esa-sceva/satcom-chunk-collection huggingface dataset:
# Bounce: A High Performance Satellite-Based Blockchain System
## 6 Experiments, Results, and Analysis  
All experiments, unless otherwise noted, were conducted on CloudLab, utilizing the c220g2 node type [[https://docs.cloudlab.us/hardware.html](https://docs.cloudlab.us/hardware.html)]([https://docs.cloudlab.us/hardware.html](https://docs.cloudlab.us/hardware.html)) (last accessed 7 March 2025) for Sending Stations and Broadcast Ground Stations and a Raspberry Pi 4 in the (eventually satellite-carried) Bounce Unit. We chose the Raspberry Pi because these are frequently used onboard satellites [[https://www.raspberrypi.com/news/raspberry-pi-zero-powers-cubesat-space-mission/](https://www.raspberrypi.com/news/raspberry-pi-zero-powers-cubesat-space-mission/)]([https://www.raspberrypi.com/news/raspberry-pi-zero-powers-cubesat-space-mission/](https://www.raspberrypi.com/news/raspberry-pi-zero-powers-cubesat-space-mission/)) (last accessed 7 March 2025). The main parameter values are presented in Table 2.  
The energy consumption of c220g2 type servers can be calculated from the processor type. It has two Intel E5-2660 v3 CPUs, each with a 105 W Thermal Design Power, which gives 210 W in total. [[https://www.intel.com/content/www/us/en/products/sku/8170](https://www.intel.com/content/www/us/en/products/sku/8170)]([https://www.intel.com/content/www/us/en/products/sku/8170](https://www.intel.com/content/www/us/en/products/sku/8170)) 6/intel-xeon-processor-e52660-v3-25m-cache-2-60-ghz/specifications.html (last accessed 7 March 2025).  
Therefore, a setting with say 100 such processors is the equivalent of about 210 light bulbs, that is 21 kilowatts. This comes to a yearly consumption of 184 megawatt-hours, about the energy load of 20 homes in the United States. The satellites themselves run on solar energy, so incur no extra energy costs and earth to satellite transmissions require under 100 watts. In terms of energy per transaction, this comes to under 0.05 joules per transaction, while other systems use between 10s and millions of joules per transaction.  
\begin{table}
\begin{tabular}{l l l l l l} \hline
**Experimental** & \(f\) **(Number of Broadcast Ground** & **Economy Merkle** & **Premium Merkle** & **Fanout** & **Hardware** \\
**Setups** & **Stations that Might Fail)** & **Tree Size** & **Tree Size** & **Fanout** & **Hardware** \\ \hline
**Section 6.2** & 18 & 1 million & 100 thousand & 3 & c220g2 \\ \hline
**Section 6.3** & one less than 80\% of the servers & 1 million & 100 thousand & 3 & c220g2, c6525-25g, r6525 \\  & within a cluster & & & & & r6525 \\ \hline \end{tabular}
\end{table}
Table 2: Hardware and parameter settings in the experiments. c220g2, c6525-25g, r6525 are hardware types in CloudLab. \(f\) is an assumed maximum of the number of Broadcast Ground Stations that can fail either cleanly or traitorously. The fanout is the fanout of the overlay tree. The hardware specifications can be found here [[https://docs.cloudlab.us/hardware.html](https://docs.cloudlab.us/hardware.html)]([https://docs.cloudlab.us/hardware.html](https://docs.cloudlab.us/hardware.html)) (last accessed 7 March 2025).
# Three-Dimensional Wide-Band Electromagnetic Forward Modelling Using Potential Technique
## 3 Results and Discussion
### Model 3  
In this section, we compute the EM response for a three-dimensional (3-D) crustal model. For this model, the (two) anomalous rectangular bodies are situated at the third-layer of the (five) background horizons with resistivity values of 0.8 and 80 \(\Omega\) m. The size of both anomalies are 60 \(\times\) 40 \(\times\) 4 km\({}^{3}\) and 20 \(\times\) 40 \(\times\) 4 km\({}^{3}\) respectively (Figure 9).  
The computed grid domain is discretized into 60 \(\times\) 53 \(\times\) 28 cells in the x, y, and z-directions. Both horizontal direction is 115 km while the vertical extent is 185 km. A plane wave source at frequency of 0.001 Hz was used for the model. Figure 10 indicate the plot of the real and imaginary part of the normalized electric field for the \(E^{x}\) polarization from our potential algorithm and direct E approach. The results are in good agreement with each other. Both imaginary and real parts of the \(E^{x}\) reflect the horizontal changes in the model structure with sharp changes at the resistivity discontinuities. The real and imaginary part of the magnetic field for the \(E^{y}\) polarization from our potential algorithm and direct \(E\) formulation are shown in Figure 11, again indicating that their results are consistent.  
Additionally, we also examined the BiCGStab, and QMR solvers with various preconditioners (Block ILU, SSOR, and SGS) on this model. Moreover, as displayed in Figures 12 and 13, the Block ILU pre-conditioner works exclusively better than the other preconditioner in this example. For this model,  
Figure 10: The plot of the real and imaginary component for the normalized electric field of the \(E^{x}\) polarization calculated using the Potentials and Direct E formulation.  
Figure 9: The planar and vertical cross-section view of the model 3. The anomalous bodies are embedded in the middle homogenous layer.  
BiCGStab with the Block ILU preconditioner outperforms the corresponding QMR, e.g., the former one converges after 220 iterations while 395 iterations for QMR. Thus, the BiCGStab iterative solver preconditioned with the Block ILU shows a better preference for this test model.  
Figure 11: The plot of the real and imaginary component for the magnetic field of the \(E^{y}\) polarization calculated using the Potential and Direct E formulation.  
Figure 12: The convergence plot of different preconditioners with BiCGStab for the model 3.  
Figure 13: The convergence plot of different preconditioners with QMR for the model 3.
# A Tweak-Cube Color Image Encryption Scheme Jointly Manipulated by Chaos and Hyper-Chaos
## 5 Performance Analysis
### Key Sensitivity Analysis  
The key sensitivity analysis is designed to observe the impact of small changes in the keys on cipher images. As for a chaotic map, the nuances of the initial conditions can produce two completely different sequences. Thus, we just changed initial value \(\mathrm{x_{10}}\) with \(10^{-14}\), then we re-encrypted the original images in Figure 5(a). The cipher images in Figure 5(a) were obtained. Figure 5(b) exhibits the pixel-by-pixel difference between Figures 5(b) and 5(a), and it can be discovered that a subtle change to  
Figure 6: Encryption results: (**a**) the original images; (**b**) the encrypted images; (**c**) the decrypted images.  
the keys can result in a completely different encryption. Moreover, Table 4 lists the difference rates between two encrypted images with only one of the keys changed, and these data are greater than 99.5%. Therefore, we can conclude that our encryption scheme has strong key sensitivity.
## 3 Methods  
### System Composition  
Figure 7 shows the structure diagram of the airborne laser communication experiment. The ground transmitting end is composed of a 650 nm laser, signal modulator, transmitting antenna, tracking camera, PC, and servo pan-tilt; the relay platform is composed of two-dimensional mirror, motor and its driver, position sensor, photodetector, and single-chip microcomputer controller. After the coarse alignment from the transmitting antenna to the two-dimensional mirror, the camera is used to select the image of the UAV, and the pitch angle and azimuth angle of the pan-tilt are adjusted according to the moving position information of the UAV so that the beam completely covers the relay system surface. The two-dimensional mirror executes the scan capture program until there is a light spot on the surface of the four-quadrant detector, and then the motor slows down for fine alignment so that the beam passes through a hole in the center of the four-quadrant detector surface. At this time, the light beam reaches the surface of the photodetector, and the signal light is loaded to a new laser through the electrical-optical conversion circuit, and the optical signal is reflected to the ground receiving end through the beam splitter prism and the two-dimensional mirror. Table 1 shows the main equipment and parameters of the experiment.  
Figure 7: Structure diagram of airborne laser communication experiment.  
Figure 8 is a schematic diagram of the airborne laser communication field experiment. The ground transmitting end and the airborne end are located on the north side and the center of the Xi'an University of Technology playground, respectively. The experiment time is 29 May 2023. The weather is fine, the temperature is 18 \({}^{\circ}\)C, and the northeast wind is 3-4.
## 4 Simulation and Fpga Implementation
### FPGA Implementation  
Implementation is demonstrated on a Xilinx XC7 VX485T FPGA chip. 128 parallel ROMs are employed to store the source data as equivalent to the 10 GHz ADCs. In order to evaluate the performance difference between simulation and FPGA implementation, periodic source is embedded into the ROMs. The write and read clock of parallel FIFO module are set to 156.268 MHz and 159.524 MHz respectively by an Mixed Mode Clock Manager (MMCM). The device utilization of the whole system is summarized in Table 3.  
The output of fractional interval is depicted in Figure 7(a). Where, '\(*\)' and '\(\Delta\)' represent MATLAB fix point simulation and hardware behavior simulation respectively. It is easy to find out that these two signals are totally overlapped, which indicates the high efficiency of hardware design. To further verify the accuracy, the difference value of the two aforementioned signals is exhibited in Figure 7(b). The constant zero proves the NCO output in MATLAB is exactly the same as in hardware behavior simulation. Moreover, not only fractional interval, but all the signals achieved in behavior simulation is exactly the same as those in MATLAB, which confirmed the correctness and effectiveness.  
The constellation diagram achieved by Xilinx XC7 VX485T FPGA chip is displayed in Figure 8(a). Figure 8(b) shows the difference value (image part) of the interpolator's output of behavior simulation and FPGA implementation.  
Figure 8: Output and difference value of fractional interval with MATLAB fixed point simulation and behavior simulation.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Resource** & **Utilization** & **Available** & **Utilization\%** \\ \hline LUT & 35,228 & 303,600 & 11.60 \\ LUTRAM & 1375 & 130,800 & 1.05 \\ FF & 55,950 & 607,200 & 9.21 \\ BRAM & 143 & 1030 & 13.88 \\ DSP48e & 480 & 2800 & 17.14 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Resource utilization comparisonThe difference here can not be guaranteed to always be zero because the initial source signals are impossible to be precisely controlled on an FPGA chip. However, the difference value is always less than 2 from about the 15,000 th datum. This means only quantization error less than 1.5% will be brought in for an 8-bit signal and further confirms that the proposed algorithm can work effectively on FPGA.
## 2 Related Work
### SLAM with Semantic Prior  
The ability of deep learning-based image segmentation methods (e.g., Yolov3 [29] and Mask R-CNN [30]) to provide semantic priors has inspired the development of SLAM. In the context of SLAM, \"semantic prior\" refers to the use of semantic information, such as object categories, to aid in the localization and mapping process. This can be especially useful in dynamic environments where objects are moving and can disrupt the SLAM process.  
DynaSLAM [9] uses semantic prior information for dynamic scene verification. When objects in the image are identified as potentially moving, such as people, animals, and cars, features located on these objects are considered dynamic features and removed. While using semantic information to detect dynamic feature points is straightforward, it has limitations. The semantic dynamic feature points do not always match the actual dynamic feature points. To overcome this limitation, some approaches combine semantic information and multi-view geometry.  
DS-SLAM [11] is one such method that uses SegNet to obtain semantic labels of feature points in a separate thread and further uses polar geometric constraints to check shift consistency after the feature points are classified as potential dynamic feature points to discriminate whether they are dynamic feature points or not. [PERSON] [27] applies semantic segmentation and MASK R-CNN networks to implement acquisition and filtering methods for panoptic information to provide pose estimation of objects, respectively.  
In addition, there are also object SLAM methods based on semantic prior, such as MaskFusion [31] and SO-SLAM [32]. These methods use semantic prior information to extract and model static objects as landmarks, including rectangular or quadratic curves. Then, they use these landmarks to complete the SLAM process. Of course, some works also incorporate dynamic objects that can be added to the back end of SLAM for joint optimization, such as [33; 34]. However, the SLAM approaches mentioned above are typically limited to visual SLAM and are not common in multi-sensor fusion SLAM.  
Compared to the aforementioned methods, this paper proposes a novel approach that begins with multi-sensor fusion SLAM and integrates the panoptic segmentation method. The proposed method achieves greater robustness and accuracy in removing the impact of dynamic objects on pose estimation and enables the estimation of the 6D pose of dynamic objects.
## 4 Proposed Data Collection Algorithms
### Probabilistic Neighborhood Covering Set-Based Greedy Heuristic Algorithm (PNCS-GHA)  
For 3D UASNs with pre-known deployment knowledge, we propose the PNCS-GHA for data collection. Similarly, our PNCS-GHA also contains three phases. In the first phase, we construct a minimum probabilistic neighborhood covering set as resident points according to the probabilistic neighborhood contour. Then, we employ the Nearest-neighbor Heuristic strategy which greedily moves to the nearest neighborhood to traverse all the covering set nodes to collect data within their probabilistic neighborhoods. After planning the data collection tour, the AUV travels along the specified path to collect data from the whole network. The AUV stores the visit status information of PNCS nodes. Once all the PNCS nodes have been visited, the AUV terminates current data collection travel and directly returns to the surface control center to prepare for the next travel. The third phase is highly similar to the data collection phase of the GPN-LSCAN, so we only discuss the probabilistic neighborhood covering set construction phase and AUV trajectory planning phase in detail.
# Fourier Domain Adaptation for the Identification of Grape Leaf Diseases
## 3 Results
### Training Details  
The experiment uses Pytorch as the deep learning framework for Python and uses NVIDIA GeForce RTX 3080 Ti to build and implement the network model. The acceleration process of this experiment is realized by calling a graphics processing unit (GPU). Table 2 shows the workstation specifications.  
With the purpose of the availability of the proposed grape leaf disease identification method based on Fourier domain adaptation, we conducted three types of experiments. The first experiment is the parameter adjustment experiment about the Fourier transform. The training set consists of the source domain image and the style transformation image containing the features from the target domain and label from the source domain after one Fourier transform; the test set is the target domain dataset. There are two sub-experiments in this experiment: (1) \(\beta\) selected 0.01, CNN network selected Alexnet. The learning rate has four parameters of 0.0001, 0.0005, 0.001, and 0.01 and the batch_size has four parameters of 4, 8, 16, and 32, which are one-to-one correspondence. After 16 experiments, two groups of batch_size and learning rate combinations with better training effects were obtained; (2) \(\beta\) has three parameters of 0.01, 0.05, and 0.09, epochs have three parameters of 50, 100, and 200, and the CNN network has three networks of Alexnet, Vgg13, and Resnet101. Combined with the two combined parameters obtained in the experiment, (3) the best grape leaf disease identification model with single-scale Fourier domain adaptation was obtained after 54 groups of experiments.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Hardware** & **Software** \\ \hline CPU: Intel Core i9-10900K & Windows10 \\ RAM: 128 GB & CUDA12.1.112 + CUDNN8.0 \\ GPU: NVIDIA GeForce RTX 3080 Ti & Pytorch Stable (2.3.0) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Workstation specifications.  
The second experiment is a comparative experiment. The source domain image without Fourier transform is trained in the same Vgg13 network under the same parameters and conditions. The results obtained are compared with the results of Fourier transform training, which proves that the proposed method has practical significance and effects.  
In the third experiment, in order to eliminate the randomness of the model, we propose a fusion farthest point sampling algorithm. Three images with far feature distance are selected in the target domain dataset and their features are adaptively transferred to the source domain dataset through the Fourier domain. In total, 1434 images are obtained and 478 images in the source domain are added to obtain 1912 training set images, which are put into Vgg13 for training and tested with target domain images.  
In the grape leaf disease identification experiment, the performance of the method was evaluated by accuracy, which is the percentage of the predicted correct sample in the total sample.
# Cooperative Communication Based Protocols for Underwater Wireless Sensors Networks: A Review
## 1 Introduction
### Paper Organization and Terminology  
The paper is organized as follows. In Section 2 the physical layer mechanisms for diversity combining in cooperative communication are presented. Error and medium access control strategies for underwater cooperative scenarios are reported in Section 3. Network performance optimization through clustering and routing is discussed in Section 4. Performance discussion and some numerical results are provided in Section 5. Section 6 highlights the open issues and new research directions related to cooperative UWSNs supported by machine learning. Finally, conclusion is drawn in Section 7.  
When dealing with physical layer issues, the amount of transmitted information is considered as a continuous flow of bits encoded in acoustic signals based on a certain modulation format. Passing to the link layer, information is formally organized in frames, composed of overhead and payload parts. At the network layer, each data unit is instead referred as packet and, following well known rules, is encapsulated in the payload of the link layer frame. Although the terms \"frame\" and \"packet\" related to different layers of the protocol stack have clear and different meanings, very often the term packet is indifferently used in both cases. Hence, in order to match with the most widespread terminology, in the rest of the paper the term packet will be used to identify the transmission data unit, but specifying whether referring to the link or network layer.
## 3 System Architecture and Modeling
### System Layout  
This section provides a detailed description of the HEMS. The system architecture of the HEMS is presented in the Figure 1. The main components of the HEMS are described below.  
* The first component of the HEMS is the forecaster. It is deputized to make predictions about the external weather and the heat-gains due to solar irradiance in the house. Data-driven prediction techniques [46; 47] are commonly used to make these predictions based on available historical data about the variable. The second component of the HEMS uses these predictions to optimally schedule the thermal appliances.
* The second and the key component of the HEMS is the Predictive MILP controller. This component operates in two MOO, which is determined automatically within the component based upon the electrical load categories (see Section 3.1). In each mode, a different mathematical optimization problem is solved by exploiting the predictions made by the forecaster about the weather conditions. The electricity prices and the consumption-capacity profile is provided to HEMS by the smart-grid operator using smart-meter communication.
## 2 Classifier Design
### Proposed PL-MSMM
#### 2.3.1 Solving Subproblem \(\mathcal{Z}\)  
First, the objective function with respect to \(\mathcal{Z}\) can be minimized by fixing \(\mathcal{W}\). \(L_{\mathcal{Z}}\) can be defined as the aggregate of all \(\mathcal{Z}\)-related terms in (11), as follows:  
\[\min_{\mathcal{Z}}\,L_{\mathcal{Z}}=Q(\mathcal{Z})+\langle\Lambda,\mathcal{Z }\rangle+\frac{\rho}{2}\parallel\mathcal{Z}-\mathcal{W}\parallel_{F}^{2}. \tag{15}\]  
Equation (15) can be solved by minimizing \(L_{\mathcal{Z}}\). As \(L_{\mathcal{Z}}\) is a non-differentiable convex function, its sub-gradient can be calculated as follows:  
\[\partial L_{\mathcal{Z}}\big{(}\mathbf{Z}_{j}\big{)}=\mathbf{Z}_{j}+\tau \partial\big{\|}\mathbf{Z}_{j}\big{\|}_{*}+\mathbf{\lambda}_{j}+\rho\big{(} \mathbf{Z}_{j}-\mathbf{W}_{j}\big{)}, \tag{16}\]  
where \(\mathbf{Z}_{j}\), \(\mathbf{W}_{j}\), and \(\mathbf{\lambda}_{j}\) denote the matrix-form hyperplanes of the \(j\)-class data for \(\mathcal{Z}\), \(\mathcal{W}\), and \(\Lambda\), respectively. \(\partial\big{\|}\mathbf{Z}_{j}\big{\|}_{*}\) denotes the sub-gradient of the kernel norm.  
The optimal solution can be represented as a singular value-thresholding operator [29], as follows:  
\[\mathbf{Z}_{j}^{*}=\frac{1}{1+\rho}\mathbb{D}_{\tau}\big{(}\rho\mathbf{W}_{j}- \mathbf{\lambda}_{j}\big{)}, \tag{17}\]  
where \(\mathbb{D}_{\tau}\) denotes the singular value-thresholding operator.  
\[\mathbb{D}_{\tau}(\mathbf{A})=\mathbf{U}\mathbf{\Sigma}_{\tau}\mathbf{V}^{T},\]  
where \(\mathbf{\Sigma}_{\tau}=\text{diag}\big{(}[\sigma_{1}(\mathbf{A})-\tau]_{+},.....,[\sigma_{r}(\mathbf{A})-\tau]_{+}\big{)}\).
## 3 Operational Requirements
### Environmental Conditions
#### 3.3.3 Environmental Factors  
Variable lighting due to the sun, plant shadows, or clouds can affect the effectiveness of the robot's vision systems. Advanced image processing systems must compensate for these changes to maintain accuracy [52]. The presence of moisture or rain can affect the robot's sensors and electronics, as well as complicating the terrain and making it more difficult to get around. Extreme temperatures can affect the performance and durability of the robot's mechanical and electronic components, requiring materials and designs that withstand these conditions. In dusty or dirty environments, the robot's sensors and mobility systems may be affected. Automatic cleaning systems and sensor protection are essential to maintain operability.