esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Evaluation of Graph Neural Network-Based RouteNet Model with Attention Mechanism
## 3 Methodology
### Detailed Internal Architecture of RouteNet with Attention
The attention mechanism calculates an attention weight \(e_{p,l}^{t+1}\) that represents the importance of the interaction between path \(p\) and link \(l\) at time step \(t+1\). The attention weights \(e_{p,l}^{t+1}\) are then normalized across all paths or links using the softmax function, resulting in the final attention weights \(a_{p,l}^{t+1}\). These attention weights indicate the relative importance of neighboring paths or links in the aggregation process. Next, the algorithm modifies the message-passing step to include the attention weights in the aggregation process. Each path-to-link message \(\tilde{m}p,l^{t+1}\) is multiplied by its corresponding attention weight \(ap,l^{t+1}\) to obtain a weighted message \(\hat{m}_{p,l}^{t+1}\). This means that more important messages will contribute more to the aggregation step. Afterward, the hidden states of links are updated by aggregating the weighted messages from paths that include them. The weighted messages for each link \(l\) are summed together to obtain an updated hidden state \(h_{l}^{t+1}\) at time step \(t\)+\(1\). The loop continues with the updated hidden states of paths and links, and this process repeats until the final time step \(T\)-\(1\) is reached. ```
Input :\(x_{p}\), \(x_{l}\), \(R\) Output :\(h_{p}^{T}\), \(h_{l}^{T}\), \(\hat{y}_{p}\)
1 Initialize attention weights: \(a_{p}^{0}\) for each path \(p\)
2 Initialize attention weights: \(a_{l}^{0}\) for each link \(l\)
3 foreach\(p\in R\)do
4\(h_{p}^{0}\leftarrow[x_{p},0,\dots,0]\);
5
6 end for
7 foreach\(l\in N\)do
8\(h_{l}^{0}\leftarrow[x_{l},0,\dots,0]\);
9
10 end for
11 for\(t=0\) to \(T-1\)do
12 foreach\(p\in R\)do
13 foreach\(l\in p\)do
14\(h_{p}^{t}\leftarrow\operatorname{GRU}_{l}(h_{p}^{t},h_{l}^{t})\);
15\(\hat{m}_{p,l}^{t+1}\gets h_{p}^{t}\);
16 Compute attention weight: \(e_{p,l}^{t+1}\leftarrow\operatorname{AttentionMechanism}([h_{p}^{t},h_{l}^{t}],a_{p }^{t},a_{l}^{t})\);
17
18 end for
19
20 end for
21\(h_{p}^{t+1}\gets h_{p}^{t}\);
22 foreach\(l\in N\)do
23 Compute attention weights: \(a_{p,l}^{t+1}\leftarrow\operatorname{Softmax}([e_{p,l}^{t+1}\mid\forall p:k\in p ])\);
24\(h_{l}^{t+1}\gets U_{l}\left\{h_{l}^{t},\sum_{p\in p}\left(a_{p,l}^{t+1}\cdot \hat{m}_{p,l}^{t+1}\right)\right\}\);
25
26 end for
27
28 end for
29\(\hat{y}_{p}\gets F_{p}(h_{p})\);
```  
**Algorithm 1**Internal Architecture of RouteNet Model with Attention  
Finally, after completing the message-passing loop, the algorithm uses the readout function \(F_{p}\) to predict path-level features \(\hat{y}_{p}\) based on the final hidden states of paths \(h_{p}^{T}\). The readout function \(F_{p}\) is an ordinary fully connected neural network with appropriate activation functions.
## 4 Performance Analysis of Closed-Form Estimator
### Impact of SNR on Estimator Performance Analysis
Figure 4: Impact analysis of estimator performance based on different SNR conditions; (**a**) Error comparison of theoretical and estimated PDF curves at \(k=3.5,\ \mu=1.5\), SNR = 30 dB; (**b**) Error comparison of theoretical and estimated PDF curves at \(k=3.5,\ \mu=1.5\), SNR = 25 dB; (**c**) Error comparison of theoretical and estimated PDF curves at \(k=3.5,\ \mu=1.5\), SNR = 20 dB; (**d**) Error comparison of theoretical and estimated PDF curves at \(k=3.5,\ \mu=1.5\), SNR = 15 dB. From the RMSE\({}_{pdf}\) column in Table 6, it can be seen that as the SNR decreases, the estimation errors of the estimators gradually increase. When the SNR drops to 15 dB, the estimation errors of all estimators are essentially unacceptable. Therefore, we can draw the conclusion that our proposed estimators have limitations in their performance when the SNR is low. When using the estimators proposed in this paper as well as [PERSON]'s estimator, it is necessary to first filter out the noise or increase the signal gain. Furthermore, as the SNR decreases, the RMSE values of all estimators gradually increase. At the 15 dB level, \(\hat{k}\)-\(\hat{\mu}_{Yacoub}\) estimator cannot be computed, and with its higher estimation error probability, it becomes unsuitable for practical applications. Moreover, although the RMSE of the \(\hat{k}\)-\(\hat{\mu}_{\rm B}\) estimator is higher than that of the \(\hat{k}\)-\(\hat{\mu}_{\rm A}\)/\(\hat{k}\)-\(\hat{\mu}_{\rm A}\) estimator under higher SNR conditions, as the SNR decreases, the performance of the \(\hat{k}\)-\(\hat{\mu}_{\rm B}\) estimator declines more slowly than that of the \(\hat{k}\)-\(\hat{\mu}_{\rm A}\)/\(\hat{k}\)-\(\hat{\mu}_{\rm A}\) estimator. Therefore, at 15 dB, the RMSE performance of the \(\hat{k}\)-\(\hat{\mu}_{\rm B}\) estimator is better than that of the \(\hat{k}\)-\(\hat{\mu}_{\rm A}\)/\(\hat{k}\)-\(\hat{\mu}_{\rm A}\) estimator, which intuitively reflects the correct trend of estimation accuracy changes for different channel parameter estimators under different SNR conditions. In the methods proposed in this paper, the \(\hat{k}\)-\(\hat{\mu}_{\rm A}\)/\(\hat{k}\)-\(\hat{\mu}_{\rm A}\) estimator is used for communication scenarios with better channel conditions, while the \(\hat{k}\)-\(\hat{\mu}_{\rm B}\) estimator is suitable for non-line-of-sight (NLOS) scenarios with poorer channel conditions and lower received SNR. This is consistent with the conclusions reached.
# Advances and Future Perspective on Detection Technology of Human Norovirus
## 2 Detection Techniques
### Biosensor  
In recent years, biosensors have shown great potential for application in pathogen detection because of their timeliness, stability, low cost and the possibility of integrating and miniaturising them into point-of-care testing devices [81; 82; 83; 84; 85]. A biosensor is usually composed of a biometric recognition element, transducer and signal processing unit. The interaction between the analyte and biometric recognition element is converted into a quantifiable signal output by the transducer [86]. The biometric recognition element is the key to determining the specificity of the biosensor. The selection of the biometric recognition element depends on the characteristics of the analyte. It should have high affinity and stability for the analyte. It includes enzymes (such as HRP), antibodies, ligands, nucleic acids and phages (for pathogenic bacteria detection), molecularly imprinted polymers, affinity and cells. Amongst them, antibodies are most commonly used for virus detection [87]. The current biosensors are primarily divided into electrochemical sensors, optical sensors and piezoelectric sensors according to the signal transduction mode [88]. Biosensors have become a new focus in the field of HuNoV detection (Table 1). Considerable research focused on the development of fast, sensitive, portable and easy-to-operate biosensors for the detection of HuNoVs in food, environmental or clinical samples.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
## 1 Introduction  
Volatile organic carbon (VOC) compounds in the atmosphere play a role in climate change, human and animal health, and oxidation capacity [1]. The results obtained from a 2020 study measuring the concentrations of various VOCs in Burnaby South and Port Moody (part of Greater Vancouver) and analyzing the factors contributing to their production showed that fuel combustion, natural gas, industrial solvents, and fugitive industrial and biogenic emissions, are the main sources that contribute to all VOC emissions [2].  
Methyl formate is a small ester derived from formic acid or formate. It can undergo a series of reactions in the troposphere initiated by the hydroxy radical and other atmospheric radicals [3]. In oxygenated fuels such as dimethyl ether and dimethoxy methane--two suggested alternatives to conventional diesel fuel [4; 5; 6]--methyl formate represents the major intermediate formed in their oxidation. It has also been shown to interact with atmospheric water [7]. The mechanisms by which organic molecules are incorporated into water clusters include switching, association, and proton transfer [4]. When formates interact with water molecules, the carbonyl group leads to further hydrogen bonding interactions. The shape and bonding strength of the cluster is altered, making it more stable overall. These findings make it clear that organic esters such as formates play a role in nucleation processes due to their ability to share intermolecular interactions with water [8; 9]. Ion-molecule reactions are one method for exploring the reaction between formates and water clusters [10].  
Water clusters play a ubiquitous role in atmospheric chemistry in both neutral and protonated states [11]. Proton-bound water clusters are widely used as reactant ions for the analysis of volatile organic compounds through mass spectrometry [12]. It is also quite common to produce protonated analyte molecules using proton-bound water clusters [13; 14]. Herein, we present details of ion-molecule reactions between proton-bound solvent clusters of water, methanol and ethanol with neutral methyl formate in the gas phase. The aim is to explore this aspect of the atmospheric fate of formates. Reaction products have been further explored with collision-energy resolved tandem mass spectrometry to understand their unimolecular chemistry.
# Resource Allocation to Massive Internet of Things in LoRaWANs
## 3 Link-Level Model
### Link Measurement Model  
The link measurement model is primarily based on [5]. It considers the impact of the propagation on the signal strength, considering small-scale fading and its influences. The received power (\(P_{rx}\)) at the GW can be computed by:  
\[P_{rx}=\frac{P_{tx}\times G_{ED}\times G_{GW}}{PL}e^{\xi}, \tag{4}\]  
where \(G_{ED}\) and \(G_{GW}\), respectively, represent the ED and GW antenna gains; \(P_{tx}\) is the transmit power, and \(PL\) is the path loss. Equation (4) can be re-written in the logarithmic domain, with \(10\xi\log_{10}e=4.34\xi\), we get:  
\[P_{rx}^{dB}=P_{tx}^{dB}+G_{ED}^{dB}+G_{GW}^{dB}-PL^{dB}+4.34\xi, \tag{5}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbols** & **Description** \\ \hline \(SR\) & packet success ratio \\ \(N_{r}\) & number of packets successfully received at the GW \\ \(N_{s}\) & number of total sent MAC packets \\ \(\hat{\mu}D\) & average end-to-end delay \\ \(T_{r}\) & the time a packet has completely arrived and successfully received at the GW \\ \(T_{s}\) & the time a packet leaves the ED queue for transmission \\ \(\hat{\mu}D_{ack}\) & average ACK delay \\ \(T_{ack}\) & ACK reception time measured at the ED \\ \(T_{tx}\) & the time a confirmed UL packet was transmitted from the ED for the first time \\ \(P_{rx}\) & the received power at the GW \\ \(P_{tx}\) & transmit power \\ \(G_{ED}\) & ED antenna gain \\ \(G_{GW}\) & GW antenna gain \\ \(e^{\xi}\) & log-normal shadowing (or log-distance path loss) component \\ \(P_{DL}\) & path loss \\ \(P_{rx(i,j)}\) & the measured received power at \(ED_{j}\), when a packet is transmitted by \(ED_{i}\) \\ \(d_{ij}\) & distance between two EDs (\(i\) and \(j\)) \\ \(BW\) & bandwidth \\ \(NF\) & noise margin at GW \\ \(\mathbb{C}\) & number of default channels in the EU region \\ \(\mathbb{Y}\mathbb{C}\) & SINR anticipated by the GW \\ \(P_{\geq},\mathbf{r}\) & power of a packet under observation \\ \(\mathbb{P}\) & cumulative power \\ \(\beta_{(i,j)}\) & threshold corresponding to an acceptable SINR \\ \hline \hline \end{tabular}
\end{table}
Table 1: Symbols utilized in the link-level model along with their description.  
where \(PL^{dB}\) is obtained by combining both the propagation and building penetration losses. The \(e^{\xi}\) in Equation (4) is the log-normal shadowing (or log-distance path loss) component, i.e., \(4.34\xi\sim\mathbb{N}(\mu,\,\sigma^{2})\), where \(\mu=0\) is the mean captured in the \(PL\), and \(\sigma\) represents the depth of shadowing, which is the logarithmic standard deviation \((4<\sigma<12)\). During the simulation experiment, we consider the value of \(\sigma=6\ dB\), as presented in [2]. It is well known that the RSSI varies because of the objects obstructing the propagation path between an ED and a GW. In this paper, we consider both log-distance (\(LOG\)) and Okumura-Hata (\(OH\)) models, as presented in [21; 22]. Additionally, we add building penetration loss caused by the signal attenuation due to the external wall, internal wall, and floors of the building [23; 24].
# Topological Interference Management for K-User Downlink Massive MIMO Relay Network Channel
## 2 Channel Model and Formula Definitions  
The Gaussian \(K\)-user MIMO downlink relay network channel model is depicted in Figure 1. \(K\)-user BS share information with the associated users through a shared relay.  
We consider \(N_{tx,k}\) transmitters and \(N_{R}\) relay with \(M\) antennas transmits independent messages to the \(N\) antenna \(N_{rx,k}\) receivers, where \(M\geq k\) and \(k\in\{1,2,3,...,K\}\). Consider that the communication over the relay functions in a half-duplex amplify and forward mode and sends an amplified version of  
Figure 1: The \(K\)-user multiple-input-multiple-output (MIMO) interferrence channel model with a shared relay.  
the transmitted signal to the receiver with a processing delay. \(\mathbf{X}^{[k]}[n]\in\mathbb{C}^{\mathbf{N_{k}}\times 1}\) and \(\mathbf{X}^{[R]}[n]\in\mathbb{C}^{\mathbf{N_{k}}\times 1}\) are the signal vectors from the transmitter and relay, respectively, where \(\mathbf{h}^{K}[n]^{T}=[h_{1}^{k}[n],...,h_{N_{k,k}}^{k}[n]]\) and \(\mathbf{h}^{K}[n]^{T}=[h_{1}^{K}[n],...,h_{N_{k}}^{K}[n]]\) are the channel vectors from the transmitter and relay, respectively. We assume that the elements of the channel matrix, \(\mathbf{H}^{[K,K]}(n)\), are drawn from an independent and identically distributed (i.i.d) complex Gaussian with zero mean and unit variance. The input-output relationships can be given as follows:  
\[\mathbf{y}_{j}^{k}(n)=\underbrace{\mathbf{h}_{j,j}^{[k,k]^{T}}(n )\mathbf{x}_{j}^{k}(n)+\mathbf{h}_{j,j}^{[k,R]^{T}}(n)\mathbf{x}_{j}^{R}(n)}_{ \text{Desired signal}} + \underbrace{\sum_{j=1,j\
eq k}^{K}\mathbf{h}_{j,i}^{[k,j]^{T}}(n )\mathbf{x}_{i}^{j}(n)}_{\text{Interference}}+Z_{j}^{[k]}(n), \tag{1}\] \[\mathbf{Y}_{j}^{R}(n) = \underbrace{\mathbf{h}_{j}^{[R,k]}(n)\mathbf{x}_{j}^{k}(n)}_{ \text{Relay signal}}+Z_{j}^{[R]}(n), \tag{2}\]  
where \(\mathbf{y}_{j}^{k}(n)\) and \(\mathbf{Y}_{j}^{R}(n)\) are the \(k\)th receiver and relay signals, respectively, from the channel use \(n\). \(\mathbf{h}_{j,j}^{[k,k]^{T}}(n)\), \(\mathbf{h}_{j,i}^{[k,R]^{T}}(n)\), and \(\mathbf{h}_{j,i}^{[k,j]^{T}}(n)\) represent the transmitter, relay, and interference channel matrices, respectively. \(\mathbf{x}_{j}^{k}(n)\) and \(\mathbf{x}_{j}^{R}(n)\) are the transmitter and relay signals; \(Z_{j}^{[k]}(n)\) and \(Z_{j}^{[R]}\) are the zero-mean additive white Gaussian noise signals for the \(k\)th receiver and relay, respectively.
## 1 Introduction
### Directly Coupled PV-Membrane System  
The concept of directly coupled PV-membrane systems--where no energy storage components are included--is to capitalize on the ability of the system to easily, efficiently and cost-effectively store clean drinking water that was created during hours of sunshine, instead of storing electricity. Several reports have demonstrated the successful operation of directly coupled renewable energy-powered membrane (RE-membrane) systems, the challenge of which is naturally dealing with the intermittency and fluctuations inherent to the wind and solar energy resource [20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30]. In the group of Murdoch University, [PERSON] et al. designed a PV-membrane system for brackish water desalination based on a piston pump with 120-W PV power that was capable of producing 400 L/d of potable water with a recovery of either 16% or 25% [24; 25; 27]. The group of [PERSON] et al. designed a directly coupled RO desalination system that could be powered by wind or PV energy [26; 28; 29]. [PERSON] and [PERSON] reported the PV-membrane system for seawater desalination, which produced \(\sim\)1.5 m\({}^{3}\)/day of permeate at a SEC of 4 kWh/m\({}^{3}\)[26]. [PERSON] et al. designed a battery-less PV-membrane desalination plant, which enabled a clean water production of 300 L on a sunny summer day with an overall SEC in the range of 2.5-4 kWh/m\({}^{3}\)[31]. In a final example, [PERSON] and [PERSON] [32] investigated the long-term performance of a RO desalination plant operating under intermittent conditions for 14 years (around nine h/d) when treating brackish groundwater with conductivity in the range of 7-9.6 mS/cm. The results indicated a specific energy consumption (SEC) in the range of 1.8-2.2 kWh/m\({}^{3}\). Such directly coupled systems can potentially exhibit higher efficiency when no batteries or additional electrical devices are incorporated, leading to a higher output power due to less power loss. However, because such systems are subject to fluctuations and intermittency from the RE source, this can result in a lower permeate quality and productivity [25; 26; 33]. A further economic issue is the underutilization of the equipment--due to operating only during the day--that ultimately affects the cost of water.
## 4 Experimental Results
### Analysis Results for False Detection Cases  
After examining the cases of false detection in the personal information category, we found errors due to missing annotations, inaccuracies in the annotation scope, or false detection based on similar forms. Missing annotations are cases where data are omitted owing to user error during data collection. Scope errors in annotations mostly occur when vocative particles such as '-ah' and '-ya' or suffixes such as '-nim' and '-ssi' are included in names, nicknames, job titles, or positions. Although annotation scope errors are useful for capturing entities with forms similar to personal information, they often misidentify these entities as different named entities. These cases constituted most false detections; their specific details are presented below.
## 3 Results and Discussion
### Statistical Characteristics
The data in the table clearly reflect the changes in the velocity profiles of each wall surface shown in Figure 4, where a positive \(F\) value indicates an upward shift in the velocity profile and a reduction in drag, and vice versa. The \(30^{\circ}\) divergent riblet wall achieved a drag reduction rate of 21.70% in the clear water flow field, a figure very close to previous research results [28; 38]. In the two-phase flow field, the drag reduction rate for this wall surface reached 26.18%, indicating that the addition of particles has a certain degree of enhancement on the drag reduction effect. Figure 5 presents the distribution curves of dimensionless comprehensive turbulence intensity and Reynolds shear stress along the wall-normal height in the turbulent boundary layer under different conditions. It can be observed that the streamwise riblet and \(30^{\circ}\) divergent riblet walls both reduce the comprehensive turbulence intensity within the turbulent boundary layer. The intensity of the streamwise riblet is very close to that of the smooth wall, primarily reducing the turbulence intensity in the logarithmic law region but enhancing it in the buffer layer region. In contrast, the \(30^{\circ}\) divergent/convergent riblet walls reduce/enhance the overall turbulence intensity of the boundary layer. According to the research by [PERSON] et al. [46], this is because the upwelling motion of the fluid in the boundary layer of the convergent riblet wall brings the fluid with larger near-wall turbulence fluctuations away from the wall, resulting in a higher turbulence intensity within the boundary layer at the same height for the convergent riblet wall. Conversely, the downward motion on the divergent riblet surface plays exactly the opposite role. The streamwise riblet surface lacks the presence of secondary flows, leading to a weaker suppression of turbulence fluctuations, resulting in slightly different changes in turbulence intensity in the buffer layer and logarithmic law region. In Figure 5b, the distribution curve of Reynolds shear stress is similar to that of the comprehensive turbulence intensity. Among the configurations, the \(30^{\circ}\) convergent riblet surface exhibits significantly higher Reynolds shear stress compared to both the smooth wall and the \(30^{\circ}\) divergent riblet surface. All of these changes are consistent with the characteristics of drag-reduction surfaces. From both figures, it can be observed that the addition of particles leads to a decrease in the comprehensive turbulence intensity and Reynolds shear stress in the logarithmic law region, while the intensity in the buffer layer and wake region is enhanced. This is because, for the buffer layer closer to the wall, the transpiration motion caused by particle-wall collisions and the vortices generated by the particles themselves lead to an enhancement of Reynolds shear stress and turbulence intensity [47]. The increase in boundary layer thickness leads
# Precipitation Estimation Using FY-4B/AGRI Satellite Data Based on Random Forest
## 4 Discussion
In the night model, woodland has a better performance. Its better performance may be attributed to the characteristics of woodland. Its lower reflectivity and higher absorption rate provide heat for the generation of showers. A large amount of water vapor from forest transpiration is quickly transported to the sky, which promotes precipitation, especially convective precipitation and topographic precipitation. Overall, the proposed model is better at identifying precipitation. Its POD, CSI and ETS scores are all higher than those of the operational product, while the FAR score of the model is higher than that of the operational product. This may be due to the fact that the model subsamples the non-precipitation samples and removes a large number of them, which leads to an overestimation of retrieval precipitation pixels. To demonstrate the value of the proposed model in an application, we also used the ground rain gauge data as our reference to conduct a comparative experiment. When using the ground rain gauge data as our reference, its accuracy of precipitation retrieval is lower than that of the GPM IMERG product, but the overall trend is consistent. On the one hand, this is because the random forest model uses the GPM IMERG product as the target data, and the retrieval model is designed and tuned to replicate the precipitation intensity as provided by the GPM IMERG product. The retrieval results are closer to those of the GPM IMERG product, so the error between the GPM IMERG product and ground rain gauge data is also substituted into the retrieval results. On the other hand, there are still temporal and spatial differences in terms of the spatio-temporal matching between the retrieval data and ground rain gauge data.