esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Synthesis of Hybrid Enzyme-Metal Carriers  
The synthesis results of hybrid enzyme-metal carriers are shown in Figure 3a-f for the different morphologies of the metal tested, including Ti, Co, Mn, Cu, Ca and Zn. Based on the morphology of the carriers (which were flower-shaped), these will be referred to as 'nanoflowers' for simplicity, as in previous investigations [38; 39]. However, in this study, the sizes of the carriers were of the order of several microns. As observed from the SEM images, only the copper nanoflowers were successfully formed; the other metal ions tested failed to produce flower-shaped hybrid carriers [21]. The shape of nanoflowers is essential as it contributes to the high surface-to-volume ratio of the flowers, thus enhancing surface adsorption for accelerating the kinetics of reactions [40]. Despite changing the pH, metal concentration, and enzyme concentration, no flower-shaped carriers were observed when using other metal ions. Several previous studies have also alluded to the failure to develop nanoflowers and have concluded that the formation of nanoflowers is sensitive to experimental conditions [40; 41; 42]. It could therefore be postulated that the optimal conditions for nanoflower formation were not met. This could probably be due to two critical factors in nanoflower synthesis: medium environment [43] and surface chemistry of the enzyme biomolecules [44]. As reported, these two parameters are essential for the organic-inorganic hybrid nucleation, growth and assembly. Thus, the results in the following sections and discussion are based on only the copper hybrid carrier (CA-Cu), whose morphology shown in Figure 3d was similar to that reported by previous studies [21; 25].  
Figure 4a-d shows the flower-like structures for the Cu-CA hybrid, confirmed by SEM images. These showed good dispersity of the petals with three-dimensional microstructures. Specifically, the hybrid \"nanoflowers\" presented a unified structural morphology like a \"Dahlia pinnata\", as depicted in the final image of Figure 4d. Since the inceptive  
Figure 3: SEM images of (**a**)Ti-CA hybrid carriers, (**b**) Co-CA hybrid carriers, (**c**) Mn-CA hybrid carriers, (**d**) Cu-hybrid carriers, (**e**) Ca-CA hybrid carriers, (**f**) Zn–CA hybrid carriers.  
investigations of new nanomaterials in the mid-2000s, different organic-inorganic nanomaterials have been created with different metal ions and organic components exhibiting different morphologies [45]. Previous studies have confirmed that nanoflower morphology is affected by the concentration of reactants and solvent conditions. Thus, there is a plethora of different nanoflower morphologies as can be attested in the literature [23; 40]. Different morphologies of each type of nanoflower have different applications, discussed in the Introduction and Discussion sections.
# Transfer-Efficient Face Routing Using the Planar Graphs of Neighbors in High Density WSNs
## 5 Performance Evaluation  
In this section, we compare the performance of legacy face routing, face routing using 2-hop node information, and the face routing proposed in this paper. In order to compare the independent performance of each, we implemented a simulation with only face routing except greedy routing.
# Content-Based Approach for Improving Bloom Filter Efficiency
## 4 Experiments and Results
### Time Analysis  
In this subsection, we discuss the trend observed for the extra time required by the content bits. Figure 4 shows the variation in the time (in seconds) as more and more content bits are utilized. The required time without employing any of the content bits is just 0.445 s. At least double the amount of time is required if any content bits are used as displayed with the help of bars in blue color. Interestingly, there is no significant increase in processing time observed when the content bits are increased from two to seven. Moreover, the time required by seven bits is slightly less than that (by 0.03 s) required by six bits.
## 5 FPGA Prototyping
### Equalizer FPGA Prototype and Validation  
To measure the exact performance of the designed equalizer architecture, on-chip validation is performed for all system configurations. This step requires, after building the complete system prototype, setting input memories with the right content. Besides the _CMem_, _ChMem_ and \(\frac{1}{x}\) LUT, a new module, the so-called _MapMem_, is established. The _MapMem_ module integrates memory blocks, which are required to contain mapper output information. The contents of the _ChMem_ and _MapMem_ memory blocks are generated automatically from the fixed-point software reference model along with  
Figure 11: Xilinx ISE simulation window showing equalizer architecture output signals.  
a reference result file containing the output of the equalizer. Concerning the control memory _CMem_, the _CWs_ produced by the compiler are utilized. Only the sequence of control signals that are related to the equalizer architecture are extracted and imported to _CMem_. The contents of \(\frac{1}{x}\) LUT are positive 16-bit inverse values represented in the two's complement format. A software model is developed to compute the reciprocal of all possible positive numbers. The generated values are stored such that each memory location contains the quantized inverse value of its address. Figure 12 shows the structure of \(\frac{1}{x}\) LUT. The implementation of \(\frac{1}{x}\) LUT requires a memory size of 64 kB. Figure 12 shows the structure of \(\frac{1}{x}\) LUT. Since \(\frac{1}{x}\) LUT stores positive values, then the most significant bit (MSB) in all stored values is zero. A 4-kBreduction in the size of the \(\frac{1}{x}\) LUT memory block can be achieved by eliminating the MSB. When retrieving an inverse value, the output should be extended by padding a zero in the MSB.  
After generating input memory blocks, ChipScope cores are inserted to the design. The ChipScope Pro Core Inserter tool is used to place cores into the design. Internal logic analyzer units are instantiated. In each unit, the trigger and capture parameters are set. Furthermore, net connections are established to link data, trigger and clock channels to required nets in the architecture design. The design is then placed and routed with the Xilinx ISE implementation software tools. As shown in Figure 13, the generated bitstream is downloaded into the device, and the design is analyzed using a host computer with ChipScope Pro Analyzer software.  
Figure 12: \(\frac{1}{x}\) LUT structure.  
Figure 13: NISC-based MMSE MIMO turbo equalizer architecture on-chip prototype.  
Figure 14 shows the waveform window of ChipScope Pro Analyzer software displaying the waveforms of captured data signals representing the equalizer outputs in the case of \(2\times 2\) block fading mode. The definitions of these signals are discussed previously for Figure 11.
# Nonconvex Nonlinear Transformation of Low-Rank Approximation for Tensor Completion
###### Abstract  
Recovering incomplete high-dimensional data to create complete and valuable datasets is the main focus of tensor completion research, which lies at the intersection of mathematics and information science. Researchers typically apply various linear and nonlinear transformations to the original tensor, using regularization terms like the nuclear norm for low-rank approximation. However, relying solely on the tensor nuclear norm can lead to suboptimal solutions because of the convex relaxation of tensor rank, which strays from the original outcomes. To tackle these issues, we introduce the low-rank approximation nonconvex nonlinear transformation (LRANNT) method. By employing nonconvex norms and nonlinear transformations, we can more accurately capture the intrinsic structure of tensors, providing a more effective solution to the tensor completion problem. Additionally, we propose the proximal alternating minimization (PAM) algorithm to solve the model, demonstrating its convergence. Tests on publicly available datasets demonstrate that our method outperforms the current state-of-the-art approaches, even under extreme conditions with a high missing rate of up to 97.5%.  
nonlinear transformation; proximal alternating minimization; tensor completion; nonconvex; lowrank +
Footnote †: journal: Applied sciences  
1
Footnote 1: School of Sports Engineering, Beijing Sport University, Beijing 100084, China; [EMAIL_ADDRESS] (Y.M.); [EMAIL_ADDRESS] (H.L.); [EMAIL_ADDRESS] (H.G.)
# Fast Simulation of Laser Heating Processes on Thin Metal Plates with FFT Using CPU/GPU Hardware
## 4 Results
### Computational Performance  
This section evaluates the performance of the proposed methods under CPU and GPU hardware architectures by making use of highly optimized FFT libraries. The Python programming language includes in its scientific package ecosystem high level wrappers to C/C++ libraries. For this reason, Python has been selected for the rapid prototyping of the proposed schemes in this work.  
The FFT algorithm is used in a wide range of performance demanding applications. Therefore, the optimization degree of its implementation is highly relevant. On the one hand, to target the CPU, the FTTPACK, MKL and FFTW libraries have been selected. On the other hand, to target the GPU, the cuFFT library from the NVIDIA CUDA Toolkit has been used. All these libraries make use of multi-core parallelization, vectorization instructions, efficient memory usage, and apply specific FFT algorithms to exploit the underlying hardware to the highest degree. It is worth noticing that the FFTPACK library is the only one (between the aforementioned ones) that provides an implementation of the DST.  
Table 3 summarizes the selected libraries along the Python wrapper packages and the targeted hardware device during the performance tests.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Library** & **Python Package** & **Hardware** \\ \hline FFTPACK & scipy.fft & CPU \\ MKL & numpy.fft & CPU \\ FFTW & pyffw & CPU \\ cuFFT & pyCUDA, scikit-cuda & GPU \\ \hline \hline \end{tabular}
\end{table}
Table 3: Selected libraries and corresponding Python packages.  
Figure 9: Temperature and absolute error distributions (w.r.t. the brute-force approach [24]) on the thin plates for the odd symmetry FFT approaches (1D and 2D).  
Two test platforms have been used for the performance measurements: _(i)_ a desktop PC using Windows 10 with an Intel Core i5-6500 (CPU), 16 GB RAM and NVIDIA GeForce GTX 960 (GPU) and _(ii)_ a desktop PC using Manjaro (GNU/ Linux) with an Intel Core i7-4700K (CPU), 16 GB RAM and NVIDIA GeForce RTX 2060 (GPU). To measure the execution times of each proposed method, each test has been computed 5 times and the minimum time has been registered.  
This section is divided into four subsections. Section 4.2.1 presents the computation times using the CPU, while Section 4.2.2 presents the computational times using GPU hardware. Then, Section 4.2.3 compares the performance difference between both devices. Finally, Section 4.2.4 presents the achieved speed-up against the state of the art brute-force solution [22; 23; 24; 25].
## 4 Case Study and Discussion  
### Coupling Correlation of Complex System  
#### 4.1.1 Hierarchical Model and Description  
1. Hierarchical model  
Integrated modular avionics (IMA) [78; 79] is a shared set of flexible, reusable, and interoperable hardware and software resources. When integrated, these resources can form a platform that provides service, designed and verified to a defined set of safety and performance requirements, to host applications performing aircraft functions [80]. Based on ASAAC criterion [81], IMA system is managed by a three-layer model: Aircraft Level (AL), Integration Area Level (IAL) and Resource Element Level (REL). This three-level hierarchy of IMA is typical task-function-resource model.  
On the basis of the initial design plan of a certain aircraft, this integrated modular avionics (IMA) system contains three functions: navigation, communication, and integrated management; nine system resources: GPM (Graphics Processing Module), GPM, DPM (Data Processing Module), DPM, SPM (Signal Processing Module), SPM, PCM (Power Conversion Module), PCM, NSM (Network Support Module). The IMA system task-function-resource mapping relationship and details of function-resource mapping relationship are shown in Figure 5 and Table 3.  
It is generally considered that in the IMA system, the top-level system functional entities are unique, and it can be considered that there is only one element in the IMA system task set \(T=\{t_{1}\}\), which is aimed to complete the management of the entire IMA system to support the operation of the  
Figure 5: Task-function-resource model of the Integrated modular avionics (IMA) system. GPM, Graphics Processing Module; DPM, Data Processing Module; SPM, Signal Processing Module; PCM, Power Conversion Module; NSM, Network Support Module.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Function Layer**} & \multicolumn{4}{c}{**Resource Layer/Resource Serial Number**} \\ \cline{2-5}  & **GPM** & **DPM** & **SPM** & **PCM** & **NSM** \\ \hline navigation & 1, 2 & 3, 4 & / & / & / \\ communication & / & 3, 4 & 5, 6 & 8 & / \\ integrated management & / & & 5, 6 & 7, 8 & 9 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Details of function-resource mapping relationship of the IMA system.  
system. Therefore, it can be ignored. Then, function element set \(F=\{f_{1},f_{2},\ f_{3}\}\); resource element set \(R=\{r_{1},r_{2},\ \ldots,\ r_{9}\}\).  
The safety critical grade \(SCG\) of the resource elements is divided into 3 levels (larger numbers indicate higher \(SCG\)), and based on experience set \(SCG_{1\times 9}=\{1,\ 2,\ 3,\ 3,\ 2,\ 1,\ 2,\ 2,\ 3\}\).  
2. Coupling degree Matrix  
Function-resource element mapping coupling matrix \(M^{fr}\) is presented in Matrix (18).  
\[M^{fr}=\left(M^{fr}_{ij}\right)_{3\times 9}=\left[\begin{array}{ccccccccc}1&1&1&1&0&0& 0&0&0\\ 0&0&1&1&1&1&0&1&0\\ 0&0&0&0&1&1&1&1&1\end{array}\right] \tag{18}\]
## 2 Cluster Head Selection
### Assisted Schemes
#### 2.2.2 Energy Expenditure Mitigation in Re-Clustering
The only difference of RRCH and LEACH-F concerns the coordinator node that is responsible for sending the sequence numbers to the cluster members, which is CH in RRCH and BS in LEACH-F.  
Another approach is Energy Balanced Clustering (EBC), in which re-clustering decisions are correlated to the traffic load processed by the CH in a round. According to the paper [57], re-clustering happens based on the burden of traffic load on CH during each round, not based on a predefined time schedule. The paper advocates the idea of restricting the number of re-clustering cycles only to the rounds it is essential and in this way, it saves remarkable proportions of energy resources of nodes spent in re-clustering stages. In this respect, each node sends details of its remaining energy with sensed data to the CH after initial setup phase. When the amount of data received at a CH exceeds a predefined threshold, the current CH selects next round CH based on the energy level of its cluster members and broadcasts a message about the new chosen CH. The proposed protocols in [58; 59; 60; 61; 62] utilize redundant and backup CHs to improve network performance and to decrease extra energy expenditures in re-clustering phases. Selecting two nodes having the highest energy levels as the potential CHs in initial round and assigning one as the premier CH and the other one as the redundant CH is proposed by [58]. According to the algorithm, nodes are ranked regarding their remaining energy and sum of hops from other nodes; the first rank node is selected as the main CH and the node ranked in the next position is assigned as the redundant CH. To determine the degree of sum of hops, both CH candidates use flooding of broadcasting status information. A list of minimum hop distances from all nodes will be generated and transmitted to the BS to calculate the sum of hops of two candidate nodes. The BS selects the optimum nodes as CHs and informs the entire network about their selection. The redundant CH dynamically updates the cluster information with the master CH, to alternate in case of any failure of CH due to damage, attack or energy depletion. The same approach is proposed in [59] with an additional stage that redundant CH periodically sends a beacon message to CH and counts the number of acknowledgments it does not receive from CH. As soon as the fault counter exceeds a given threshold or current CH remaining energy descends a predefined level, the redundant CH takes over the task of master CH. In [60], the BS selects the Main CHs based on sensor nodes' remaining energy, location and frequency once selected as CH. Then, the main CHs select the Alternative CHs and Vice-CHs. The Main CHs are responsible for inter-cluster data transmission, while Vice-CHs do data collection and aggregation. Upon the decrease of the remaining energy of Main CHs or Vice-CHs to half of the initial energy, Alternate CHs take their place.
## 3 Results and Discussion
### Comparison with Spectrophotometry
#### 3.4.1 Comparison Results for Standard Solution  
The proposed method and spectrophotometry were used to determine the standard solutions of VC with 1, 1.5, 2, 2.5, 3, 3.5, and 4 \(\upmu\)g/mL, respectively. After independent-sample T-testing of the VC concentrations, the P-value was obtained. The results show that the data from the measuring methods of L, grayscale, log-grayscale, and spectrophotometry were not significantly different from the standard VC concentrations. Table 3 shows the measured concentrations, P-values, and standard deviation (SD) of the four methods.  
In Table 3, the method L was closer to the standard VC concentration and had a higher accuracy. The methods of L, grayscale, and log-grayscale can be all used for the VC concentration measurement, and although gray and log-grayscale were less accurate than other methods, we suggest using gray or log-grayscale, as the transformation from RGB to Lab space was very time-consuming.
###### Abstract  
This paper presents a virtual-force-based guidance law (VFGL) for path following and obstacle avoidance in unmanned aerial vehicles. First, a virtual spring force and a virtual drag force are designed for straight-line following; then, the dynamic of the cross-track-error is equivalent to a spring mass system, which is easy to tune to acquire stability and non-overshoot convergence. Secondly, an additional virtual centripetal force is designed to counteract the influence of the curvature of the planned path so that the guidance law can accurately track a curve with a time-varying curvature. Thirdly, an extra virtual repulsive force is designed directly according to the sensor inputs; the virtual repulsive force pushes the vehicle away to move around obstacles. The use of artificial physics means the guidance law is founded on solid physical theory and is computationally simple. The physical meanings of the parameters are definite, and the VFGL has a large parameter adaptation. These make the guidance law easy to tune in application. Both the numerical and hardware-in-the-loop simulation results demonstrated the effectiveness of the proposed guidance law for path following and obstacle avoidance in unmanned aerial vehicles.  
unmanned aerial vehicles; path following; obstacle avoidance; virtual-force-based guidance law +
Footnote †: journal: Applied  
Article Path Following and Obstacle Avoidance for Unmanned Aerial Vehicles Using a Virtual-Force-Based Guidance Law.
# Traffic Prediction of Space-Integrated Ground Information Network Using the GTCN Algorithm
## 4 GTCN Algorithm
### Network Traffic Data Segmentation  
We perform segmentation of network traffic data to enable TCN in predicting the traffic rapidly and accurately when processing large samples of the network traffic time series, without consuming excessive computing resources. The entire network traffic data were divided into equal-length segments and each segment was trained separately using TCN. To preserve the correlation between each segment, we applied the self-correlation degree of network traffic data as the basis of segmentation. Self-correlation is an important attribute of a single sequence in a time series, allowing us to observe the lag variable that is used to predict the reference, that is, the degree of correlation between previous data and a certain sampling point. The degree of autocorrelation is determined by the self-correlation and the calculation of the autocorrelation coefficient is as follows:  
\[R(n)=\frac{\sum\limits_{i=1}^{n-h}\left[(x_{i}-\mu)(x_{i}+h-\mu)\right]}{\sum \limits_{t=1}^{n}\left(x_{i}-\mu\right)^{2}} \tag{3}\]  
The autocorrelation function of the time series is the set of several order autocorrelation coefficients, where \(n\) is the data volume, \(H\) is the order of the autocorrelation coefficient, and \(\mu\) is the mean value of the time series. The autocorrelation coefficient value is between \(-1\) and \(1\). When the absolute value of the autocorrelation coefficient is closer to \(1\), a strong  
Figure 4: TCN Network Residual Block.  
correlation of the time series is shown; on the other hand, when the absolute value of the autocorrelation coefficient is closer to 0, a weak correlation is shown.  
As a criterion to evaluate the influence of past time nodes on current time node, the autocorrelation coefficient can help us to determine the sample size of each segment well. Since the relevance usually has a small sample size, the strong correlation between the sample size is smaller. To avoid excessive piecewise gating control unit update reset too frequently, weak correlations with large values of sequence are included in the reference and the relevance ratio of the sample size, as the sample size of each paragraph does not terminate the network traffic in achieving relevance and enhancing the training efficiency. Because of the self-similarity of the traffic data of the Space-Integrated-Ground Information Intelligent Network, there is no need to create a separate model for each data segment, instead, the same training model can be used for every data segment.