update gate controlling how much of the new state is retained, and \(r_{t}\) is the reset gate determining how much of the past state is forgotten. The hidden state \(h_{t}\) serves as the basis for generating the update direction.  
Generating the update direction \(g_{t}\): The hidden state \(\mathrm{h_{t}}\) is processed through a fully convolutional network to produce the update direction. This update direction indicates how to adjust the perturbation to further reduce the loss.  
\[g_{t}=ConvNet(h_{t}) \tag{12}\]  
Updating the perturbation: Using the generated update \(g_{t}\) update the current perturbation \(\delta_{t-1}\) to obtain the new perturbation \(\delta_{t}\).  
\[\delta_{t}=\delta_{t-1}+\eta\cdot g\big{(}\
abla_{\delta_{t-1}}L(X+\delta_{t- 1},M),X,\delta_{t-1}\big{)} \tag{13}\]  
\(\eta\) is the learning rate, \(\
abla\delta_{t-1}\) is the gradient with respect to the perturbation, and the function \(g\) is used to process these inputs and update the perturbation with respect to the GRU. Finally, the steganographic image is generated after several iterations, where \(T\) denotes the total number of iterative optimizations.  
\[S=X+\delta_{T} \tag{14}\]
## 3 Results and Discussion
### Analysis of the Statistical Design of Experiments  
Table 2 lists the results of the removal efficiency of chrysoidine G and methylene blue for each of the 32 experiments that constitute the experimental matrix.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Experiment**} & \multicolumn{4}{c}{**Coded Values**} & \multicolumn{2}{c}{**Removal Efficiency (\%)**} \\ \cline{2-6}  & **Fe (II)** & **Fe (III)** & **H\({}_{2}\)O\({}_{2}\)** & **Polyphenols** & **Chrysoidine** & **Methylene Blue** \\ \hline
1 & 0 & 2 & 0 & 0 & 89.5 & 87.4 \\ \hline
2 & 0 & 0 & 0 & 0 & 88.3 & 90.6 \\ \hline
3 & 1 & 1 & \(-1\) & \(-1\) & 85.1 & 86.8 \\ \hline
4 & \(-1\) & \(-1\) & \(-1\) & \(-1\) & 83.2 & 76.4 \\ \hline
5 & \(-1\) & \(-1\) & \(-1\) & 1 & 80.7 & 49.5 \\ \hline
6 & 0 & 0 & 0 & 0 & 88.9 & 83.9 \\ \hline
7 & 0 & 0 & 2 & 0 & 93.8 & 91.6 \\ \hline
8 & 0 & 0 & 0 & 0 & 89.2 & 87.8 \\ \hline
9 & 1 & 1 & 1 & \(-1\) & 94.9 & 97.5 \\ \hline
10 & 0 & 0 & 0 & \(-2\) & 74.4 & 85.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental matrix and removal efficiency of chrysoidine G and methylene blue.
## 4 Materials and Methods
### Machine Learning Segmentation Process  
#### 4.3.1 Image Annotation and Color Transformation  
During the image-processing stage, illumination differences can create difficulties in achieving accurate segmentation. Various color spaces have been shown to possess unique characteristics that can minimize the impact of illumination on images and can resist noise variations during processing. Based on this concept, color features were extracted from different color channels to build a segmentation model. To accomplish this, image patches were created for plants at various growth stages and non-plant materials, which were manually annotated [36] as the foreground and background (Figure 8). The foreground consisted of 1400 patches (800 from field and 600 from glasshouse images), while the background had 1100 patches (600 from field images and 500 from glasshouse images). Each patch was then resized to a 20 \(\times\) 20 pixels patch and transformed into eight different sets of color channels, including RGB, HSV, YCbCr, Lab, YUV (Y = luma or brightness, U = blue projection, and V = red projection), Luv (L = luminance, u = blue axis, and v = red axis), hls (h = hue, l = lightness, and s = saturation), and XYZ (chromatic coordinates). In this case, each pixel with eight transformed color channels gives a final feature vector of size 24. Each patch represents a \(m\)\(x\)\(n\) matrix, where \(m\) is the number of pixels, and \(n\) is the number of color channels. Hence, for each image patch, (20 \(\times\) 20) \(\times\) 24-pixel features were obtained. With 1400 foreground and 1100 background patches, a total of 24,000,000-pixel features (foreground = 13,440,000, and background = 10,560,000) were used to train a binary classifier for segmentation.
# MAToC: A Novel Match-Action Table Architecture on Corundum for 8 \(\times\) 25G Networking
## 4 Implementation
### Simulation  
For verification purposes, the Cocotb [28] is used. This is a python verification framework with adequate support on bus interfaces and a capacity for wide test coverage. The functional verification and stress tests are performed separately for match stage, action stage, MAToC top module and at PCIe level. The simulation at PCIe level is done using a simulating driver in python provided by Corundum.  
The implementation is coded in Verilog. Many of the components come from the Corundum library, including the multiplexer that constructs the packet processing data path. A few improvements have been made. For example, the original multiplexer introduces a bubble between packets when there is only one single channel that keeps entering. This is because the arbiter excludes the channel granted for its decision, so the channel would not be granted again until the transfer of the current packet was completed. In order to eliminate bubbles without adding extra delays, a prediction grant result is assigned to the currently granted channel by the arbiter.
## 8 PET-Side Multiplexed Sensing and Clinical Quantification  
PET-side signal enhancement was achieved via structured multiplexed acquisition using deterministic binary coding masks applied during list-mode capture. These masks introduced controlled overlapping projection paths, enabling simultaneous sampling of adjacent field-of-view regions. The forward model was adapted to accommodate these encoding matrices.  
Clinically, this led to superior lesion contrast detectability, particularly for sub-centimeter nodules. Using simulated phantoms, detection sensitivity increased from 72% to 93% for lesions 6-8 mm in diameter. Standardized uptake value (SUV) quantification was evaluated for 12 in vivo cases using \({}^{18}\)F-FDG. The proposed model yielded higher recovery coefficients: 0.82 (standard OSEM) versus 0.92 (GAN + multiplexed) in low-contrast lesions.  
Radiotracer details: \({}^{18}\)F-FDG administered at 3.7 MBq/kg, post-injection scan delay: 60 min. Quantification metrics included mean SUV, peak SUV, and SUVmax, validated against radiologist annotations. The proposed reconstruction noticeably improved accuracy without prolonging scan duration.
# Mechanical Assembly Monitoring Method Based on Semi-Supervised Semantic Segmentation
## 2 Related Work
### Unsupervised Semantic Segmentation  
Unsupervised learning uses unlabeled datasets to train models. The unsupervised semantic segmentation method can avoid the consumption of human and material resources required by data labeling. [PERSON] et al. [17] first performed pixel-level representation learning and then fine-tuned the network to realize unsupervised semantic segmentation. Through a training method using the virtual city dataset as the source domain and the real city dataset as the target domain, [PERSON] et al. [18] adopted the adversarial idea to achieve unsupervised domain adaptation of the target domain and achieve semantic segmentation of the target domain. In the field of domain adaptation, unsupervised learning is realized by training the network in the source domain and predicting in the target domain. Although the unsupervised semantic segmentation method does not need labeled data, the accuracy of the current unsupervised semantic segmentation method cannot meet the high-precision requirements in the industry.
# A Practical Neighbor Discovery Framework for Wireless Sensor Networks
## 4 Spear: Neighbor Discovery Framework
### Measurements  
In this paper, we utilize three metrics to evaluate the algorithms. Considering each node \(u_{i}\),  
1. [label=(0)]
2. **Lifetime**\(Lf_{i}\) reveals how long the node can survive;
3. **Discovery latency**\(L(i,N_{i})\) is the number of time slots (\(t_{0}\)) to discover all neighbors;
4. **Discovery rate** is the percentage of discovered neighbors in \(N_{i}\) for a bounded latency.  
Lifetime and discovery latency are commonly adopted in the existing works. Due to communication collisions, some nodes may not be able to find all neighbors, and so we introduce discovery rate for evaluation.
## 4 Performance Analysis of the Generated Models in Virtual Environments
### Performance Analysis of Models
#### 4.2.5 CPU Utilization Percentage  
Similarly, the tool records the CPU utilization percentage of the headset, represented by the worst-performing core. Figure 11 shows the average CPU usage percentage, taking into account that it captures the core with the worst performance in each measurement.  
Figure 10: Meta Quest 3 bottleneck identification based on frame rate and App T.  
In the case of Quest 2 and 3, there is a clear trend of a higher percentage of CPU usage in scenes with low polygonal load, which decreases as the polygonal load of the scenes increases. In the case of the Quest Pro, the percentages remain close, between 20 and 30% usage, except in the three scenes with the highest polygonal load of all. The reason for the downward trend in CPU usage in devices may be due to the bottlenecks generated by the GPU in more demanding scenes during rendering, so the CPU waits for the GPU to finish rendering before starting to process the next frame. Conversely, in the case of scenes with lower polygonal load, the CPU is more used as it needs to prepare and send a greater number of frames to the GPU, meeting the frame rate obtained in these scenes as shown in Section 4.2.1. Finally, we highlight the difference in CPU usage in the Quest 2 compared to the other two more recent Meta devices. While in scenes with 4 LP and MP objects its usage is considerably higher, it is not concerning since these scenes feel fluid. However, it can be observed, especially with the data from the Quest 3, that the processor of the latter is used approximately 10% more for the rest of the scenes, as the power of its CPU allows it to try to reach a higher frame rate.
## 3 Results and Discussions
### Temporal Dispersion
Here we give the LD-based numerical results in order to make a comparison between the diffusive and collimated links in Figure 6. It can be found that the LD-based channel shows less channel dispersion effect than the LED-based link due to its collimation and much smaller divergent angle.
## 2 Topology and Modelling of the Studied System
### Modelling of EV Charging  
The charging protocol of lithium-ion batteries of EVs is the Constant Current/Constant Voltage (CC/CV) process [19; 22]. During the CC mode, the charging current remains constant until the voltage rises to a cut-off voltage. During the CV mode, the voltage remains constant while the current drops. This CC/CV procedure is supposed to be controlled by a battery management system already integrated into the EV battery system. When the EV starts charging, the EV battery voltage is relatively low. If the charging current is not constant, the battery lifecycle and the charger lifecycle will be shortened. When the battery is nearly fully charged, the procedure goes into a constant voltage phase, whose goal is to prevent the battery from overcharging. In this paper, it is assumed that three charging modes exist: the fast mode, the average mode, and the slow mode. The charging mode and demanded maximal charging power are shown in Table 1. The EV battery power, \(p_{EV}\), and EV battery energy for the same EV are presented in Figure 4, and the EV battery energy is about 24 kWh.  
In Table 1, \(P_{FAST\_MAX}\) is the maximal charging power demanded by the fast mode, and the value is 83 kW; \(P_{AVER\_MAX}\) is the maximal charging power demanded by the average mode, and the value is 27 kW; \(P_{SLOW\_MAX}\) is the maximal charging power demanded by the slow mode, and the value is 7 kW.  
The EV charging modelling equations are expressed by Equation (10) where \(v_{EVs}\) and \(i_{L_{EVs}}\) are the voltage and current of the EV converter's output side, \(L_{EVs}\) and \(C_{EVs}\) are the EV inductor and capacitor, respectively, \(i_{EVs}\) represents the input current for EVs, while \(v^{\prime}_{EVs}\) and \(i^{\prime}_{L_{EVs}}\) are the voltage and current of the EV converter's input side.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Charging Mode** & **Maximal Charging Power** \\ \hline Fast charging mode & \(P_{FAST\_MAX}\) \\ Average charging mode & \(P_{AVER\_MAX}\) \\ Slow charging mode & \(P_{SLOW\_MAX}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Charging mode and demanded maximal charging power.  
\[\begin{array}{l}\frac{dv_{EVs}}{dt}=\frac{i_{EVs}-i_{EVs}}{C_{EVs}}\\ \frac{di_{EVs}}{dt}=\frac{v^{\prime}_{EVs}-v_{EVs}}{L_{EVs}}\\ \left[\begin{array}{c}v^{\prime}_{EVs}\\ i^{\prime}_{L_{EVs}}\end{array}\right]=f_{EVs}\left[\begin{array}{c}v_{C}\\ i_{L_{EVs}}\end{array}\right]\Rightarrow\left[\begin{array}{c}v^{\prime}_{EVs} \\ i^{\prime}_{L_{EVs}}\end{array}\right]=m_{EVs}\left[\begin{array}{c}v_{C}\\ i_{L_{EVs}}\end{array}\right]\\ m_{EVs}=\frac{1}{7}\int_{0}^{T}f_{EVs}dt\text{ with }m_{EVs}\in[0;1]\end{array} \tag{10}\]
## 4 Experiment and Results
### Instances and VMs  
Table 4 shows the four VMs launched for RSBD pre-processing. Thus, we created instances 1 and 2 using the local cluster of the UPC equipped with OpenStack. On the other hand, we allocated instances 3 and 4 using the Elastic Compute Cloud (EC2) of the Amazon Web Services (AWS) to obtain more computing capacities for testing.  
This paper acquired NRT data from five sources: the MDEO, NASA, NOAA, ESA, and some Meteorological Ground Station (MGS). The data were measured with around 25 satellite sensors and more than 60 ground sensors. The collected data were transmitted through downlink channels, providing about 50 products. Moreover, the data were stored in a scientific file format, such as NetCDF, HDF5, BUFR, and GRIB. The total daily volume of data sums up 50 GB, and the velocity reaches more than 40,000 files per day. The acquired data's latency averages between one minute and three hours, as shown in Table 5. The total number of plots (a single measurement of a variable in a specific time and location in the map) in Morocco 24 h sums up 10 million datasets.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Instance Type** & **Instance (VM)** & **VCPU** & **RAM** & **Storage** \\ \hline Private (UPC) & Instance 1 & 7 & 16 & HDD \\ Private (UPC) & Instance 2 & 8 & 32 & HDD \\ Public (AWS) & Instance 3 & 16 & 64 & SSD \\ Public (AWS) & Instance 4 & 32 & 128 & SSD \\ \hline \hline \end{tabular}
\end{table}
Table 4: The configuration of the used VMs.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Organizations** & **Sensors** & **Products** & **File Format** & **Size/ Day (GB)** & **Velocity/ Day** & **Latency (Minutes)** \\ \hline MDEO & 8 & 27 & NetCDF, & 10 & 20,000 & 1–35 \\ NASA & 8 & 14 & HDF5, & 12 & 7000 & 40–140 \\ NOAA & 6 & 6 & BUFR, & 14 & 8000 & 60–180 \\ ESA & 3 & 5 & GRIB, & 17 & 150 & 120–180 \\ MGS & 61 & 7 & Bin & 1 & 100 & 1–10 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The specifications of the daily input data.
* To improve the optimization performance, especially while solving complex multi-objective optimization problems, the hybrid algorithm between DE and PSO algorithms, called MHPSODE algorithm has been proposed. The hybridization between two well-known EAs has been achieved by introducing mutation operators from DE algorithm into the velocity update equation of the PSO algorithm. Based on an adaptive normalized population spacing parameter the proposed algorithm is able to choose appropriate modified mutation operator for the current optimization conditions. * The performance of the proposed MHPSODE algorithm has been verified by comparing it with well-known multi-objective metaheuristic algorithms such as NSGA-II and DEMO on 10 benchmark MOO problems defined in CEC2009. The experimental results demonstrated that the proposed MHPSODE algorithm has significantly better optimization performance compared to other existing algorithms, in terms of both quality of the obtained Pareto solutions and convergence. This paper is organized as follows. In Section 2 the formulation of the multi-objective planetary gear optimization problem has been given. Furthermore, in Section 2.1 the formulation of the constrains for the considered optimization problem have been outlined. Section 2.2 gives the mathematical formulation of the planetary gear train efficiency model. Section 3 presents the problem of constrained multimodal optimization. Furthermore, in Sections 3.1 and 3.2 the theoretical formulation of the PSO and DE algorithms, has been outlined, respectively. In addition, Section 3.3 gives the formulation of the proposed hybrid HPSODE algorithm. Section 4 presents the results of the numerical simulation, where in Section 4.1 the focus is on the experiments dealing with planetary gearbox optimization, and in Section 4.2 the statistical comparison of the optimization performance of the proposedalgorithm on the CEC2009 benchmark problems has been outlined. Finally, the conclusions are drawn in Section 5.
# Comparative Analysis of SAAS Model and NPC Integration for Enhancing VR Shopping Experiences
## 5 Discussion  
Our study findings indicate that the SAAS model surpasses other models in producing individualized purchasing recommendations by utilizing user behavior and past interactions. The subsequent phase of our research will explore the integration of the SAAS model with a 3D virtual reality (VR) setting, where non-player characters (NPCs) are envisioned to provide voice-based recommendations to users. The purpose of this integration is to improve the engaging purchasing experience by utilizing AI-powered recommendations provided through interactive non-player characters (NPCs) [51; 52].  
While our study focuses on the incorporation of the SAAS model within a VR environment to enhance online shopping experiences, the practical implementation and detailed evaluation of NPC integration is an area for future exploration. Our current work lays the groundwork by demonstrating the potential of NPCs to deliver AI-powered, voice-based recommendations in a VR setting. However, a comprehensive user study to evaluate the effectiveness and user satisfaction of NPC interactions within the VR shopping environment is planned for future research. This will involve gathering user feedback to refine NPC behavior, interaction quality, and overall system usability.  
Within this virtual reality (VR) setting, NPCs engage with Users by offering tailored shopping recommendations derived from the analyzed data of the SAAS paradigm. The lifelike vocal interactions of the NPCs increase the immersion and authenticity of the virtual shopping experience by effectively delivering these recommendations. This integration utilizes sophisticated AI algorithms to examine user preferences and behavior, enhancing the purchasing experience by making it intuitive and fast.  
Figure 9 illustrates a virtual reality setting where an NPC interacts with the user, offering personalized shopping recommendations. The image shows a VR shopping environment with an NPC providing suggestions based on the user's previous interactions and preferences.
## 3 Challenges and Related Recommendation
### Future Research Challenges
#### 3.2.3 Cloud-Edge Computing
Collaboration sensing across multiple nodes in a network and the need for individualized data collection are further factors to think about. Lastly, research into methods of providing extensive advance knowledge for the best scheduling selection of decentralized computational power via the network sensing feature is still required based on the multi-layer ubiquitous computing system. As a result, using exchanged global computing resources to conduct tailored extraction of features and data synthesis on sensor data is difficult. In addition, it would be fascinating to investigate in the future ways to increase the power of ubiquitous computing by improving communication quality.