esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Feature Selection (FS)
#### 3.2.4 Singular Value Decomposition (SVD)  
Singular Value Decomposition (SVD) is a well-known approach in the information retrieval study area developed in [58] to lower the dimensionality in applications such as text classification, where documents are presented with vectors.  
From a linear algebra-based theorem, the SVD technique can decompose a matrix \(A\) of size \(m\times n\) into the product of three matrices: an \(m\times m\) orthogonal matrix \(u\), an \(m\times n\) diagonal matrix \(S\), and the transpose of an orthogonal matrix \(V\) of size \(n\times n\). SVD formula is usually presented as the following:  
\[A_{n\times m}=U_{m\times m}\;\times\;S_{m\times n}\;\times\;V_{n\times n}^{T} \tag{8}\]  
Dimensionality reduction is applied to the data to remove the noise done by deleting rows from the bottom of matrices \(U\) and \(S\). The left columns from matrices \(S\) and \(V^{T}\) are also removed.
# Performance of Cooperative Relay NOMA with Large Antenna Transmitters
## 4 Numerical Results and Analyses  
In this section, we validate the proposed 3D channel model for an mMIMO CR-NOMA system through simulation using the MatLab numeric computing platform for evaluation purposes. We compared the results to a system using CBSM channel model such as Rayleigh fading channel [17; 19]. The major application of CBSM is for assessing theoretical performance because it is imprecise at simulating channel characteristics for real large MIMO systems.  
We evaluate the proposed 3D GBSM models by setting the AoA cluster \((\phi,\theta)\) to 0.7, and AoA offset standard deviations, \((\sigma_{\Delta\phi},\sigma_{\Delta\theta})\) to \(-0.3\) at 2.6 GHz carrier frequency [113]. The proposed system model uses 5G network operating bandwidth of 200 MHz [114]. We consider the entire physical space of URA antenna components at the TX as \(l=4\lambda\) on both the \(y\) and \(z\) axes.  
Similarly, for the CA, adjacent antenna element are spaced by \(l=4\lambda\) on the \(z\) axis and are located at a maximum radius of \(\rho=2\pi/l\) from the cylinder center to \(x\), \(y\) plane. The CA has a \(2\times N_{t}\) number of components, and in the azimuth domain, there are two circular arrays with \(N_{t}=4\) elements each. We studied a \(N_{t}\times N_{t}\) array element in the case of the URA, where \(N_{t}\) is the number of antenna elements in a row or column.  
For each TX antenna topology, we calculated the channel coefficient as the distance between the \(s\)th transmit antenna port and the \(u\)th receive antenna port. In order to validate the proposed model, we considered \(\theta_{tilt}=95^{\circ}\), \(\theta_{3 dB}=15^{\circ}\) and \(\phi_{3 dB}=70^{\circ}\) at the TX for the 3D channel modeling between the TX, RS and UEs in Figure 3. Table 2 gives the summary of simulation parameters.  
Additionally, the power of azimuth spectrum (PAS) arriving at the RS, multipath components for each AoA, and the multipath delay associated with AoD are all represented as Laplacian distributions with specified 3 GPP specifications. For the cooperative communication at the relay, we considered DF and AF with the proposed 3D GBSM between the TX, RS and UEs. The OP, achievable rate and BER are determined for each relaying scheme and compared with the results of a system using CBSM, as illustrated in Figure 2.
## 4 Defining the Optimization Objectives
### The Proposed Objective Functions  
The first objective function is reducing the output power of the synchronous generators of the network (increasing the reserve of the reactive power generated by the generators) that generate reactive power.  
\[Q_{G}=\sum_{i=1}^{NG}Q_{G_{i}}\,\ \forall Q_{G_{i}}>0 \tag{15}\]  
\[minf_{1}=Q_{G}\times\left(1+\alpha P_{QG}+\beta P_{V}+\lambda P_{tap}+\delta P _{Cap}\right) \tag{16}\]  
The second objective function is increasing the output power of the reactive power generation resources or the line charging capacitors. To achieve these conditions, the voltage of the buses should be increased, which improves the voltage profile.  
\[maxf_{2}=Q_{charge}\times\left(1+\alpha P_{QG}+\beta P_{V}+\lambda P_{tap}+ \delta P_{Cap}\right) \tag{17}\]  
The third objective function is minimizing the costs of supply and the purchase of the total reactive power  
\[minf_{3}=Cost_{QG}\times\left(1+\alpha P_{QG}+\beta P_{V}+\lambda P_{tap}+ \delta P_{Cap}\right) \tag{18}\]  
The fourth objective function is minimizing the supply and purchase costs of reactive power considering the reactive power generation costs of the compensators installed in the network. The total costs of this function should be lower than the cost calculated in the previous section so that installing new reactive power compensators is cost-effective. Thus, this objective function can be represented as follows:  
\[P_{loss}=\sum_{i=1}^{NG}P_{G_{i}}\,\ \sum_{j=1}^{Nbus}PD_{j} \tag{19}\]  
\[minf_{4}=P_{loss}\times\left(1+\alpha P_{QG}+\beta P_{V}+\lambda P_{tap}+ \delta P_{Cap}\right) \tag{20}\]  
In which \(P_{G_{i}}\) is the total power generation of the network generators, and \(PD_{j}\) is the real load connected to each bus.
## 1 Delineation of Rain Areas with TRMM Microwave Observations Based on PNN  
**[PERSON] 1,\({}^{\dagger}\), [PERSON] 1,\({}^{\star}\), [PERSON] 2,\({}^{\dagger}\) and [PERSON] 3,\({}^{\dagger}\)**  
1. The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China; E-Mail: [EMAIL_ADDRESS]
2. Department of Geography and Program in Planning, University of Toronto, Toronto, ON M5S 3G3, Canada; E-Mail: [EMAIL_ADDRESS]
3. National Meteorological Information Center (NMIC), China Meteorological Administration (CMA), Beijing100081, China; E-Mail: [EMAIL_ADDRESS]  
\({}^{\dagger}\) These authors contributed equally to this work.  
\({}^{\star}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-8725-2343.  
External Editors: [PERSON] and [PERSON]  
_Received: 19 August 2014; in revised form: 18 November 2014 / Accepted: 19 November 2014 / Published: 4 December 2014_  
**Abstract:** False alarm and misdetected precipitation are prominent drawbacks of high-resolution satellite precipitation datasets, and they usually lead to serious uncertainty in hydrological and meteorological applications. In order to provide accurate rain area delineation for retrieving high-resolution precipitation datasets using satellite microwave observations, a probabilistic neural network (PNN)-based rain area delineation method was developed with rain gauge observations over the Yangtze River Basin and three parameters, including polarization corrected temperature at 85 GHz, difference of brightness temperature at vertically polarized 37 and 19 GHz channels (termed as TB37v and TB19v, respectively) and the sum of TB37v and TB19v derived from the observations of the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). The PNN method was validated with independent samples, and the performance of this method was compared with dynamic cluster K-means method, TRMM Microwave Imager (TMI) Level 2 Hydrometeor Profile Product and the threshold method used in the Scatter Index (SI), a widely used microwave-based precipitation retrieval algorithm. Independent validation indicated that the PNN method can provide more reasonable rain areas than the other three methods.  
Furthermore, the precipitation volumes estimated by the SI algorithm were significantly improved by substituting the PNN method for the threshold method in the traditional SI algorithm. This study suggests that PNN is a promising way to obtain reasonable rain areas with satellite observations, and the development of an accurate rain area delineation method deserves more attention for improving the accuracy of satellite precipitation datasets.  
**Keywords:** probabilistic neural network; rain area delineation; Scatter Index; dynamic cluster K-means; support vector machine
###### Abstract  
Gravity-driven membrane (GDM) filtration is one of the promising technologies for decentralized water treatment systems due to its low cost, simple operation, and convenient maintenance. The objective of this study was to evaluate the permeability of submerged GDM filtration with three different membranes, i.e., polyethersulfone and polyvinylidene divfluoride ultrafiltration (PES-UF and PVDF-UF) and polytetrafluoroethylene microfiltration membrane (PTFE-MF). The GDM system was operated using lake water for about one year. The determined average permeability values were high for PVDF-UF (192.9 L/m\({}^{2}\)/h/bar (LMH/bar)) and PTFE-MF (80.6 LMH/bar) and relatively lower for PES-UF (46.1 LMH/bar). The observed higher permeability for PVDF-UF and PTFE-MF was thought to be related to the rougher surface of these two membranes compared to PES-UF. The fouling layers of PVDF-UF and PTFE-MF were characterized by high biomass and the presence of a number of nematodes, while PES-UF showed a thin fouling layer with no nematode. The relatively high and fluctuated permeability of PVDF-UF and PTFE-MF could thus be attributed to the high biological activity of nematodes making the fouling layer more loose and porous. This was supported by a good linear relationship among the permeability, biomass concentration, and the number of nematodes in the fouling layers. These results provide important insights into membrane selection as a critical factor affecting the flux performance of the GDM filtration system for a decentralized drinking water supply.  
gravity-driven membrane (GDM); permeability; surface roughness; nematode; biomass concentration; intermittent operation mode +
Footnote †: journal: Applied Sciences
## 6 Simulation and Analysis of Protocol Based on BFGO-C in HWSN
### Comprehensive Evaluation Based on Entropy Weight Method  
Information entropy can measure the discreteness of indicators, and the larger the discreteness, the more significant the impact of the index on a comprehensive evaluation. The entropy weight method determines the index's weight in the comprehensive evaluation according to the variability of the information entropy reaction of the index.  
From the simulation results and analysis of the protocol based on BFGO-C in Section 5, the entropy weight method is used to comprehensively evaluate the four protocols in combination with four metrics: the lifetime of the network, the death time of the first node, remaining energy, and the volume of transmission. The radar chart of the comprehensive analysis is shown in Figure 18.  
As can be seen from Figure 18, the comprehensive performance of the protocol based on BFGO-C covers the most extensive range, and it has outstanding performance in the two indicators of network life and volume of transmission, although it is slightly worse in the indicator of the remaining energy of the first node death. Compared with the HCR protocol, the energy of the protocol based on BFGO-C lasts longer in the network. It can be seen from the comprehensive evaluation that the protocol based on BFGO-C can effectively extend the network lifetime by saving energy.
## 3 Results and Discussions
### Flow Regime Mapping for Horizontal Flow
#### 3.1.4 Air/non-Newtonian Two-Phase Flow
To understand this result further and demonstrate its wider applicability, the present study was extended to a non-dimensional number, i.e., the Froude number, presented in Figure 20 as the relationship between slug frequencies and Froude number for an air/xanthan gum solution. In Figure 20, it is shown that the slope of slug frequency vs. slug Froude number for air/xanthan gum solution can be modeled using the equation \(v_{s}=0.0082\left( \begin{array}{c}N_{f}\end{array} \right)^{1.5624}\), where the model strength R\({}^{2}\) is 80.62%. In order to further validate the experimental data for an air/non-Newtonian two-phase flow system, Figures 21 and 22 represent the relationship between the experimental and predicted data of the [PERSON] (2010) proposed model with standard error bars and 95% confidence level, respectively. Figure 19: Slug frequency vs. mixture velocity for air/non-Newtonian fluid flow. Figure 21: Experimental vs. predicted data [41] with standard error bars. Figure 22: Experimental slug frequency for the air–xanthan gum system compared to predictions by the [PERSON] & Scott [41] correlation, where R\({}^{2}\) = 74.6%. The error bar in Figure 21 shows the 95% confidence interval of the data; none of the confidence intervals includes zero, which means the data are statistically significant and repeatable for air/water two-phase flow. [PERSON] et al. [47] modified the slug frequency equation of [PERSON] & Scott [41] for a shear-thinning non-Newtonian fluid. Figure 21 represents the comparison of experimental results with the modified [PERSON] [41] slug frequency equation with error bars. The R\({}^{2}\) value of 75.3% also represents the reliability and repeatability of the experimental data of this study. Figure 22 also shows the 95% confidence interval of the experimental data.
# A Data Secured Communication System Design Procedure with a Chaotic Carrier and Synergetic Observer
## 3 Methodology and the System Design Procedure
### Alternative Reconstruction Method Demonstration
Figure 18: Useful signal reconstruction with a two channel LS method observer: (**a**) Useful signal \(a\) (blue is the initial signal at the transmitter side; and red is the reconstructed signal at the receiver side); (**b**) Useful signal \(c\) (blue is the initial signal at the transmitter side; and red is the reconstructed signal at the receiver side).
## 2 Materials and Methods
### SE Channel Attention Module  
The network incorporates the squeeze-and-excitation (SE) module [36] to dynamically adjust the importance of each channel. As shown in Figure 1e, the module first compresses each channel using global average pooling to capture global context. Then, fully connected layers are used to model inter-channel dependencies, generating attention weights to recalibrate the input features. The mathematical expressions for this operation are  
\[Z\mathrm{c}=\frac{1}{H\cdot W}\sum_{i=1}^{H}\sum_{j=1}^{W}F\mathrm{c}(i,j) \tag{1}\]  
\[s_{c}=\sigma(W_{2}\cdot\delta(W_{1}\cdot Z\mathrm{c})) \tag{2}\]  
Here, \(Z\mathrm{c}\) denotes the compressed feature for channel \(c\), \(F\mathrm{c}(i,j)\) is the value at position (i,j) in the input feature map for channel \(c\), \(s_{c}\) represents the attention weight for channel \(c\), \(W_{1}\) and \(W_{2}\) are the fully connected layer weights, \(\delta\) is the ReLU activation function, and \(\sigma\) is the Sigmoid function, which produces values in the range of [0, 1] to modulate channel importance.  
By enabling the adaptive calibration of features, this module helps emphasize crucial information and strengthens the network's capacity to capture relevant details.
# An Adjusted Frequency-Domain Algorithm for Arc Array Bistatic SAR Data with One-Moving Transmitter
## 3 Imaging Processing of AA-BiSAR with One-Moving Transmitter
### Residual Phase Compensation  
In this section, the residual phase compensation will be deduced to solve part of the range cell migration. Due to the special configuration of AA-BiSAR with one-moving transmitter, the residual phase introduced by the moving of the transmitter will cause severe range cell migration. Therefore, it is necessary to introduce a phase compensation function to correct the residual phase in the range frequency-azimuth time domain. The range frequency-azimuth time domain expression of the echo signal is given by:  
\[\begin{array}{ll}S_{s}(f_{r},t_{a})=&A_{0}\omega_{a}\Big{(}\frac{\theta_{r}- \theta_{a}}{\theta_{a}}\Big{)}W_{r}(f_{r})\\ &\cdot\exp\Big{[}-j2\pi\frac{(f_{r}+f_{c})}{c}\cdot R_{\rm tr}(t_{a})\Big{]} \cdot\exp\Big{(}-j\pi\frac{f_{r}^{2}}{K_{r}}\Big{)}\end{array} \tag{11}\]  
where \({\rm W}_{r}(f_{r})\) is the envelope of the signal in the range frequency-domain, and \(f_{r}\) is the range frequency variable. Substituting Equation (8) into Equation (11):  
\[\begin{array}{ll}S_{s}(f_{r},t_{a})=&A_{0}\omega_{a}\Big{(}\frac{\theta_{r}- \theta_{a}}{\theta_{a}}\Big{)}{\rm W}_{r}(f_{r})\\ &\cdot\exp\Big{\{}-j2\pi\frac{(f_{r}+f_{c})}{c}\cdot[R(\theta_{r})+R(t_{a})] \Big{\}}\cdot\exp\Big{(}-j\pi\frac{f_{r}^{2}}{K_{r}}\Big{)}\end{array} \tag{12}\]  
In order to compensate for the residual phase caused by the moving platform, the compensation function is constructed as follows:  
\[H_{c}(f_{r},t_{a})=\exp\biggl{[}j2\pi\frac{(f_{r}+f_{c})}{c}\cdot R(t_{a}) \biggr{]} \tag{13}\]  
Multiplying Equations (12) and (13), obtained the result as follows:  
\[\begin{array}{ll}S_{s1}(f_{r},t_{a})=&A_{0}\omega_{a}\Big{(}\frac{\theta_{r} -\theta_{a}}{\theta_{a}}\Big{)}{\rm W}_{r}(f_{r})\\ &\cdot\exp\biggl{[}-j2\pi\frac{(f_{r}+f_{c})}{c}\cdot R(\theta_{r})\biggr{]} \cdot\exp\Bigl{(}-j\pi\frac{f_{r}^{2}}{K_{r}}\Bigr{)}\end{array} \tag{14}\]  
According to Equation (14), it can be seen that after the residual phase is compensated, the raw signal of the target is no longer related to the velocity of the transmitter. Afterward, the range pulse compression is performed, and the matched filter function is constructed by the matched filtering method as follows:  
\[H_{r}(f_{r})=\exp\biggl{(}j\pi\frac{f_{r}^{2}}{K_{r}}\biggr{)} \tag{15}\]  
After the range pulse compression, the echo signal is compressed as:  
\[\begin{array}{ll}S_{sf\_c}(f_{r},t_{a})&=S_{s1}(f_{r},t_{a})\cdot H_{r}(f_ {r})\\ &=A_{0}\omega_{a}\Big{(}\frac{\theta_{r}-\theta_{a}}{\theta_{a}}\Big{)}{\rm W }_{r}(f_{r})\cdot\exp\Bigl{[}-j2\pi\frac{(f_{r}+f_{c})}{c}\cdot R(\theta_{r}) \Bigr{]}\end{array} \tag{16}\]
# No-Reference Image Quality Assessment with Convolutional Neural Networks and Decision Fusion
## 3 Experimental Results and Analysis
### Parameter Study  
First, we examine the performance of the individual models which consist of a pre-trained CNN body, global average pooling layer and a regression head. Second, we compare the effects of two different fusion of quality subscores, i.e., average and median pooling. The evaluation protocol is exactly the same as described in Section 2.1. The results obtained on KonIQ-10k [4] are summarized in Table 3. From these results, it can be concluded that using an optional pretrained CNN body results in a rather strong correlation between the predicted and ground-truth quality scores. On the other hand, the performance of VGG16 [56] and ResNet50 [57] lags behind those of the other pretrained networks. Further, regarding the decision fusion of different network types, both average and median pooling are able to increase the prediction performance substantially. Since median pooling outperforms average pooling, as one can see in Table 3, this was used in our NR-IQA method for a comparison to other state-of-the-art algorithms.
# Performance Analysis of Quantum Key Distribution Technology for Power Business
## 4 Structure of Power QKD Network
### QKD-Based Distribution Automation  
Distribution automation is an important technology that serves the construction of urban distribution networks. The safety of distribution instructions is critical. Compared with dispatching automation, the distribution network is multi-faceted, multiple communication methods coexist, and are greatly affected by environmental and social factors. It is prone to the risk of theft or tampering of unidirectional authentication and message transmission due to insufficient border protection [32]. Considering that the information exchange of new-type urban distribution networks is more frequent and more open, the use of QKD technology to encrypt the transmission data of distribution automation production is proposed. This study uses quantum keys to perform encryption on important authentication information and uses quantum relay technology to ensure long-distancetransmission of quantum networks. Finally, tamper-proof and anti-eavesdropping of information distribution is realized, thereby improving the anti-risk capability of the distribution network and ensuring the quality of electricity for urban residents. Figure 7 shows the network structure of quantum key encryption for distribution of automation data.
# Automatic Measurement of Inclination Angle of Utility Poles Using 2D Image and 3D Point Cloud
## 2 The Proposed Method
### Utility Pole Point Cloud Segmentation
#### 2.4.2 PointNet Point Cloud Segmentation  
The instance segmentation of the pole images has been classified into categories; therefore, point cloud segmentation only needs to distinguish the foreground and background. Although Pointnet [33] is a lightweight network model, it has achieved good results in both point cloud classification and point cloud segmentation. Considering the segmentation speed and accuracy requirements, PointNet is selected as the point cloud segmentation model. Figure 12 shows the PointNet point cloud segmentation model. The preprocessed frustum is input. Firstly, the MLP network is used to gradually upgrade the point cloud from \(6\left(x,y,z,r,g,b\right)\) dimensions to a high-dimensional space of 1024 dimensions, in which two T-Net networks are introduced to solve the point cloud rotation invariance problem. Then, the Max Pooling is taken as a symmetric function to extract the global features of the point cloud. Finally, the local features of each point are fused with the global features, and then the point cloud dimension is gradually reduced to two dimensions through MLP again.  
During data acquisition, the poles probably exist on the left and right sides of the acquisition device, so there are the pole point cloud data in the positive and negative intervals of the camera coordinate system. Compared to the rectified linear unit (ReLU) function, the leaky ReLU (LReLU) function has a relatively small slope \(\alpha\) on the negative axis, as shown in Equations (14) and (15). To avoid the disappearance of the gradient, LReLU replaces the ReLU activation function as the intermediate layer activation function. The activation function of the output layer uses the Sigmoid function and the binary cross entropy loss function is applied to calculate the loss, which is calculated as Equation (16).  
\[ReLU(x)=\left\{\begin{array}{ll}x,&x>0\\ 0,&x\leq 0\end{array}\right. \tag{14}\]  
\[LReLU(x)=\left\{\begin{array}{ll}x,&x>0\\ \alpha x,&x\leq 0\end{array}\right. \tag{15}\]  
\[Loss=-\frac{1}{n}\left[\sum_{i=1}^{n}y_{i}\log y_{i}^{\prime}+(1-y_{i})\log \bigl{(}1-y_{i}^{\prime}\bigr{)}\right] \tag{16}\]  
where, \(i\) represents the \(i\)th sample, \(n\) represents the number of samples, \(y_{i}\) represents the true label value, \(y_{i}^{\prime}\) represents the predicted value, and \(Loss\) represents the loss value.