esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
In this study, ammonia nitrogen, nitrate nitrogen, pH and C/N were the main physicochemical indices affecting the diversity and distribution of the bacterial community. Ammonia nitrogen was an important source of available nitrogen; however, excessive ammonia nitrogen would degrade the soil and was harmful to bacterial growth. Previous study has found that ammonia nitrogen gradually decreased vertically with reaction attenuation in the process of vertical infiltration [34]. However, thesubsurface soil ammonia nitrogen was higher than that in the topsoil due to the migration of high concentrations of ammonia nitrogen in leachate [4]. In addition, AOB could oxidize ammonia to nitric acid and further reduce the concentration of ammonia nitrogen in the topsoil [17]. The high concentration of ammonia nitrogen indicated that the degree of waste degradation was low in subsurface soil, which reduced the diversity and changed the distribution of the bacterial community. The nitrate nitrogen also had a significant influence on the bacterial community structure in this landfill. Denitrifiers can reduce nitrate under anaerobic conditions and reduce the concentration of nitrate in subsurface soil [15]. Compared with the high concentration of ammonia nitrogen, nitrate nitrogen had less effect on bacterial community structure in this study. Moreover, it has been proved that the optimal growth pH range of bacteria is narrow [31,32]. Therefore, top and middle soil layers with pH ranging from 7.06-7.89 may support the coexistence of diverse bacteria. The C/N ratio is also reported to be a key factor affecting the diversity and structure of the bacterial community [15,17]. The structure of the bacterial community was also influenced by C/N in this study. These influencing factors and relationships with bacterial communities provide insights for the management of closed landfills around urban areas.
## 3 Evaluation Method
### Global Metrics
**Average gradient, \(\hat{G}\)**: Since \(P^{-1}\) is a function of two variables (the \(x\)- and \(y\)-coordinates of a pixel), one can measure its gradient magnitude \(G\) at every pixel \(\mathbf{p}\) through the central differences [25] as follows:  
\[G_{x}(\mathbf{p})=\frac{P^{-1}(\mathbf{p}+(1,0))-P^{-1}(\mathbf{p}-(1,0))}{2},\]  
\[G_{y}(\mathbf{p})=\frac{P^{-1}(\mathbf{p}+(0,1))-P^{-1}(\mathbf{p}-(0,1))}{2},\]  
\[G(\mathbf{p})=\sqrt{\|G_{x}(\mathbf{p})\|^{2}+\|G_{y}(\mathbf{p})\|^{2}}. \tag{5}\]  
Large gradient values indicate pixels where the decision map has a high likelihood of being incorrect, since neighboring pixels correspond to faraway data points; thus, data points can be classified differently. Separately, discontinuous changes in this map indicate areas where \(P^{-1}\) is not smooth, where we expect to see errors in the decision map. We can reduce \(G\) to a scalar value by computing the average \(\hat{G}\) of the normalized values \(G(\mathbf{p})/G_{max}\) over all the decision-map pixels, where \(G_{max}\) is the maximal value of \(G\) over the set of decision maps being compared. The value \(1-\hat{G}\) ranges in \([0,1]\), thus signaling how smooth a decision map is. Values close to \(0\) indicate a smooth map (with low average gradients). Values close to \(1\) indicate a map with many discontinuities, which are thus more prone to errors, as explained above.
# Compact Multilayer Yagi-Uda Based Antenna for IoT/5G Sensors
## 5 Conclusions  
In this paper, two compact printed antennas, based on Yagi structures, were presented as a solution to integrate IoT sensors. A multilayer antenna, which is an adaptation of the conventional planar Yagi structure was designed, improving the most important properties compared with the planar antenna and with the state-of-the-art. The multilayer antenna designed provides more than 25% bandwidth, covering the entire 5G allocated band in Europe, allowing its usage for IoT applications. Its improved gain to almost 11 dBi in a cube structure of nearly 1 cm\({}^{3}\) of volume is a good achievement to combat the propagation issues of use mmWave frequencies, a gain considerably higher than the conventional microstrip patch antennas.  
Moreover, the multilayer configuration permits to change the direction of radiation to the vertical plane, allowing it to be placed over the IoT sensors circuits. These results were not obtained at the expense of a reduction of antenna efficiency, because the multilayer structure shows an efficiency of 86%. Finally, the multilayer antenna has modular physical features that allow it to be easily grouped into high gain antenna arrays, or also arrays for 5G applications using beamforming techniques. Thus, the multilayer antenna presents better radiation performance, essentially, better gain and a wider bandwidth while being simultaneously smaller and more compact.  
In conclusion, the possibility of using both Yagi antennas in 5G scenarios, physical sensors, or as a singular element of a larger array has been confirmed. Moreover, a multilayer implementation seems to be a promising chance for achieving better performances at higher frequencies.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Antenna** & **Bandwidth** & **Gain** \\ \hline \multirow{2}{*}{Planar} & 4.42 GHz & \multirow{2}{*}{8.9 dBi} \\  & 18\% & \\ \multirow{2}{*}{Multilayer} & 6.9 GHz & \multirow{2}{*}{10.9 dBi} \\  & 27\% & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Measured bandwidth and simulated gain of both antennas.  
**Author Contributions:** All the authors have contributed to this paper. [PERSON] has designed the proposed antennas, performed the simulations and the measurements, and wrote the paper. [PERSON] and [PERSON] have supervised the entire research, the approach used, the results analysis, and discussion. Additionally, both have strongly contributed to the writing of the paper.  
**Acknowledgments:** This work is supported by the European Regional Development Fund (FEDER), through the Competitiveness and Internationalization Operational Programme (COMPETE 2020) of the Portugal 2020 framework, Project, RETIOT, POCI-01-0145-FEDER-016432.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Parameter Optimization Procedure  
This section describes the tuning procedure used to offline optimize the coefficients of the state and control-weighting matrices, expressed in Equation (17), associated with the baseline LQI controller as well as the learning parameters (\(\beta\), \(\gamma\), \(\delta\), \(\rho\), and \(\mu\)) linked with the RLCA scheme. The optimization is carried out by minimizing the cost function in Equation (34).  
\[I_{e}=\int_{0}^{\mathrm{T}}\Big{[}(e_{\theta}(t))^{2}+(u(t))^{2}\Big{]}dt \tag{34}\]  
where T is the total duration of a simulation trial. The said cost function captures the variations in the system's state error \(e_{\theta}(t)\) as well as the applied control input \(u(t)\). The function assigns equal weights to both variables to optimize state regulation while economizing the control energy expenditure. The coefficients of the state and control-weighting matrices of the LQI controller are chosen within the range of 0 to 100. The parameters associated with the RLCA are chosen within the range of 0 and 10. Section 5 outlines the procedure for conducting simulation trials to tune the parameters. All tuning parameters are initially set to unity. Figure 8 illustrates the parameter tuning process [33].  
The tuning begins with initial parameter settings and proceeds iteratively [37]. In each trial, the UAV model is tasked to track a step reference position along the longitudinal axis for T = 5.0 s, during which the cost \(J_{e,k}\) is evaluated; where \(k\) is the trial number. The algorithm searches for optimal parameters by following the descending gradient of the cost function. If a new trial results in a lower cost \(J_{e,k}\) than the cost of the previous trial \(J_{e,k-1}\), the local minimum \(J_{e,min}\) is updated. The process stops when either the maximum number of trials (\(k_{max}\)) is reached or the cost falls below a predefined threshold. This threshold is determined empirically through pilot runs, ensuring a balance between computational efficiency and solution accuracy while preventing premature termination. To determine the said threshold, the cost for the initial parameter settings is recorded as \(J_{e,min}^{0}\approx 2.47\times 10^{4}\), and its scaled-down value (heuristically set to 0.05) is used as the algorithm termination criterion. A larger scaling factor increases computational demands, while a smaller one risks premature termination. The algorithm is thus concluded when \(J_{e,min}\) approaches 0.05 \(J_{e,min}^{0}\).  
Accordingly, the threshold value for \(J_{e,min}\) as well as \(k_{max}\) are set at \(1.2\times 10^{3}\) and 40, respectively. Thus, the state and control costs optimized are \(\mathbf{Q}=\text{diag}\Big{(}1.05\quad 31.58\quad 3.22\quad 2.11\Big{)}\) and \(R=1.02\), respectively. Correspondingly, the LQI state feedback gain vector is computed as \(\mathbf{K}=\Big{[}0.66\quad 5.34\quad 3.41\quad 1.85\Big{]}\). Similarly, the optimized values of the parameters linked with the RLCA scheme are \(\beta=4.092\), \(\gamma=3.87\), \(\delta=5.63\), \(\rho=1.48\), and \(\mu=2.19\).
## 2 Literature Review and Preliminaries
### Proportional Hesitant Fuzzy Set  
In 2018, [PERSON] et al. [22] proposed the concept of PHFS.  
**Definition 5**.: _[_22_]_ _Let \(X\) be a fixed set, and the proportional hesitant fuzzy set (PHFS) on \(X\) is described as follows:_  
\[E=\left\{(x,ph_{E}(x)\mid x\in X)\right\}=\left\{(x,(h_{E}(x),p_{E}(x))\mid x \in X)\right\}, \tag{4}\]  
_where_  
1. \(h_{E}(x)=\left\{\gamma_{1},\gamma_{2},0...,\gamma_{n}\right\}\) _is a set of_ \(n\) _values in_ \([0,1]\)_, presenting possible membership values of the element_ \(x\in X\) _to set_ \(E\)_, and_
2. \(p_{E}(x)=\left\{\tau_{1},\tau_{2},0...,\tau_{n}\right\}\) _is a set of_ \(n\) _values in_ \([0,1]\)_, where_ \(\tau_{i}\left(i=1,2,0...,n\right)\) _denotes the proportion of membership values_ \(\gamma_{i}\left(i=1,2,0...,n\right)\) _such that_ \(\sum_{i}^{n}\tau_{i}=1\)_._  
For convenience and simplicity, we call \(ph=ph_{E}(x)\) a proportional hesitant fuzzy element (PHFE).  
**Definition 6**.: _Let ph and \(ph_{1}\) be two PHFEs on the fixed set \(X\), then:_  
1. \(ph\bigcup ph_{1}=\bigcup_{(\gamma,\tau)\in ph_{1}(\gamma_{1},\tau_{1})\in ph_{ 1}}\left\{(\gamma\vee\gamma_{1},\tau\tau_{1})\right\}\)_;_
2. \(ph\bigcap ph_{1}=\bigcup_{(\gamma,\tau)\in ph_{1}(\gamma_{1},\tau_{1})\in ph_{ 1}}\left\{(\gamma\wedge\gamma_{1},\tau\tau_{1})\right\}\)_._  
**Definition 7**.: _Let ph be a PHFE. Then, its score and deviation values are defined as follows:_  
\[s(ph)=\sum_{(\gamma,\tau)\in ph}\gamma\tau; \tag{5}\]  
\[t(ph)=\sum_{(\gamma,\tau)\in ph}\tau\left(\gamma-s(ph)\right)^{2}. \tag{6}\]
# Modeling and Experimental Study of the Dual Cylinder Fluid Inerter
## 3 Modeling of Mechanical Properties of a Dual-Cylinder Fluid Inerter
### Simulation of Mechanical Properties  
The force generated by the dual-cylinder fluid inerter is the resultant force of inertia force and damping: i.e., \(F\) is:  
\[F=B\ddot{x}+F_{c} \tag{16}\]  
MATLAB was used to write programs to establish the inertial force \(F_{b}\), damping force \(F_{c}\) and resultant force \(F\) by combining Equations (7), (15), and (16). The structural parameters are shown in Table 1, and the results are shown in Table 2.  
As can be seen from Figures 3 and 4, the mechanical properties of a dual-cylinder fluid inerter are equivalent to nonlinear damping and linear inerter in parallel.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Structure Size** \\ \hline
**Diameter of main chamber cylinder 2 \(D_{2}\) (mm)** & **80** \\
**Diameter of main chamber cylinder 6 \(D_{6}\) (mm)** & **80** \\
**Diameter of piston rod 18 \(D_{18}\) (mm)** & **50** \\
**Diameter of piston rod 17 \(D_{17}\) (mm)** & **50** \\
**Diameter of pipeline 4 \(D_{4}\) (mm)** & **12** \\
**Diameter of pipeline 11 \(D_{11}\) (mm)** & **12** \\
**Length of pipeline 4 \(L_{2}\) (mm)** & **5000** \\
**Length of pipeline 11 \(L_{11}\) (mm)** & **5000** \\ \hline \multicolumn{3}{c}{**Parameters of the Fluid Medium**} \\ \hline
**Fluid type** & **water** \\
**Density \(\rho\) (kg/m\({}^{3}\))** & **1000** \\
**Dynamic viscosity \(\mu\) (cS\(\Phi\))** & **1** \\ \hline \multicolumn{3}{c}{**Input Signal Parameters**} \\ \hline
**Input type** & **sinusoidal velocity signal** \\
**Amplitude (mm)** & **60** \\
**Frequency \(\omega\) (Hz)** & **1** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Table of result.  
Figure 3: The curve of damping force and velocity.  
Figure 2: The curve of damping and inertia force of a dual-cylinder fluid inerter.  
In Figure 5, the solid line is the resultant force calculated by Formula (16), and the dotted line is the result calculated by Simulink/Hydraulic Segmented Pipe LP. The Segmented Pipe LP module could consider the inertia force and damping force of the fluid simultaneously. \"The Segmented Pipe LP\" module in Simulink was based on the finite-element idea. Except in the initial stage of loading, there was shock in the Segmented Pipe LP module, and after stabilizing, the results of the above two methods were highly consistent, which proved the correctness of the established dual-cylinder fluid inerter mechanical model before the test comparison.  
The oscillations in the calculation results of Simulink are a normal physical phenomenon. At the moment of loading, the internal pressure of the fluid inerter transiently fluctuates, and gradually converges to a steady state after a period of oscillation. The mechanical model established by Formula (16) is based on the empirical formula of fluid mechanics and is a description of a steady state, so there is no oscillation in Figure 5.
## 2 Proposed Circuits
### Voltage-Mode Multifunction Biquadratic Filter
As indicated by Equations (2)-(4), if V\({}_{\rm i2}\) = 0 (grounded) and V\({}_{\rm i1}\) = V\({}_{\rm in}\) is given by the input voltage signal then the LP, BP, and BR filtering functions are given by:  
\[\frac{\rm V_{o1}}{\rm V_{in}}=\frac{\rm V_{LP}}{\rm V_{in}}=\frac{1}{\rm s^{2}C _{1}C_{2}R_{2}R_{3}+sC_{1}R_{1}+1} \tag{5}\]  
\[\frac{\rm V_{o2}}{\rm V_{in}}=\frac{\rm V_{BP}}{\rm V_{in}}=\frac{\rm sC_{1}R_{ 3}}{\rm s^{2}C_{1}C_{2}R_{2}R_{3}+sC_{1}R_{1}+1} \tag{6}\]  
\[\frac{\rm V_{o3}}{\rm V_{in}}=\frac{\rm V_{BR}}{\rm V_{in}}=\frac{\rm s^{2}C _{1}C_{2}R_{2}R_{3}+1}{\rm s^{2}C_{1}C_{2}R_{2}R_{3}+sC_{1}R_{1}+1} \tag{7}\]  
By letting V\({}_{\rm i1}\) = 0 (grounded) and proving V\({}_{\rm i2}\) = V\({}_{\rm in}\), the IBP and HP filtering functions can be easily obtained from the two voltage outputs \(V_{o1}\) and \(V_{o3}\) as follows:  
\[\frac{\rm V_{o1}}{\rm V_{in}}=\frac{\rm V_{IBP}}{\rm V_{in}}=\frac{-sC_{2}R_{ 2}}{s^{2}C_{1}C_{2}R_{2}R_{3}+sC_{1}R_{1}+1} \tag{8}\]  
\[\frac{\rm V_{o3}}{\rm V_{in}}=\frac{\rm V_{HP}}{\rm V_{in}}=\frac{\rm s^{2}C _{1}C_{2}R_{1}R_{2}}{s^{2}C_{1}C_{2}R_{2}R_{3}+sC_{1}R_{1}+1} \tag{9}\]  
As illustrated in Equations (5)-(9), the proposed filter simultaneously provides LP, BP, and BR voltage responses while the IBP and HP responses can also be obtained. The pass-band gains, G\({}_{\rm BP}\), G\({}_{\rm IBP}\), and G\({}_{\rm HP}\), for the BP, IBP, and HP responses are given by:  
\[\rm G_{BP}=\frac{R_{3}}{R_{1}},\ G_{\rm IBP}=-\frac{C_{2}R_{2}}{C_{1}R_{1}},\ G_{\rm HP }=\frac{R_{1}}{R_{3}} \tag{10}\]  
Figure 3: The proposed high-input impedance CFOA-based multifunction biquadratic filter. Figure 2: Implementation of CFOA given by a CCII+ and a VB. Table 2 summarizes the two inputs and three outputs CFOA-based voltage-mode multifunction biquadratic filter of the transfer functions. Comparing the expression for the denominator to the characteristic equation \(\mathrm{D(s)=s^{2}+\frac{\omega_{0}}{Q}s+\omega_{0}^{2}}\), the parameters \(\omega_{0}\) and Q of the proposed filter are given by:  
Figure 1:0  
Figure 1:1  
Equations (11) and (12) show that the parameter Q can be tuned by adjusting the resistor R\({}_{1}\) without affecting \(\omega_{0}\). In other words, Q and \(\omega_{0}\) are orthogonally adjustable. The orthogonal controllability is a desirable property for the designing and tuning flexibility of the biquadratic filters.
## 2 Places, Instruments, and Data
### Data  
To develop the FDS GMF and test its performance, we introduced three datasets: buoy measurements, satellite scatterometer observations, and European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis data.
## 4 Experiments and Analysis of Results  
### Mdf-Gan Based Fault Diagnosis Steps  
To validate the effectiveness of the rolling bearing fault diagnosis method based on MDFT-GAN proposed in this paper, extensive experiments and data analysis were conducted on two case datasets: the CWRU dataset [31, 32] and the Jiangnan University dataset [33]. The experimental process was divided into three stages. First, MDFT-GAN was trained using preprocessed data to generate high-quality synthetic samples. Second, the quality of the generated samples was evaluated using sample quality assessment metrics. Third, the generated samples were combined with the original samples to expand the training dataset for fault diagnosis model training. The effectiveness and superiority of the MDFT-GAN-based method were demonstrated by comparing its performance with several state-of-the-art fault diagnosis methods.  
The method was implemented using the PyTorch deep learning framework in Python 3.11. The development environment was Pycharm 2023, and the experimental hardware configuration included an Intel Xeon Platinum 8362 CPU and an NVIDIA GeForce RTX 3090 GPU. The hyper-parameter settings for the modeling process are detailed in Table 1. The network structure of the proposed methodology is shown in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline
**Hyperparameter** & **Augmentation Model** & **Classification Model** \\ \hline Learning Rate & 0.0001 & 0.00001 \\ Optimizer & Adam & AdamW \\ Training Epochs & 6000 & 100 \\ Dropout & - & 0.5 \\ Batch size & 128 & 64 \\ \hline \end{tabular}
\end{table}
Table 1: Hyperparameter settings.  
\begin{table}
\begin{tabular}{c c} \hline
**Model** & **Layer** & **Parameters** \\ \hline \multirow{8}{*}{Generator} & Input Data & \(3\times 64\times 64\), emb = 100 \\  & MDFT Block-1 & d = 1, emb = 512, n\_heads = 8, in\_c = 100 \\  & & out\_c = 512, k = 4 \(\times\) 4, s = 1, p = 0 \\  & MDFT Block-2 & d = 1, emb = 256, n\_heads = 8, in\_c = 512, \\  & & out\_c = 256, k = 4 \(\times\) 4, s = 2, p = 1 \\  & MDFT Block-3 & d = 1, emb = 128, n\_heads = 8, in\_c = 256, \\  & & out\_c = 128, k = 4 \(\times\) 4, s = 2, p = 1 \\  & MDFT Block-4 & d = 1, emb = 64, n\_heads = 8, in\_c = 128, \\  & & out\_c = 64, k = 4 \(\times\) 4, s = 2, p = 1 \\  & Output Data & \(3\times 64\times 64\) \\ \hline \end{tabular}
\end{table}
Table 2: The proposed network structure.
## 4 Future Data
### Passive Microwave  
The future provision of satellite passive microwave imagery of the Earth's surface is not entirely clear. The instruments that have provided much data since the year 2000 have been the AMSR-E on the Aqua satellite and the SSMIS onboard DMSP. AMSR-E failed in 2011, but SSMIS continues to provide data. A DMSP platform with SSMIS successfully was launched in April 2014, and another is planned for launch in 2020.
# Research on Scene Classification Method of High-Resolution Remote Sensing Images Based on RFPNet
## 5 Discussion
### Analysis in Relation to the Training Iterations  
The concept of iteration is the process of training once with a batch of samples. In order to analyze the influence of different training iterations on test results, the corresponding experiments were carried out. As shown in Figure 19, the accuracy of both the UC Merced dataset and SIRI dataset has not changed significantly after about 25,000 iterations, and the accuracy then increases slowly, steadily, and slightly. In the paper, with the adoption of the residual block, there is no gradient explosion or gradient disappearance in the testing process, which indicates the advantages of the proposed method again.  
Figure 19: Schematic diagram of the classification accuracy corresponding to the training iteration.  
Figure 18: Schematic diagram of the classification accuracy corresponding to the feature length of the fully connected layer.
## 3 Results and Discussion
### LID Scenario Analysis and Benefit Evaluation
#### 3.4.1 Simulation Effects of the LID Plans Under Different Structural Parameters
Correspondingly, the change of slope increases the collision between the runoff and the impervious side wall on the planting soil surface, and  
Figure 4: Real-time traffic flow of the concave green space and its hardened surface in different schemes. (**a**) Default parameter scheme; (**b**) A 10% reduction in planting soil thickness program; (**c**) A 10% slope reduction program for planted soil; (**d**) A 10% increase in planted soil program; (**e**) A 10% slope increase program for planted soil. the flow velocity decreases, which decreases with the discharge speed of the drainage pipe in the overflow well, thus reducing the total overflow flow in the whole process. After comprehensive consideration, the control effect of the total runoff flow of the green space with downward depression, which is still the default parameter, is the most significant, which is consistent with the above TOPSIS analysis results. However, according to the above TOPSIS results, the optimal solution ordering, with flood peak reduction rate, flood peak time delay rate, and total overflow reduction rate as the calculation objectives, shows that the benefit ranking of the concave green space is lower when the disturbance rate is 0. The reason for this is that, in order to ensure sufficient data, the data contained in the planting soil porosity and drainage pipe porosity scheme with low parameter sensitivity are substituted into the analysis when TOPSIS analysis is used, which has a certain impact on the benefit ranking of green spaces under the default parameters. At the same time, the default concave green space structure parameters are empirical values or normative values selected in combination with the relevant literature and norms of Tianjin, which are highly scientific and rational, so the runoff control effect is better when applied to the simulation of actual precipitation conditions. To sum up, the optimal structural parameters of the concave green space were obtained as an aquifer height of 200 mm, a planting soil thickness of 600 mm, a planting soil slope of 1.5%, a planting soil porosity of 45%, and a pipeline porosity of 30%.