esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Second, the external spatio-temporal network (ESTNet) is stored in each ECS for training and uploading gradients to the ESTNet that is deployed on the central cloud. For each ESTNet, we design a gating fusion layer to fuse urban \(PM_{2.5}\) features and external factors according to the diverse effects of various external factors. This is achieved by categorizing the external factors according to their types, then using one-hot encoding and fully connected networks (FCs) to embed them into the same space for fusion, and finally leveraging non-linear functions for weighted integration with urban \(PM_{2.5}\) features. In this way, our designed model can discern variations in the impacts generated by different external factors, which is of great significance for identifying mutations in urban \(PM_{2.5}\). The main contributions of this work are as follows:  
* Since multiple urban monitoring sites produce massive amounts of \(PM_{2.5}\) data, high computational pressure and data leakage levels occur when leveraging only a central cloud for all data and computing task. In light of this, we present an edge-based federated learning architecture. Specifically, we deploy a separate ECS in each urban region. The data acquired by the sensors in each urban region are uploaded to the corresponding ECS instead of the central cloud for training, which effectively mitigates the computational pressures on the central cloud and prevents security risks. Subsequently, the gradients trained on the ECS are uploaded to the central cloud for parameter updates, and then the model deployed on the central cloud predicts the \(PM_{2.5}\) in all urban regions, which creates a \"distributed training, centralized prediction\" mode. * We design a novel ESTNet model, in which a gating fusion layer is proposed to adaptively fuse external factors and urban \(PM_{2.5}\) features based on the different impacts of external features. * The experimental results on 13 prefecture-level cities in Jiangsu Province confirmed that FedDeep outperformed other state-of-the-art baselines. Specifically, FedDeep achieved the highest accuracy in predicting \(PM_{2.5}\) for the subsequent 12 h. Meanwhile, FedDeep had a short training time and low GPU usage, thus showing its high efficiency. The remainder of this paper is organized as follows: Section 3 introduces our studied region, the data used, and the architecture of our proposed model. The experiment description and result analysis are given in Section 4. Finally, Section 5 concludes this work.
## 2 Materials and Methods
### Boundary Conditions  
The preliminary analysis consisted of obtaining solar irradiation and temperature, considering the factors that directly affect the output of the PV system. Monthly weather data for the Olympic Stadium were obtained from the NREL National Solar Radiation Database (NSRDB) [32]. This is a dataset with high temporal and spatial resolution consisting of the three most widely used measurements of solar radiation (global horizontal, direct normal, and diffuse horizontal irradiance) as well as other meteorological data. The annual average horizontal solar irradiation in Rome has been found to be on the order of 4.10 kWh/m\({}^{2}\)/day. The daily average solar irradiance is shown in Figure 3.  
Figure 3: Daily average solar irradiance in Rome according to [32].  
Figure 2: Aerial view of the Olympic Stadium.  
The electrical load demand has been considered highly irregular because it depends on the activities that take place in the stadium. A modern medium-sized stadium with a capacity of 55,000 spectators typically has an annual energy consumption of 10,000 MWh [33]. However, a significant portion of this electricity is used during a limited number of hours on match days, with energy consumption estimates ranging from 15,000 to 30,000 kWh, depending on factors such as the stadium's size, season, temperature, and number of attendees [34]. Approximately 40% of the total energy consumed during a sports event is attributed to floodlighting, scoreboards, and advertising LED boards. Additionally, over 20% is dedicated to heating food and refrigerating beverages within the stadium. The broadcasting of games to a global audience requires robust satellite transmitters, multiple HD cameras, and specialized editing suites, which together account for about 11% of the stadium's overall energy expenditure [35].  
Starting from this, with detailed utility bills specific to the case study being unavailable and necessitating the estimation of electricity loads during a match or event, the electrical energy consumption was approximated at around 22,000 kWh in analogy to stadiums with similar capacity. This estimation aims to meet the peak levels of energy demand during events and ensure the storage of sufficient power from the photovoltaic system. Analysis of the official stadium calendar reveals that, on average, the venue hosts 102 events annually, translating to an average of two events per week. Leveraging these data, it becomes feasible to construct the annual curve of the stadium's electrical loads, focusing solely on the energy consumption during events.
# Estimation of Time-Series Forest Leaf Area Index (LAI) Based on Sentinel-2 and MODIS
## 3 Results  
### Remote Sensing Model of LAI in Sailanba Area  
#### 3.1.1 Univariate Regression Analysis  
Establish the single factor linear regression model and quadratic, logarithm, power, exponential and other nonlinear regression models with four vegetation indexes and LAI, respectively, and calculate \(R^{2}\) and \(RMSE\). The results are shown in Table 4.  
From the results of the linear and nonlinear regression models of the LAI and single vegetation index, the optimal regression equation of the DVI is a quadratic equation, and the respective optimal regression equations of the NDVI, SR and ARVI are power equations. The regression models of the NDVI and SR are not as effective as the ARVI, the estimation effect of the power regression equation of the ARVI and LAI was the best (\(R^{2}=0.499\), \(RMSE=0.413\)), while the regression model of the DVI and LAI was relatively worse than that of the other three single factor regression models of the vegetation index.
## 2 Experimental Tests  
### Basic Indicators of Coal Rock  
The samples were cut from the mechanically cut rock mass rather than blasted. According to the standards of the International Society for Rock Mechanics and Rock Engineering(ISRM) [27], standard cylindrical specimens measuring 50 \(\times\) 100 mm were prepared with specified tolerances: (1) the non-parallelism error between the two end surfaces of the specimen could not exceed 0.05 mm; (2) the diameter's error along the height of the specimen could not exceed 0.3 mm; and (3) the end face had to be perpendicular to the specimen's axis, and the maximum deviation could not exceed 0.25. After measuring the specimens, a standard specimen could be obtained through screening, as shown in Figure 1.  
To examine the mechanical properties of coal rock with respect to different time periods of immersion, we utilized five sets of samples for an immersion analysis. The immersion durations ranged from 0 to 30 days. Following immersion, we conducted ultrasonic longitudinal wave velocity tests and measured the weight of each sample to determine the water absorption rate. The physical indices after water immersion are listed in Table 1. The wave velocity of the coal rock specimens exhibited an upward trend with an increase in the water immersion duration.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Time (d)**} & **Size** & **Mass Before** & **Mass After** & **Water Ratio** & **Before** & **After** \\  & **(mm \(\times\) mm)** & **Immersion (g)** & **Immersion (g)** & **(\%)** & **Vp (km/s)** & **Vp (km/s)** \\ \hline
0 & 50.06 \(\times\) 100.10 & 323.90 & 323.90 & 0 & 2.12 & / \\
1 & 50.02 \(\times\) 100.00 & 325.70 & 329.10 & 0.89 & 2.34 & 2.45 \\
3 & 50.06 \(\times\) 100.00 & 323.60 & 327.60 & 1.61 & 2.24 & 2.48 \\
5 & 50.00 \(\times\) 100.04 & 321.40 & 326.30 & 1.31 & 2.19 & 2.50 \\
7 & 50.02 \(\times\) 100.04 & 325.40 & 330.80 & 1.66 & 2.18 & 2.41 \\
10 & 50.11 \(\times\) 10.10 & 321.5 & 326.6 & 1.59 & 2.22 & 2.43 \\
15 & 50.10 \(\times\) 100.30 & 317.4 & 322.9 & 1.73 & 2.19 & 2.45 \\
20 & 50.00 \(\times\) 100.34 & 325 & 331.2 & 1.91 & 2.17 & 2.38 \\
25 & 50.21 \(\times\) 100.18 & 327.4 & 333.8 & 1.95 & 2.27 & 2.45 \\
30 & 50.00 \(\times\) 100.28 & 325.1 & 331.5 & 1.97 & 2.27 & 2.32 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters of the coal specimens.  
Figure 1: The experimental specimen (**a**) The coal specimen and (**b**) the microstructure.
# Application of Blockchain Technologies in Verification of Software Metrics
## 3 Methodology
### Verification on Blockchain  
In order to achieve immutability and data integrity, simply calculating these metrics is insufficient, since this leaves room for manipulation. To address this issue, we utilize blockchain technology. It provides an immutable and transparent ledger (Figure 2), ensuring that the results of software metric calculations are tamper-proof and auditable [25]. By leveraging blockchain, the integrity of the metrics is achieved, preventing unauthorized modifications and enhancing trust in the accuracy of the data. Additionally, blockchain's decentralized nature enables secure and distributed verification of software metrics, offering a reliable mechanism for validating the quality of code across different stakeholders without relying on a centralized authority [26]. This approach not only improves transparency but also fosters accountability and traceability, which can serve as a basis for assessing long-term software quality.
## 3 Results and Discussion
### Simulation and Experiment
#### 3.1.1 Simulation Results
To validate the correctness of simulation results and the proposed methods, a test measurement was carried out to obtain the vibration displacement amplitude of the winding rotor, whose structure and parameters were consistent with the simulation model shown in Figure 1.  
Figure 11: Dynamic response of different investigated points in the \(X\) direction under the speed-up time of 60 s. (**a**) Investigated points on the winding rotor; (**b**) displacement amplitude curve; (**c**) velocity amplitude curve; (**d**) acceleration amplitude curve.
# Spatiotemporal Monitoring and Evaluation Method for Sand-Filling of Immersed Tube Tunnel Foundation
## 4 Application of Spatiotemporal Monitoring Model
### Spatiotemporal Monitoring for Sand-Filling
Therefore, the constrained end of the sand deposit can be filled in a shorter time. On the contrary, for the free end, since the sand deposit is not constrained by the adjacent sand deposit, the sand-filling time is longer. According to the diffusion radius with sand-filling time, the diffusion model of the expansion of the sand-filling hole W6 is shown in Figure 15. For the free end, the sand deposit gradually expands along the direction of the diffusion radius, which is defined as diffusion form. For the constrained end, the sand deposit is formed faster and connected with the adjacent sand deposit, which is defined as docking form. With the help of the monitoring and evaluation model, the state of the sand deposit was well monitored, providing guidance for sand-filling construction. Figure 13: Spatiotemporal monitoring results of sand-filling hole W6.
# A Routing Optimization Method for LEO Satellite Networks with Stochastic Link Failure
## 2 Mathematical Model
### LEO Satellite Network Model
#### 2.4.3 Stochastic Link Failure  
Generally, when the channel capacity \(C\) is insufficient to meet the requirement of transmission rate \(R_{0}\), we refer to this situation as communication failure. The stochastic failure matrix is denoted by \(\mathcal{B}^{c}{=}[b_{ij}^{c}]\in\mathbb{R}^{n\times n}\), where \(b_{ij}^{c}\in[0,1]\) represents the probability of communication failure from satellite \(i\) to \(j\), expressed as  
\[b_{ij}^{c}=P[C(S)\leq R_{0}] \tag{8}\]  
where \(S\) denotes the signal-to-noise ratio (SNR). Since \(C(\bullet)\) increases monotonically with \(S\), the failure probability can be shown as  
\[b_{ij}^{c}=P(S\leq a) \tag{9}\]  
In the formula, \(a\) is the preset threshold, and \(C^{-1}(\bullet)\) is the inverse function of \(C(\bullet)\).  
At the receiving system, the signal has additive white Gaussian noise with mean of 0 and variance of \(\sigma_{N}^{2}\) after the photoelectric detector. Therefore, the instantaneous signal-to-noise ratio \(S\) of the electrical signal received can be represented as:  
\[S=\frac{I^{2}}{2\sigma_{N}^{2}} \tag{10}\]  
Combining Equations (4), (6) and (10), the CDF of \(S\) can be indicated as  
\[F_{s}(s)=P(S\leq s)=P\left[\theta\geq\sqrt{\frac{1}{2G_{T}}\ln\frac{B}{s}} \right]=1-F_{\theta}\left[\sqrt{\frac{1}{2G_{T}}\ln\frac{B}{s}}\right]=\frac{s }{B}\exp(\frac{1}{4G_{T}\sigma_{\text{mod}}^{2}}) \tag{11}\]  
where  
\[B=\frac{1}{2\sigma_{N}^{2}}\left(\frac{\lambda}{4\pi d_{ij}}\right)^{4}(RP_{T} G_{T}G_{R}\eta_{T}\eta_{R})^{2} \tag{12}\]  
For a given threshold \(a\), the failure probability is calculated by \[b^{r}_{ij}=P(S\leq a)=\frac{a}{B}\exp(\frac{1}{4G_{T}\sigma_{\rm mod}^{2}}) \tag{13}\]
# Intelligent Handover Decision-Making for Vehicle-to-Everything (V2X) 5G Networks
## 5 Adjust the TOPSIS weights  
After updating the Q-values, the mechanism adjusts the weight (importance) of each network parameter to dynamically optimize TOPSIS decision-making. These adjustments ensure that handover decisions align with real-world network performance. For example,  
If a high SINR consistently leads to successful handovers, increase the SINR weight.  
If a low delay is critical, increase the delay weight.  
If high packet loss leads to failures, reduce the weight on unreliable connections.  
The fundamentals of the work can be explored in Algorithm 1.  
Figure 2: Flowchart of the Q-Learning implementation.  
```
1 Model the network grid
2 Identify UEs and plot their movements in the network grid sector
3 Inputs:
4 User movement in Sector \(Sec_{n}\)
5 Distance between each movement \(D\)
6 UE velocity \(V\)
7 List of \(SCs\) in UE Sector ID No (\(m\), \(n\)) = [x/20],[y/20] ranked using TOPSIS
8 Q-table \(Q(S,A)\) initialized with zero values
9 Q-Learning parameters:
10 Learning rate \(\alpha\) = 0.3
11 Discount factor \(\gamma\) = 0.7
12 Exploration rate \(\varepsilon\) = 1.0
13 Outputs:
14 Successful and unsuccessful handover attempts
15 Small cell selection and stay time assignment
16 Q-Learning adjusted weights
17 Network performance metrics
18 while List of \(SCs\) [i] \(\leq\) List of \(SCs\)[max] do
19 Extract and normalize parameters values \(N\_ij\) = \(X\_ij\)/sqrt(sum(\(X\_ij^{2}\)))
20 Compute weighted normalized values \(W\_ij\) = \(N\_ij\) * \(W\_j\)
21 Compute Ideal Positive and Negative Solutions (\(IPS\), \(INS\))
22 Compute Performance Index \(PI\) = \(D^{-}\)/(\(D^{+}\) + \(D^{-}\))
23 Rank SCs \(R_{n}\) = \(arg\) max \(Pi\)()
24 Filter top n% SCs \(LSC\) = \(\{Si/Si\in Sorted\ Rn\ \land\ Rn\ \times C/100\}\)
25 Compute ST Value for sector \(STV\) = \(D\)//
26 Assign \(ST\_SC\) = \(STV\) - (\(PI\) + \(R\))
27 endwhile
28 for each UE movement
29 while Handover required = True do
30 Query \(SC\) Load
31 if\(SC\) Load < Max \(SC\) Load then
32 Choose _Action (A): Stay or Handover using \(\varepsilon\)-Greedy strategy_: (1)
33 \(\bullet\) Exploration: Select a random action with _probability \(\varepsilon\)_
34 \(\bullet\) Exploitation: Select the action with the _highest_\(Q(S,A)\)_value_
35 Compute _Reward (R)_: (2)
36 \(\bullet\) Successful handover \(R\) = +20
37 \(\bullet\) Failed handover \(R\) = \(-10\)
38 \(\bullet\) Unnecessary handover (Ping-Pong) \(R\) = \(-20\)
39 \(\bullet\) Blocked handover \(R\) = +5
40 Update Q-table:
41 \(Q(S,\ A)=Q\ (S,A)+\alpha\ [R+\gamma\ max_{A^{\prime}}Q(S^{\prime},\ A)-Q(S,\ A)]\)
42 Adjust TOPSIS weights dynamically based on learned Q-values
43 if\(ST\leq STV\)then
44 Assign current \(SC\) = \(SC\)
45 Increment \(SC\) Load by 1
46 Decrement \(ST\) by 1
47 else
48\(\bullet\) Current \(SC\) = None
49 endwhile
50 endfor
```  
**Algorithm 1**IHD-V2X Algorithm
# Two Interval Upper-Bound \(Q\)-Function Approximations with Applications
## 2 Related Work and Motivation  
The main motivation of numerous research studies [7; 8; 9; 10; 11; 12; 13; 14; 15], including the one presented in this paper, is to solve the problem of deriving closed-form expressions for the SEP evaluation for signals with a Gaussian probability density function (PDF). In particular, the \begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol** & **Brief Description** \\ \hline \(Q(x)\) & Gaussian \(Q\)-function \\ \hline \(erfc\) & Complementary error function \\ \hline \(Q^{[11]}\) & [PERSON]’s \(Q\)-function approximation \\ \hline \(Q^{[12]}\) & \(Q\)-function approximation by [PERSON] et al. \\ \hline \(Q^{[13]}\) & [PERSON]’s \(Q\)-function approximation \\ \hline \(Q^{[14]}\) & \(Q\)-function approximation by [PERSON] et al. \\ \hline \(Q_{1}\)\({}^{[7]}\) and \(Q_{1}\)\({}^{[7]}\) & \(Q\)-function approximations by [PERSON] and [PERSON] \\ \hline \(Q^{om\_1}\) and \(Q^{om\_2}\) & \(Q\)-function approximations by authors \\ \hline \(P_{\beta}\) & Conditional SEP over the Nakagami-\(m\) fading channel amplitude for the assumed modulation format \\ \hline \(E_{b}\) and \(N_{0}\) & Average bit energy and the one-sided noise power spectral density, respectively, \\ \hline \(f_{x}(x)\) & PDF of the Nakagami-\(m\) fading channel amplitude \\ \hline \(\Omega\) & Average signal power \\ \hline \(m\) & Inverse normalized variance of \(x\) \\ \hline \(\Gamma(m)\) & Special gamma function \\ \hline \(f_{\gamma}(\gamma)\) & Nakagami-\(m\) PDF of the signal-to-noise ratio (SNR) per symbol \\ \hline \(\gamma\) & Average SNR per symbol \\ \hline \(\gamma\) & Relation between received carrier amplitude under fading influence \(x\), its mean squared value \(\Omega\), and the instantaneous signal-to-noise power ratio (SNR) per symbol. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Table of important symbols used throughout the paper, in order of appearance. problem reduces to providing a closed-form formula that approximates the \(Q\)-function, given by [25; 26]:  
\[Q(x)=\frac{1}{\sqrt{2\pi}}\int_{x}^{\infty}\exp\biggl{\{}-\frac{t^{2}}{2}\biggr{\}} dt. \tag{1}\]  
The Gaussian \(Q\)-function can be expressed in terms of the complementary error function:  
\[Q(x)\stackrel{{\Delta}}{{=}}\frac{1}{2}\ \text{erfc}\biggl{(} \frac{x}{\sqrt{2}}\biggr{)}, \tag{2}\]  
given by [18; 19]:  
\[erfc(x)=\frac{2}{\sqrt{\pi}}\int_{x}^{\infty}\exp\Bigl{\{}-t^{2}\Bigr{\}}dt. \tag{3}\]  
In [11], [PERSON] proposed a simple upper-bound approximation of the Gaussian \(Q\)-function for \(x\geq 0\), in the form  
\[Q^{[11]}(x)=\frac{1}{\sqrt{2\pi}}\frac{1}{x}\biggl{(}1-\exp\biggl{\{}-\sqrt{ \frac{\pi}{2}}x\biggr{\}}\biggr{)}\exp\biggl{\{}-\frac{x^{2}}{2}\biggr{\}}. \tag{4}\]  
[PERSON] derived analytically an even simpler upper-bound approximation of the \(Q\)-function in the form of the sum of two exponential functions [12]:  
\[Q^{[12]}(x)=\frac{1}{12}\exp\biggl{\{}-\frac{x^{2}}{2}\biggr{\}}+\frac{1}{4} \exp\biggl{\{}-\frac{2x^{2}}{3}\biggr{\}}\, \tag{5}\]  
which is the upper-bound approximation of the \(Q\)-function for \(x>0.5\).
# Telemedicine: A Survey of Telecommunication Technologies, Developments, and Challenges
## 3 Overview of Telemedicine
### Forms of Telemedicine
#### 3.4.2 Remote Health Monitoring  
Remote monitoring is a process of continuously monitoring patients with chronic diseases or specific conditions by means of various communication devices. This method is usually employed to monitor ailments such as heart disease, diabetes, and asthma. As it serves as an alternative to the intensive care unit (ICU), it can reduce post-operative costs [81]. This method enables real-time monitoring of patients outside of conventional medical facilities while they carry out their normal activities. This method is achieved by  
Figure 6: General classification of Telemedicine.  
Figure 7: Forms of Telemedicine Based on Mode of Operation.  
deploying wearable devices that monitor vital signs, acquire data, and transmit the data to a monitoring unit for remote diagnosis. This approach offers great advantages in terms of patient mobility and the ability to perform routine activities while monitoring patients for any health emergencies [43].
## 4 The Proposed Algorithm
### Iteration Framework
\(\overset{\text{-}}{\boldsymbol{Z}}_{j}^{(k-1)}\) is the previous design point, which is based on the previous equivalent boundary of \(f\left(\boldsymbol{Z}-\boldsymbol{S}_{j}^{(k-1)}\right)=v_{j}^{(k-1)}\). The rectangular domain represents the FD at the previous design point. \(F\) represents the domain of \(f\leq v_{j}^{(k-1)}\). If the FD is entirely in the \(F\) domain, \(\mathit{Bel}\left(f\leq v_{j}^{(k-1)}\right)=100\%\). In Figure 2, the FD of \(\boldsymbol{Z}\) is partially in the \(F\) domain, and \(\mathit{Bel}\left(f\leq v_{j}^{(k-1)}\right)\) is still less than \(\mathit{Bel}_{j}^{T}\). To satisfy \(\mathit{Bel}\left(f(\boldsymbol{Z})\geq v_{j}^{(k-1)}\right)\geq\mathit{Bel}_{ j}^{T}\), \(\overset{\text{-}}{\boldsymbol{Z}}^{(k-1)}\) needs to move further into the \(F\) domain. Therefore, the equivalent boundary needs to move further toward the \(F\) domain. The updated equivalent boundary is constructed as follows:  
\[f\left(\boldsymbol{Z}-\boldsymbol{S}_{j}^{(k)}\right)=v_{j}^{(k-1)},\ \ \boldsymbol{S}_{j}^{(k)}= \boldsymbol{S}_{j}^{(k-1)}+\Delta\boldsymbol{S}_{j}^{(k)} \tag{15}\]  
where \(\Delta\boldsymbol{S}_{j}^{(k)}\) denotes the increment of the previous shifting vector. The principle for calculating \(\Delta\boldsymbol{S}_{j}^{(k)}\) is set as \(\mathit{Bel}\left(f(\boldsymbol{Z})\geq v_{j}^{(k-1)}\right)\geq\mathit{Bel}_ {j}^{T}\) and is just satisfied. Thus, the mathematical model of \(\Delta\boldsymbol{S}_{j}^{(k)}\) is created as:  
\[ \begin{array}{l}\min_{\boldsymbol{s}}\|\boldsymbol{s}\|\\ \text{s.t. }\mathit{Bel}\left(f(\boldsymbol{Z}+\boldsymbol{s})\leq v_{j}^{(k-1)} \right)=\mathit{Bel}_{j}^{T}\end{array}  \tag{16}\]  
Equation (16) can be solved by multivariable optimization methods [38]. To further improve efficiency, the \(f\) -function is replaced by the \(L\)-function formulated in Equation (13). Figure 1: Uncertainty analysis for the performance function. FD: frame of discernment; MPFE: most probable focal element. Uncertain analysis and design optimization are carried out alternatively until they meet the following convergence criteria:  
\[\left. \begin{array}{c}Bel_{j}^{(k)}\geq Bel_{j}^{T}\\ \left|\frac{v_{j}^{(k)}-v_{j}^{(k-1)}}{v_{j}^{(k)}}\right|\leq\varepsilon_{r} \end{array} \right\}j=1,2,\ldots,n_{T} \tag{17}\]  
where \(\varepsilon_{r}\) is the minimal error limit. The solutions of \(\left(\mathbf{d}_{j}^{*},\overset{\mathbf{\cdot}}{X_{j}}^{*}\right),j=1,2,\ldots,n_{T}\) form the final optimal set. The flowchart of the EBRO algorithm is summarized as Figure 3.