esa-sceva/satcom-chunk-collection huggingface dataset:
# Context-Aware Gossip-Based Protocol for Internet of Things Applications
## 6 Effects on Application Requirements
### Bandwidth-Intensive IoT Applications  
The smart domiciliary care system considered in this manuscript (see Section 3) offers on-line appointment services that can be utilised by less able residents to ensure timely health checks. When [PERSON] becomes aware of Mr. [PERSON]'s need to visit a urologist, she books an on-line appointment with Mr. [PERSON]'s general practitioner on the same day to ensure that Mr. [PERSON] is referred to the correct specialist by his doctor. This means that there is an enhanced need for bandwidth-saving measures to ensure smooth videoconference communication between Mr. [PERSON] and his general practitioner.  
The parameters sink distance (\(f3\)) proved superior with the lowest percentage of broadcasts and highest percentages of saved rebroadcasts (see Figures 3 and 4). Followed by residual energy (\(f1\)) and message priority (\(f5\)). These results validate their inclusion in the bandwidth-intensive MFWF. Since the parameters were studied in isolation in Section 5, the performance of bandwidth-intensive MFWF was assessed with and without next-node distance (\(f2\)) and node density (\(f4\)). The findings showed that the performance of the MFWF was superior with the inclusion of the two parameters \(f2\) and \(f4\). For that purpose, all five parameters are activated in the MFWF to ensure the reduction of the percentage of rebroadcast nodes while also increasing the percentage of saved rebroadcasts, according to Equation (12) (see Table 4):  
\[w=f_{1}\cdot p_{1}+f_{2}\cdot p_{2}+f_{3}\cdot p_{3}+f_{4}\cdot p_{4}+f_{5} \cdot p_{5} \tag{12}\]where \(p_{1}\) is defined in Equation (2), \(p_{2}\) is defined in Equation (3), \(p_{3}\) is defined in Equation (6), \(p_{4}\) is defined in Equation (7), and \(p_{5}\) is set to 1 for high-priority messages.
## 7 Experimental Results
### Performances Comparison with Original Policy Iteration  
This experiment is done in a virtual machine with Ubuntu 22.04.1 LTS deployed on computer with 16 GB DDR5 memory with frequency of 4000 MHz and Intel Core I/7-14700K CPU and encrypted-state computing is run in PyCharm 2024.3.1.1 with Charm-Crypto 0.50.  
To set up maze environment, we set the space of maze as a \(20\times 10\) grid matrix. The environment build system would randomly choose a grid at the leftmost column to be the beginning and one of the rightmost column as goal. Then, some grid would be set as the wall. In order to ensure a high probability of the existence of a path from the beginning to the goal, we set the probability of a grid to be the wall as \(20\%\).  
Figure 2: This is a wide figure. Schemes follow the same formatting. If there are multiple panels, they should be listed as: **(a)** A solution by policy after 6000 iterations. **(b)** A solution by random policy.  
In theory, the number of iterations needed for the policy to generate the best action chain in our scheme is the same that without encryption. We still run this algorithm under encrypted state of our scheme and original way for 100 times, and the result of both way is shown in Table 2.  
In Table 2, the maze size and wall probability are fixed so that the experiments could be successfully conducted without be stopped by an unsolvable maze frequently. Because the maze is randomly generated, the average steps and number of iterations are not completely equal in our scheme with the original policy iteration without encryption. But they are quite close to each other.  
For the time cost for each round is not evenly distributed as is shown in Figure 3. The difference in time costs is relatively apparent at the beginning but reduced during the policy iteration. At the beginning, the policy is not optimized so that the attempt history is composed by a large number of steps, which leads to a longer time of updating for both method. With the continuous updating of policy, the steps of attempt histories become stable and better, which result in a similar time of proposed scheme and original policy iteration algorithm.  
As is shown in Figure 3, our scheme shows sensitivity to the actual number of stage-action pairs in attempt history, but the time cost of our scheme is not too much comparing to original policy iteration process. Considering that the computation and conversion on ciphertext in our scheme are almost done by server, we can announce that our scheme is usable for actual scenario.
# A Variational Neural Network Based on Algorithm Unfolding for Image Blind Deblurring
## 2 Related Work
### Algorithm Unfolding  
In image restoration, variational models are often used to frame the restoration process as an optimization problem, minimizing a cost function. Traditional methods rely on gradient descent or other optimization algorithms to iteratively solve this problem, but these approaches are computationally expensive and difficult to interpret, especially in complex tasks. To address these limitations, algorithm unfolding has emerged in recent years, combining variational models with deep learning to enhance both efficiency and interpretability. The core idea of algorithm unfolding is to transform the iterative optimization process into a deep neural network, where each layer represents one iteration. After some iterations, the optimization process is mapped to a network with corresponding layers. During training, the network learns the optimal parameters from the optimization algorithm, merging variational methods with deep learning. This approach not only improves computational efficiency but also enhances interpretability by making the optimization process more transparent and offering better insights into the network's evolution, in contrast to traditional black-box models. The flowchart of this algorithm unfolding is shown in Figure 1.
# Mapping with Pleiades--End-to-End Workflow
## 2 Related Work
### Digital Surface Model Generation
#### 2.2.5 DSM Fusion  
As mentioned above multiple DSMs stemming from individual stereo pairs (e.g., from a triple acquisition or neighboring stereo acquisitions) have to be fused into one final DSM. One option is to apply local approaches like mean, median or probability model-based filtering [85]. The other option is to state a global optimization problem to be solved numerically via total variation [87] or via gradient descent [86].
## 9 Discussion and Simulation Results
### Case 1: \(L_{2}n>(1-r)n\)  
The rate \(r\) of the codes whose pairs \(\lambda(x)\) and \(\rho(x)\) were listed in Tables 1-3, is \(1/2\). It follows that, for the examples considered in the paper, \(1-r=1/2\). Thus, the degree distributions fulfilling the condition \(L_{2}n>(1-r)n\) are those with \(L_{2}n>n/2\), i.e., \(L_{2}>1/2\). This condition is fulfilled by the distributions having \(d_{l}=4\), \(6\), and \(8\) of Table 1. This implicates that \(l\leq n/2\) for all these codes, i.e., that \(l\) is fixed. Given this fixed \(l\) value, the performance of the random selected LDPC codes having degree distributions given in the 2-nd, 3-rd, and last column of Table 1, are characterized by an asymptotic BER performance that depends on the probability of Equation (33) only, i.e., only on the value of the product \(\lambda^{\prime}(0)\rho^{\prime}(1)\). Taken these three degree distributions, the random selected LDPC code having a pair \(\lambda(x)\) and \(\rho(x)\) that minimizes Equation (33), and thus is characterized by the best asymptotic BER performance, is the one for which the product \(\lambda^{\prime}(0)\rho^{\prime}(1)\) is minimum. This is the case of the code ensemble with \(d_{l}=8\) in Table 1. On the other hand, the random selected LDPC code having a pair \(\lambda(x)\) and \(\rho(x)\) that maximize Equation (33) is the one for which the product \(\lambda^{\prime}(0)\rho^{\prime}(1)\) is the maximum. This is the case of the code ensemble with \(d_{l}=4\) in Table 1. Moreover, the minimization of the product \(\lambda^{\prime}(0)\rho^{\prime}(1)\) implies, as expected from Equation (39), the maximization of \(\sigma^{*}_{\rm{Iensen}}\) and, thus, a minimization of \(\rm{SNR}_{\rm{gap}}\) and of \(\rm{SNR}_{\rm{gap-spline}}\).  
Figure 1 illustrates the BER performances of two randomly generated codes with pairs \(\lambda(x)\) and \(\rho(x)\) having \(d_{l}=4\) and \(8\), respectively, minimizing and, respectively, maximizing the product \(\lambda^{\prime}(0)\rho^{\prime}(1)\).  
As shown in the figure above, the simulated performance achieved by the codes having \(d_{l}=8\) is better than that achieved by the codes having \(d_{l}=4\). For instance, at \(E_{b}/N_{0}=5\) dB, the BER obtained with \(d_{l}=4\) is \(\sim\)\(3\times 10^{-7}\), whereas the BER obtained with \(d_{l}=8\) is \(\sim\)\(5\times 10^{-8}\).
# An Efficient Neural Network for Shape from Focus with Weight Passing Method
## 5 Experiments  
### Experimental Setup  
The performance of the proposed system consisting of NN.Planar model, WP initial weight setting method is evaluated using image sequences of synthetic and real objects. Synthetic objects plane, sinusoidal, cone, and wave are virtually created by simulation tool. For each object, synthetic image sequence is generated by using a defocus simulation tool box [28] with varying focus settings. Each synthetic image in the sequences is resultant of convolution of actual texture image and Point Spread Function (PSF). Thus, in the synthetic image sequence, the Gaussian function is taken as PSF, camera parameters and the true depth map of the synthetic object are used. For more details, refer to [12; 29]. Image sequences for each synthetic object consists of 80 images (or frames) each with size \(360\times 360\) pixels.  
Figure 6a represents sample frames from image sequences of plane, sinusoidal, cone, and wave objects. From the figure, we can observe that some parts of the objects are well focused and others are out of focus with a degree of blur. And for evaluating the proposed algorithms in real world, we have performed experiments using real objects of real cone, engraved letter I, coin, and TFT-LCD color filter. An image sequence from real cone is obtained by using a CCD camera system with varying focus levels [3], and it consists of 97 images of 200 \(\times\) 200 pixels. Other image sequences from microscopic objects of engraved letter I, coin, and TFT-LCD color filter are acquired by using a microscopic control system [2]. In the case of coin object, the image sequence consists of 68 frames of 300 \(\times\) 300 pixels. And each image sequence from engraved letter I and TFT-LCD color filter consists of 60 images of 300 \(\times\) 300 pixels. Sample frames from each image sequence are represented in Figure 6b.  
In order to produce comparative analysis, first, depth maps have been obtained from image sequences by applying conventional methods and the proposed method. Then, the resultant depth maps are compared qualitatively and quantitatively. We performed experiments for networks NN.FIS and NN.Planar with combinations of RS initial weight setting method [10] and WP initial weight setting method. In the experiments, the numbers of hidden layer \(H\), the total iteration number \(T\), the window size \(M\times M\) are set as 20, 50, 7\(\times\) 7, respectively. Moreover, the learning rate \(\beta\)s: 19.92, 2.35, 6.15, 7.23, and 1.25 are taken for synthetic objects, real cone, engraved letter I, coin, and TFT-LCD color filter, respectively. There is not any specific method to determine the size of the hidden layer \(H\). If \(H\) is too large it suffers from expensive computation. On the other hand, if \(H\) is too small the network may not provide desired results. Therefore, \(H\) is determined empirically through experiments.
## 3 Results and Discussion
### Total Polyphenol Analysis  
Total phenolics were determined after 150 min of extraction. The purpose was to compare the efficiency of the synthesized DESs. The total phenolic content of all DESs extracts was found to be higher than that of ethanol (70% \(v/v\)), methanol (70% \(v/v\)), and water occurring in the same extraction conditions (Figure 3). Comparison carried out among the DESs tested informed about the optimum \(Y_{TP}\) response and conditions to extract polyphenols from olive leaves. The results exhibited the highest antioxidant activity at similar operational extraction conditions in all three DESs, which were \(R_{L/S}\) = 150 mL g\({}^{-1}\), \(C_{\rm DES}\) = 90%, and \(T\) = 80 \({}^{\circ}\)C.  
The DESs tested were not equally efficient with respect to conventional solvents. GL-Lys was found to be the most efficient solvent system for OLL phenol extraction among the DESs tested and the conventional solvents. GL-Arg was slightly better than GL-Pro. The results displayed the \(Y_{TP}\): 188.39, 100.01, and 95.96 mg g\({}^{-1}\) GAE dm for GL-Lys, GL-Arg, and GL-Pro, respectively, compared with 65.95, 62.27, and 52.97 mg g\({}^{-1}\) GAE dm corresponding to ethanol, methanol, and water \(Y_{TP}\), respectively. It should be noted that the extraction with GL-Lys allowed 64%, 66.01%, and 71.09% higher \(Y_{TP}\), compared with that attained with ethanol, methanol, and water in the given order. This outcome indicates the efficiency of GL-Lys among the other DESs tested and the conventional solvents as well regarding polyphenol extraction. This result is in accordance with the literature in which a greater extractability with DESs is demonstrated than with conventional solvents [57]. The reason for this difference could be strongly associated with the physicochemical properties of the amino acids used (HBAs), which would interact more or less favorably with glycerol during the preparation process and also with the polyphenols contained in the plant material during the extraction. These intermolecular forces involve hydrogen bonding and even van der Waals interactions [58].  
Figure 2: The presented 3D Plots show simultaneous variations in (1) \(R_{\text{L/S}}\) and \(C_{\text{DES}}\), (2) \(R_{\text{L/S}}\) and \(T\), and (3) \(C_{\text{DES}}\) and \(T\) on the \(Y_{\text{TP}}\) in all three DESs.
# LDPC Coded Massive MIMO Systems
## 5 Numerical Results  
We considered LDPC coded massive MIMO systems over \(16\times 16\), \(64\times 64\) and \(256\times 256\) channels with code rates of \(R=0.5\) and \(0.75\). Coded bits were mapped to 4-QAM transmit symbols by Gray-mapping. We considered a coded massive MIMO system using GIO JDD with an arbitrary \(N_{g}^{GIO}\) as a reference system, based on which the number of multiplications was evaluated as a function of \(N\) from Table 2. Then, we chose candidate values of \(N_{dec}\) of the proposed JDD strategy such that the total number of multiplications with \(N_{det}=1\) was equivalent to that of GIO JDD, where \(N_{g}\) was determined by \(N_{dec}\) with other given parameters. We solved the optimization problem in Equation (26) for each \(E_{b}/N_{0}\) and \(N_{dec}\) by using the differential evolution algorithm [41]. We found degree distributions and the value of \(N_{dec}\) resulting in the smallest threshold and the rate exceeding the target rate. Note that, in determining degree distributions of LDPC codes, we used the concentrated check node degree distribution [30], i.e., \(\rho(x)=\rho_{d_{c}}x^{d_{c}}+(1-\rho_{d_{c}})x^{d_{c}+1}\). We constructed the parity check matrix of LDPC codes by using the degree distribution and the PEG algorithm. Then, we generated LDPC codes from the obtained parity check matrix and performed BER simulations.
## 3 Simulation and Discussion
### Effect of Initial Position Deviation on the Coupled-MTF  
Affected by the fitting error, there is always an error \(\delta\) in the translation direction between the peak of the input cosine target wave and the initial position of the fiber center. Assuming that the fiber radius \(R=3.2\)\(\upmu\)m, the fiber core layer radius \(r=2.67\)\(\upmu\)m, the number of fibers is \(1000\times 1000\), the image coupling error along the x direction is \(\Delta i=0.5R\), the image coupling error along the y direction is \(\Delta j=0.7R\), the initial position deviation is set to 0\(\sim\)4\(R\) (0\(\sim\)12.8 \(\upmu\)m), and the spatial frequency of the input cosine target is the Nyquist frequency, that is, the \(f=f_{N}\), then the simulation of the coupled-MTF with the initial position variation can be obtained, as shown in Figure 11.  
Figure 11: Simulation of the coupled-MTF with the initial position deviation \(\delta\).  
Figure 10: Simulation of oscillation convergence of the coupled-MTF with the number of fibers when the deviation between the cosine target spatial frequency and Nyquist frequency is 0.1%.  
The simulation results in Figure 11 show that if the input signal is a cosine, the modulation transfer function of the hexagonally aligned fiber image imaging bundle coupled with the area array CCD exhibits periodic oscillation with increase \(\delta\) of the initial position error value of the cosine signal wave peak and the fiber center, and the oscillation period is \(T=4R\), i.e., two fiber diameters. The minimum value is reached at the initial position error value of 6.4, i.e., when \(\delta=2R\), then the coupled-MTF falls to the valley value of 0. The maximum value is reached when the initial position error values are 0 and 12.8, i.e., when \(\delta=0\) or \(\delta=4R\), then the coupled-MTF rises to the peak value of 1.
## 2 Experimental Setup and Detection Method
### Environmental and Experimental Setup  
The apple orchard studied was the Simcoe Research Station, located in Simcoe, Ontario, Canada, and involved a small portion of the research center which consisted of 86.8 hectares of land. The apple orchard that will be utilized for the experiments consisted of two rows of Ambrosia trees on M.9 rootstocks. Each row had 106 trees which had different treatment methods, which resulted in varying fruit flower yields. The trees were grouped by treatment methods with three in each group and two non-treated trees in-between. The Ambrosia trees had an in-row distance of 0.9 m and between-row distance of 3.5 m. The average height of the trees was 2.5 m and the canopy width was 1.2 m. An example of the placement of the Ambrosia trees can be observed in Figure 1.  
Figure 1: Spacing of the Ambrosia trees at the Simcoe Research Station in Ontario, Canada.  
Figure 2 shows a bird's-eye view of the apple orchard. The camera was mounted on a mobile vehicle and was run between the two rows of the Ambrosia trees, placed so the trees are properly in frame of the camera. One side per run was scanned and the number of fruit flower and apples per tree was estimated. There were some challenges with regard to the occlusion of the fruit flower and/or apples. To combat this, either some predictor function was needed or both sides of the apple tree were observed and the results were combined (this is the most realistic solution for farmers). The camera that was used for data collection and online detection was Microsoft's Azure Kinect DK.  
The object detection algorithm, YOLOv4, will be trained on data similar to the one shown in Figure 1 to have a view of the full tree as well as the fruit flower clusters. An example of a fruit flower cluster can be seen in Figure 3. Once fully trained, the algorithm will be tested on data that the algorithm has not seen before, to ensure the algorithm is working properly. Once the algorithm is deemed successful with the dataset, physical testing will be conducted at the Simcoe Research Station using the aforementioned mobile vehicle shown in Figure 4 as the mounting platform.  
Figure 4: Mobile robot platform that was used for the data collection.  
Figure 3: Close-up view of the fruit flowers used in the study.  
Figure 2: Birds-eye view of the apple orchard used in the study.
## 3 Current Status of the Research  
The IoNT has the potential to find solutions to many of the challenging problems the world is currently facing. It functions similarly to how we connect devices on the IoT, the key difference being that it can combine nano-elements, which the IoT cannot. As a result, a groundbreaking revolution could be seen in domains in the near future wherein the IoT is served. Owing to the novelty in the field, only a few works of research have been conducted so far, as the technology is in its infancy age. In order to provide a status of the current research in the field, we provide a summary of the latest research work. In Table 1, we provide the reference, scope of the study, and research contributions made by the researchers. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Scope of the Study** & **Contributions** \\ \hline \multirow{4}{*}{([PERSON] and [PERSON], 2010) [1]} & \multirow{4}{*}{IoNT communication} & The researchers discuss the current state of electromagnetic communication among nanoscale devices, highlighting the major research challenges in terms of channel modeling, information encoding, and protocols for nanonetworks. \\ \cline{3-3}  & & The authors examine the key challenges of the IoNT, including security, privacy, and communication difficulties. The fundamental design of the IoNT has been described, and methods for overcoming the challenges have also been provided. \\ \hline \multirow{4}{*}{([PERSON] et al., 2017) [3]} & \multirow{4}{*}{IoNT communication} & The authors addressed how to route messages across nanonetworks and nano-communications using Forster resonance energy transfer (FRET), which was found to be a technology with very fast signal propagation speed. Based on the biological characteristics of specific molecules, they proposed five novel routing mechanisms, and one of these processes was empirically verified. \\ \cline{1-1} \cline{3-3}  & & The authors addressed how to route messages across nanonetworks and nano-communications using Forster resonance energy transfer (FRET), which was found to be a technology with very fast signal propagation speed. Based on the biological characteristics of specific molecules, they proposed five novel routing mechanisms, and one of these processes was empirically verified. \\ \cline{1-1} \cline{3-3}  & & The authors addressed how to route messages across nanonetworks and nano-communications using Forster resonance energy transfer (FRET), which was found to be a technology with very fast signal propagation speed. Based on the biological characteristics of specific molecules, they proposed five novel routing mechanisms, and one of these processes was empirically verified. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the current status of the latest research.
# MIMO Radio Frequency Identification: A Brief Survey
## 2 Estimation Techniques
Simulation results and experimental analysis using Universal Software Radio Peripheral (USRP) device suggests that the proposed  
Figure 4: (**a**) Full duplex RFID Reader; (**b**) Half duplex RFID Reader. Reproduced from [32] with permission from IEEE. Figure 3: Architecture of the multi receive antenna RFID system. Reproduced from [31] with permission from IEEE. scheme complies with the EPCglobal standard and improves the interrogation range as well as the packet error rate of the system.