esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The RZE Method for Rainfall Detection and Rainfall Intensity Level Retrieval
### Rainfall Intensity Level Retrieval
In the fitting of each order function, the RZE parameters are substituted into the fitted function to obtain the theoretical value of rainfall intensity, and the fitting error of rainfall intensity is obtained by subtracting the theoretical value with the actual value of rainfall intensity. Figure 10 shows the fitting function and error of each order. The accuracy of fitting can be improved with the increase of the function order, but over fitting will reduce the stability of the function. The error of the second order fitting function is much lower than that of the first order fitting function, especially when the rainfall intensity level is light rain or heavy rain. The error of the second order fitting function is basically in the range of \(-0.15\)-\(0.15\) mm/\(10\) min, but the error value is larger when the rainfall intensity level is moderate rain. The error of the third order fitting function is lower than that of the second order fitting function, especially when the rainfall intensity is greater than \(0.9\) mm/\(10\) min, so the fitting accuracy of the third order function is higher. The error of the fourth order fitting function increases when the rainfall intensity is less than \(0.1\) mm/\(10\) min and greater than \(1.1\) mm/\(10\) min, which indicates that the fourth order function has over fitting phenomenon. Therefore, the third order fitting function is used as the fitting function for rainfall intensity level retrieval, and the fitting function is shown as  
\[H=a_{1}\gamma^{3}+a_{2}\gamma^{2}+a_{3}\gamma+a_{4} \tag{8}\]  
\[\left\{ \begin{array}{l}a_{1}=1.123\times 10^{-0.8}\\ a_{2}=-2.839\times 10^{-0.6}\\ a_{3}=-0.003377\\ a_{4}=1.139\end{array} \right. \tag{9}\]  
where \(a_{1},i=1,2,3,4\) are not fixed values with different selected radar image data, and the \(a_{1},i=1,2,3,4\) in this fitting is shown in Equation (9). \(\gamma\) is the RZE parameter value of the selected rain-contaminated radar image, and \(H\) is the value of rainfall intensity. The rainfall intensity level of the selected rain-contaminated radar image can be determined by Table 2.  
Figure 9: Scatterplot of the outliers removed. (**a**) 1000 groups of data; (**b**) 500 groups of data. Figure 10: The fitting function and error graph of each order. (**a**) The fitting function of first order; (**b**) The error graph based on (**a**); (**c**) The fitting function of second order; (**d**) The error graph based on (**c**); (**e**) The fitting function of third order; (**f**) The error graph based on (**e**); (**g**) The fitting function of fourth order; (**h**) The error graph based on (**g**).
## 1 Introduction
### Hdtn  
With the advancements in optical communication technology used in space communication such as with the Laser Communications Relay Demonstration mission, it is possible to achieve data rates up to 1.244 Gbps [22]. However, the existing DTN implementations, interplanetary overlay networks (IONs), are limited in processing such high rates due to the lack of parallel processing capabilities and shared memory. To overcome the rate asymmetries in space data communication, HDTN was developed by NASA. HDTN uses high-performance computing elements in space gateways, which are size, weight, and power constraint platforms (SWap), to provide DTN services to high-rate space communication links (optical, laser) by optimizing the network processing tasks [23]. Currently, HDTN is used as a DTN gateway for the International Space Station to store and forward scientific payloads. It is used for network flow management using a distributed and service-based approach. HDTN supports optical data rates and cognitive networking capabilities [24].
## 3 Results and Discussion
### Correlation Analysis between Microbial and Volatile Compounds  
The processing of dry-cured ham involves a series of biochemical and enzymatic reactions, including protein hydrolysis, lipid oxidation, and the Maillard reaction. These reactions are vital for the flavour formation of dry-cured ham, and the enzymatic activity of microorganisms also contributes to the production of flavour compounds [103]. To explore the relationship between off-odour in Xuanwei ham and microorganisms, a correlation analysis was conducted between key volatile compounds (OAV > 1) and microbial species. According to previous studies, microorganisms are the key factors affecting the flavour of different grades of ham in Jinhua [104]. The heatmap of the correlation between key volatile compounds and bacteria for both normal and spoiled ham is shown in Figure 6A. Trimethylamine, methanethiol, valeric acid, and hexanal were identified as the key volatile compounds that mainly contribute to the undesirable flavour in spoiled ham, particularly in the presence of _Bacillus cereus_, _Bacillus subtilis_, _Bacillus licheniformis_, _Pseudomonas sp._, and _Moraxella osloensis_ (_p_ < 0.05). _Pseudomonas sp._ is an aerobic bacterium commonly associated with spoiled meat [22] and has also been found to have higher levels in spoiled hams than in normal hams in dry-cured hams [21]. _Moraxella osloensis_ is a gram-negative aerobic bacterium known to be an opportunistic human pathogen responsible for diseases such as endocardiis, osteomyelitisitis, central venous catheter infections, and meningitis [105]. In summary, aerobic bacteria are more likely to cause spoiled ham in Xuanwei ham and also impact its flavour profile. Figure 6B illustrates the correlation thermograms between key volatile compounds of normal and spoiled ham with fungi. Spoiled ham was dominated by _Aspergillus ruler_, which correlated with most of the key volatile compounds, particularly exhibiting a strong positive correlation with trimethylamine. On the other hand, normal ham is dominated by _Wallemia ichthyophaga_, which has the highest positive correlation with 3-(methylthio)propionaldehyde and 3-methyl butanal. This suggested that _Wallemia ichthyophaga_ promoted the production of 3-(methylthio)propionaldehyde and 3-methyl butanal, which impart favourable volatile compounds to Xuanwei normal ham. In conclusion, microorganisms play a vital role in determining the flavour profile of Xuanwei ham.  
Figure 6: _Cont._
## 6 Construction Method and Engineering Implementation Significance
### Channel Structure Construction Method of Honeycomb Gas Flow Network
(**a**) Circular plastic zone (**b**) Oval plastic zone (**c**) Butterfly plastic zone. gas flow in each borehole surrounding rock during gas drainage. Additionally, reduce the overlap of plastic zone expansion and reduce the cost of gas drainage. For example, for the extraction of coal seam gas in a uniform or approximately uniform stress environment, at the initial stage of borehole unloading. A large amount of elastic energy stored in the surrounding rock mass is released. The plastic zone of the surrounding rock of each hole expands independently. After that, due to the strong coupling and pressure relief between the surrounding rocks of adjacent boreholes [20], the plastic zone of each hole expands to the adjacent plastic zone in a convex shape in the horizontal and vertical directions, forming an approximate honeycomb gas flow network channel structure, as shown in Figure 15a,b. For the borehole extraction of coal seam gas in a high deviatoric stress environment, the plastic zone expansion of borehole surrounding rock is mainly subjected to high deviatoric stress. The formation of the honeycomb gas flow network channel structure is through the expansion of butterfly leaves in the plastic zone of the surrounding rock of each borehole, as shown in Figure 15c. In the test of Puxi Mine, the expansion and permeability-increasing of the plastic zone of the surrounding rock can be effectively realized when the borehole spacing is 5 m. Too small or too large hole spacing will produce unsatisfactory results, increase costs or affect the gas circulation effect.
###### Abstract  
Lithium-sulfur (Li-S) batteries offer a high theoretical energy density but suffer from poor cycling stability and polysulfide shuttling, which limits their practical application. To address these challenges, we developed a PANI-modified MoS\({}_{2}\)-NG composite, where MoS\({}_{2}\) nanoflowers were uniformly grown on graphene oxide (GO) through PANI modification, resulting in an increased interlayer spacing of MoS\({}_{2}\). This expanded spacing exposed more active sites, enhancing polysulfide adsorption and catalytic conversion. The composite was used to prepare MoS\({}_{2}\)-NG/PP separators for Li-S batteries, which achieved a high specific capacity of 714 mAh g\({}^{-1}\) at a 3 C rate and maintained a low capacity decay rate of 0.085% per cycle after 500 cycles at 0.5 C. The larger MoS\({}_{2}\) interlayer spacing was key to improving redox reaction kinetics and suppressing the shuttle effect, making the MoS\({}_{2}\)-NG composite a promising material for enhancing the performance and stability of Li-S batteries.  
Keywords:lithium-sulfur batteries; electrocatalys; MoS\({}_{2}\) nanoflowers; interlayer spacing; catalytic conversion +
Footnote †: journal: _molecules_  
_Article_  
Nitrogen-Doped Graphene Uniformly Loaded with Large Interlayer Spacing MoS\({}_{2}\) Nanoflowers for Enhanced Lithium-Sulfur Battery Performance
## 3 Results
### Comparison Experiments
The quantitative results are shown in Table 2. Regarding precision metrics, the average improvement of our network is 36.3%, 40.3%, 13.8%, and 4.5% compared to FCN-8s, FCN-32s, SegNet, and U-Net networks, respectively. In terms of recall metrics, the average improvement of our network is 29.2%, 31.2%, 12.2%, and 5.6% compared to the four networks, respectively. Regarding the _IoU_ metric, the average improvement of our network is 44.5%, 48.9%, 22.0%, and 6.1% compared to the four networks, respectively. Clearly, in terms of _precision_, _recall_, _F1 score_, and _IoU_, the quantitative results of our network outperform the other four network models.
# Adaptive Wavelet Coding Applied in a Wireless Control System
## 3 System Description
### Implemented Control Loop for Performing Computational Simulations  
In this work, the computational simulations analyze the impact of the degenerative effects caused by the wireless communications in a control loop. In the block diagram of Figure 6, the sensor receives from the plant, by wired communication, a signal related to the level of the tank to be controlled (\(L_{2}\)), then performs the wavelet coding of that signal. The coded symbols are then transmitted to the controller via a channel with Rayleigh fading and with Doppler shift. Each sample of the signal to be controlled is taken in a period \(t_{s}\). The controller decodes these symbols and performs a PID control routine based on the value of the setpoint set on the computer. The control signal (\(v_{p}\)) obtained by the PID algorithm is coded, and the generated symbols are transmitted to the actuator by a channel with flat Rayleigh fading and with the Doppler effect. The actuator decodes the received symbols via wireless communication and injects that signal into the plant. It is important to note that the signal \(v_{p}\) is limited to an operating region between 0 V and 5 V.
# Hybrid Sensing Platform for IoT-Based Precision Agriculture
## 4 Results
### Data Validation
#### 4.1.1 Aerial Temperature and Humidity Data Validation Using Ground-Based Data--Experiment 1
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Aerial Temperature**} & \multirow{2}{*}{\
\begin{tabular}{c} **Correlation Coefficient with** \\ **Ground-Based Temperature** \\ \end{tabular} } & \multicolumn{1}{c}{
\begin{tabular}{c} **Linear Regression with** \\ **Ground-Based Temperature** \\ \end{tabular} } \\  & & **(Coefficient of Determination/R\({}^{2}\))** \\ \hline Minimum & 0.97 & 0.93 \\ Average & 0.94 & 0.88 \\ Maximum & 0.88 & 0.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Aerial temperature correlation coefficient and linear regression with ground-based temperature. Figure 6: Aerial minimum, average, and maximum humidity compared with ground-based humidity. Table 4 shows that the aerial maximum humidity has the highest correlation coefficient value with 0.98, which means that it has the strongest linear relationship with ground-based humidity. Therefore, aerial maximum humidity has higher validity to be selected as aerial humidity data. Similar to temperature, the linear regression analysis was also conducted on the humidity data. As Table 4 shows, the aerial maximum humidity has the highest R\({}^{2}\) value, which is 0.95. This value indicates a strong linear relationship and shows that aerial maximum humidity has higher validity to be selected as aerial humidity data compared with aerial average and minimum humidity data. The data validation performed on aerial minimum, average, and maximum temperature and humidity data shows that all of these data have relatively strong linear relationships with the ground-based data. However, aerial minimum temperature and aerial maximum humidity have stronger correlation coefficient and linear regression values compared with other data sets. Therefore, aerial minimum temperature and aerial maximum humidity are selected to be used in the data calibration process, which is discussed in Section 4.2.
## 4 Experiments
### Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Dataset** & _CR1_ (0.9-0.1) & _CR2_ (t = 1.1) & **k-NN** & **RF** & **SVM** & **MLP** & **NB** & **NC** \\ \hline Dermatology & 0.955 & **0.983** & 0.934 & 0.979 & 0.977 & 0.981 & 0.923 & 0.731 \\ \hline HeartDisease & 0.605 & **0.670** & 0.505 & 0.580 & 0.605 & 0.600 & 0.618 & 0.562 \\ \hline Thyroid & 0.909 & 0.763 & 0.551 & **0.919** & 0.501 & 0.505 & 0.475 & 0.598 \\ \hline Pageblocks & **0.883** & 0.857 & 0.750 & 0.779 & 0.802 & 0.849 & 0.768 & 0.593 \\ \hline BreastCancer & 0.969 & **0.973** & 0.971 & 0.967 & 0.960 & 0.960 & 0.963 & 0.956 \\ \hline Shuttle & 0.917 & 0.929 & 0.885 & 0.926 & 0.917 & **0.967** & 0.872 & 0.720 \\ \hline Newthyroid & 0.946 & 0.942 & 0.899 & 0.943 & **0.987** & 0.737 & 0.951 & 0.865 \\ \hline Geobia & **0.916** & 0.895 & 0.692 & 0.910 & 0.752 & 0.708 & 0.866 & 0.658 \\ \hline Glass & 0.748 & 0.719 & 0.789 & **0.855** & 0.743 & 0.604 & 0.686 & 0.718 \\ \hline Leaf & 0.868 & 0.862 & 0.777 & **0.876** & 0.745 & 0.753 & 0.843 & 0.765 \\ \hline Sonar & **0.840** & 0.751 & 0.815 & 0.825 & 0.759 & 0.806 & 0.698 & 0.667 \\ \hline SPECTF & 0.696 & 0.525 & 0.616 & 0.627 & 0.654 & 0.619 & 0.761 & **0.772** \\ \hline Average AUC & **0.854** & 0.823 & 0.766 & 0.849 & 0.784 & 0.7489 & 0.786 & 0.717 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The performance of all classifiers based on the AUC average using 5-cross validation. Based on the average rank presented in Table 3, our prototype-based approaches outperformed other prototype-based classifiers, including k-NN, NB, and NC. Notably, _CR1_ (percentile-based) achieved the highest average AUC among all classifiers.
## 5 Experimental Results
### Novel GVNS versus Nearest Neighbor  
In this work, we propose a novel GVNS version based on quantum computing principles and techniques, and we apply it to a garbage collection application, an actual GPS-based problem. Below we present the computational results of our approach. As already mentioned, NN and its variants are widely used in GPS/GIS applications in order to construct the tour of the underlying graph. In particular, we compare our method against the NN heuristic and two of its most well-known variants: the repeated NN and the improved NN. This section presents the comparative analysis among NN, GVNS, and the optimal value (OV), showing the results in a series of figures and tables. It is important to point out that we chose to examine the NN because of its widespread use in GPS applications.  
Table 1 contains the aggregated experimental results. Specifically, it contains the benchmark name, the NN cost, the average for first improvement (FI) for each problem, the gap between GVNS using FI vs. OV, the average for best improvement (BI) for each problem, the gap between GVNS using BI vs. OV,  
and the OV. Given the outcome \(x\), its gap from the optimal value \(OV\) is computed by the formula \(\frac{OV-x}{OV}\). The gap is widely used in the field of optimization to measure how close to the optimal a particular solution is. The data demonstrate that GVNS  
* is consistently very close to the optimal value both with FI and BI, and
* outperforms NN in all cases.  
The shaded lines in Table 1 emphasize the superiority of GVNS for certain instances of the benchmarks. Specifically, we highlight these instances where GVNS has a 2% or less gap from the optimal value. For example, the NN's gap from the optimal value for _att48_ is \(-0.2101\), whereas GVNS's BI search strategy achieves \(-0.0016\), and GVNS using FI achieves \(-0.0024\). Moreover, GVNS achieves the optimal value (0.0000 gap from OV) for _bayg29_, _bayg29_, _eil51_, _fri26_, _gr17_, _gr21_, and _gr24_. Another characteristic case is the benchmark _lin105_, where NN has a \(-0.4156\) gap from the optimal, while GVNS with BI strategy achieves \(-0.0163\), and GVNS using FI achieves \(-0.0159\).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Benchmark** & \multirow{2}{*}
# A Review of the Optimal Design of Neural Networks Based on FPGA
## 4 Neural Network Optimization Technology Based on FPGA
### Optimization of Data and Its Operations
#### 4.1.4 Pipelined Design
[102] proposed pipelined stochastic adaptive distributed architectures (P-SCADAs) by combining pipelined design and adaptive technology in the LSTM network, which improved FPGA performance and saved FPGA resource consumption and power consumption.
# Design of PIDD(r) Controller for Robust Performance of Process Plants
###### Abstract  
Managing industrial processes in real-time is challenging due to the nonlinearity and sensitivity of these processes. This unpredictability can cause delays in the regulation of these processes. The PID controller family is commonly used in these situations, but their performance is inadequate in systems and surroundings with varying set-points, longer dead times, external noises, and disturbances. Therefore, this research has developed a novel controller structure for PIDD(r) that incorporates the second derivative term from PIDD(r) while exclusively using fractional order parameters for the second derivative term. The controllers' robust performance has been evaluated on four simulation plants: first order, second order with time delay, third-order magnetic levitation systems, and fourth-order automatic voltage regulation systems. The controllers' performance has also been evaluated on experimental models of pressure and flow processes. The proposed controller exhibits the least overshoot among all the systems tested. The overshoot for the first-order systems is 9.63%, for the third-order magnetic levitation system, it is 12.82%, and for the fourth-order automatic voltage regulation system, it is only 0.19%. In the pressure process plant, the overshoot is only 4.83%. All controllers for the second-order systems have a time delay, while the flow process plant has no overshoot. The proposed controller demonstrates superior settling times in various systems. For first-order systems, the settling time is 14.26 s, while in the pressure process plant, the settling time is 8.9543 s. Similarly, the proposed controllers for the second-order system with a time delay and the flow process plant have the same settling time of 46.0495 s. In addition, the proposed controller results in the lowest rise time for three different systems. The rise time is only 0.0075 s for the third-order magnetic levitation system, while the fourth-order automatic voltage regulation system has a rise time of 0.0232 s. Finally, for the flow process plant, the proposed controller has the least rise time of 25.7819 s. Thus, in all the cases, the proposed controller results in a more robust controller structure that provides the desired performance of a regular PIDD(r) controller, offering better dynamic responses, shorter settling times, faster rise times, and reduced overshoot. Based on the analysis, it is evident that PIDD(r) outperforms both PID and FOPID control techniques due to its ability to produce a more robust control signal.  
PID; fractional order; robustness; process control; stability +
Footnote †: journal:
# Location-Based Lattice Mobility Model for Wireless Sensor Networks
## 3 Problem Statement and Lattice Mobility Model Formulation
\begin{split} C_{c}=\bigg{\{}\frac{\{n\times(\Delta D_{p}\times R _{e})+n\times(A_{e}\times C_{m})\}^{2}}{2M_{e}}+r^{2}\;(K_{n}-1)\bigg{\}}\\ +\Big{\{}\frac{n\times(\Delta C_{p}\times R_{e})+n\times(A_{e} \times C_{m})}{2M_{e}}+r^{2}\;(K_{n}-1)\Big{\}}\end{split}  \tag{29}\]  
By combing the Equations (26) and (29), we get the total energy consumed for updating the location and each complete cycle as given by:  
\[E=L_{ue}+C_{c} \tag{30}\]  
This model helps accurately determine the moving location, the distance from the original location of the sensor node to the current location. Additionally, it reduces control packets by applying the optimized data frame format model approach discussed in [23], energy consumption and end-to-end delay. The mobile sensor nodes in LMM sense the event from the target location and transfer the generated events to the base station. If any sensor node moves from its original position, another node fills the gap. The location of the moving node is determined to manage the data transmission rate. The lattice provides scalability to WSNs because the locations and distances of the moving sensor nodes are easily determined to control the mobility. The LMM is suitable for single-hop destinations, but its performance on multi-hop destinations has not been tested. Moreover, multiple hops on the path create nonlinearities in the WSN. Before a packet can be transmitted, a node should wait for the node of the next hop to wake up. The packet is held for a variable amount of time on every path. This feature creates uncertainty for several types of WSN applications, especially surveillance and military applications. The LMM is a memory less model because WSNs have sensor nodes with minimum memory and power. The lower energy consumption for mobility helps gather additional data due to a longer network life. We focus on other lower-memory mobility models, including the geographic based CMM, WMM, and RWMM. The performances of these mobility models are compared in the realistic scenario of an earthquake, which is simulated in the next section.