esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study on LTE Mobile Network Performance Parameters for Controlled Drone Flights
## 5 Evaluation and Analysis of Measured Results
### Recommendation  
For safer and more efficient UAV traffic management and applications through cellular connectivity, real-time digital twinning (DT) of mobile network coverage and performance is seen as a revolutionary technology that will undoubtedly shape the next generation of aircraft and spacecraft [49]. Therefore, mobile network measurements described in this research were also used to test the precision and usability of the DT platform for automated analytics of cellular connectivity in the airspace with flight path analysis and data for BVLOS drone operations--\"AirborneRF\" [50]. A model of mobile network coverage and UAV flight DT was developed to elaborate on the future research directions for the management of cellular network-enabled UAV-traffic management, see Figure 14. The set of KPI values developed could be used in the DT of mobile network coverage to plan and manage UAV traffic or adapt the network infrastructure to ensure continuous connectivity for UAV flights. According to defined KPIs, different altitudes or routes could be proposed for UAV flights. Those KPIs in DT could also serve as reference points for the deployment and upgrade of mobile network infrastructure.  
DT, as a concept, was introduced in the year 2002 by [PERSON] as a digitally connected virtual replica of the physical asset [51]. Besides various DT applications in different industries, International Telecommunication Union has also published recommendations on the requirements and architecture of a digital twin network (DTN) for analyzing, diagnosing, emulating, and controlling the physical network [52].  
Figure 13: SINR average value over 50 m distance during the whole flight at 40, 60, 90 and 110 m height.  
The proposed DT model digitally mirrors physical mobile network coverage and physical UAV, as well as UAV flight routes in real-time. This model could be used for UAV traffic management, adapting drone traffic in real-time based on the availability of network coverage and capacity. The DT model could also be used for planning or even the allocation of mobile network infrastructure in real-time depending on UAV connectivity needs.
## 3 Results and Discussion
### Effects of Leading on Lead Speciation and Soil Quality
The pH value fluctuation of soil and redox conditions has important effects on the reducible fraction. After leaching with HCl, a large amount of iron and manganese was extracted during the leaching process, resulting in a reduction in the reducible lead content from 1895 mg\(\cdot\)kg\({}^{-1}\) to 555 mg\(\cdot\)kg\({}^{-1}\), a decrease of 71%. Compared with HCl, the remediation efficiency of CA for the reducible lead was only 47% because of its weak acidity and oxidation reduction. The oxidizable fraction usually refers to lead binding with OM and sulfide in soil. Since the soil used in this study contains high OM (Table 1), the oxidizable lead could be easily removed by the chelation of Cl\({}^{-}\) and CA. The residue fraction is usually relatively stable, but the residual lead in soil after leaching by HCl and CA also decreased by 41% and 20%, respectively. This may be due to the dynamic equilibrium of lead forms in the soil. When the other forms of lead (acid-soluble fraction, reducible fraction, and oxidizable fraction) are consumed, part of the residual lead will be converted into them [41]. A 0.05 mol\(\cdot\)L\({}^{-1}\) concentration of CA has a high removal rate of 83% for the highly active, acid-soluble lead in contaminated soil, and the removal rate of the oxidizable lead is as high as 77%, which was comparable to 0.2 mol\(\cdot\)L\({}^{-1}\) HCl. CA can reduce the total lead content from 8043 to 3732 mg\(\cdot\)kg\({}^{-1}\), and the remediation efficiency reaches 53.7%.The \(M_{F}\) value of the soil after leaching by CA is 12.3%, meaning that the mobility of lead is greatly reduced. The pH of the soil after leaching by CA is 6.97, and that of the protogenetic soil is 7.89, indicating that CA is a mild eluent that is Biodegradable and causes no secondary pollution, which is very suitable for the remediation of lead-contaminated soil. In the future, compound leaching will be used to further solve the problem that the total lead content in the residue soil after single CA leaching still slightly exceeded the standard.
## 3 Results  
### Relationship between Site Index and Volume  
SI ranged form 16 to 30 m (base age 25) in the control plots. The exponential, sigmoidal, and linear relationship between volume and SI had \(R^{2}\) values of 0.93 (Figure 2). All parameters used to derive the relationship between site index and volume were highly significant (\(p<0.0001\)). When leave-one-out cross validation was carried out among the three model forms, MAE, RMSE, and PRESS statistics were lowest for the linear model, followed by the sigmoidal model (Table 4). However, the sigmoidal model performed better when extrapolated outside the regressor variable hull. Both the exponential and linear models performed poorly when extrapolated outside the regressor variable hull. For example, the linear model predicted negative yield when site index was below 12.9 m. Therefore, the sigmoidal model was selected as the best model to predict volume (m\({}^{3}\) ha\({}^{-1}\)) from site index at base age 25 (m):  
\[\mathrm{Volume}=\frac{379.57}{1+\mathrm{e}^{(4.556-0.185\mathrm{SI})}} \tag{2}\]  
Figure 2: Scatter plots between stand volume (m\({}^{3}\) ha\({}^{-1}\)) and site index (m). Left, middle, and right diagram show the exponential, sigmoidal, and linear relationships, respectively.
## Wind Tunnel Tests
### Experimental Results  
The variation of thrust and power consumption is shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Wind Speed (m/s)** & **Rotor Speed (r/min)** & **Diameter (mm)** & _h/D_ & _l/D_ & **Rotor Pitch (mm)** \\ \hline
0, 2.5, 4.0 & 1500–2300 & 400 & 0.19 & 1.2 & 157 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental parameters.  
Figure 4: Sketch of experimental setup.  
It showed that both the thrust and power of the multi-rotor aircraft increased with the rotational speed. Compared with 0 m/s, the thrust decreased with the effect of horizontal wind with a higher rotor speed. However, the power still increased at the same time. This extra power may be caused by the aerodynamic interference between the upper and lower rotors of the coaxial rotor pair, as well as the strengthened wake between adjacent coaxial rotor pairs.  
The variations of the increments of thrust coefficient \(C_{T}\) and power coefficient \(C_{P}\) are shown in Figure 6.  
As shown in Figure 6, the thrust increment of \(C_{T}\) gradually decreased with the rotor speed. In particular, \(C_{T}\) with wind effect is lower than that at 0 m/s from 2000 r/min. On the contrary, the power coefficient \(C_{P}\) at the horizontal wind of 2.5 m/s and 4 m/s increased significantly compared with 0 m/s, and the maximum increments were 3.51% at 2.5 m/s with 1600 r/min and 3.37% at 4.0 m/s with 1900 r/min, respectively. This may have been caused by the strong interaction on downwash flows of adjacent coaxial rotor pairs.  
The variation of hover efficiency \(\eta\) is shown in Figure 7.  
Figure 5: Thrust and power consumption in different rotational speeds.  
Figure 6: \(C_{T}\) and \(C_{P}\) increment with different wind speeds.  
It is clear that there was higher hover efficiency with horizontal wind disturbance, with a lower rotor speed of around 1800 r/min compared with 0 m/s. The hover efficiency \(\eta\) at the wind speed of 2.5 m/s and 4.0 m/s reached a maximum of 0.302 and 0.306 at 1600 r/min. However, \(\eta\) with wind effect decreased at higher rotor speed around 1800 r/min. In addition, the hover efficiency without wind disturbance is obviously higher than that with wind disturbance. Moreover, lower hover efficiency is found at higher wind speed. This indicated that the horizontal wind disturbance strengthened the strong aerodynamic interference between the adjacent coaxial rotor pairs with a negative impact on the hover performance of the multi-rotor aircraft at higher rotational speed.  
Therefore, although the multi-rotor aircraft consumes more power with the horizontal wind disturbance, the horizontal wind improved its aerodynamic performance by increasing hover efficiency at lower rotational speed. With the increase in the rotor speed, the performance decreased with higher power where the horizontal wind aggravated the downwash flows of the coaxial rotors.
## 3 Numerical Simulations
### Influence of Excitation Amplitude on Vibration Characteristics
It fully demonstrates the performance of IBPEH that can achieve high efficiency produced under the condition of lower excitation amplitude. Figure 11: Bifurcation diagram with excitation amplitude as parameter: (**a**) BPEH; (**b**) IBPEH. Figure 12: RMS voltage diagram with excitation amplitude as parameter: (**a**) BPEH; (**b**) IBPEH. When \(f=11\) Hz, continue to increase the excitation amplitude to the condition of \(A=14\) m/s\({}^{2}\). Figure 15a,b shows the simulation results of time-domain velocity versus the displacement and the voltage-time diagram, respectively. It can be seen from Figure 15a combined with the bifurcation diagram in Figure 11 that both BPEH and IBPEH are in a state of large-amplitude interwell periodic motion under this excitation condition. Concerning Figure 15b, it can be seen that BPEH and IBPEH can continuously yield voltage with high energy. Compared with BPEH, the RMS voltage produced by IBPEH increased by 24.56%. For a bistable energy harvesting system, the bistable large-amplitude period oscillation is a motion state in which the energy harvester operates with high efficiency. It can be seen from the above analysis that with the increase in the excitation amplitude, the kinetic energy obtained by the system increases, which will help the piezoelectric energy harvester to achieve a large-amplitude periodic motion state. In addition, compared with BPEH, IBPEH can achieve a large-amplitude periodic oscillation motion state at a lower excitation level, and within the range of excitation amplitude studied, IBPEH can produce a higher voltage. Figure 14: Simulation results of time-domain velocity versus the displacement and voltage–time diagram when \(f=11\) Hz and \(A=11\) m/s\({}^{2}\). (**a**) Simulation results of time-domain velocity versus the displacement; (**b**) voltage–time diagram. Figure 13: Simulation results of time-domain velocity versus the displacement and voltage–time diagram when \(f=11\) Hz and \(A=8\) m/s\({}^{2}\). (**a**) Simulation results of time-domain velocity versus the displacement; (**b**) voltage–time diagram.
# MGHT: Statistical Methodology for Missing-Data Imputation in Food Composition Databases
## 2 Materials and Methods
### Related Work
#### 2.2.2 Non-Negative Matrix Factorization  
Recently, non-negative matrix factorization (NMF) has been proven to be useful for missing value imputation in many applications in the environment, pattern recognition, multimedia, text mining, and DNA gene expressions. The roots of NMF can be traced back to the 1970s [25] and was initiated and studied extensively by [PERSON] and [PERSON] [26].  
NMF [27; 28] is a group of algorithms in multivariate analysis and linear algebra where a matrix \(V\) is factorized into two matrices \(W\) and \(H\), where all three matrices have no negative elements. This property of non-negativity makes the resulting matrices easy to inspect.  
Non-negative matrix factorization (NMF) has previously been shown to be a useful decomposition for multivariate data. The authors of [29] interpreted the factorization in a new way and used it to generate missing attributes from test data. They provided a joint optimization scheme for the missing attributes as well as the NMF factors. In addition, they proved the monotonic convergence of their algorithms, and presented classification results for cases with missing attributes.
# MSEN: A Multi-Scale Evolutionary Network for Modeling the Evolution of Temporal Knowledge Graphs
## 3 The Proposed Method
### Scoring Function and Loss Function  
In this section, the paper mainly introduces the details of scoring function and the loss function. In order to obtain the probability of an event occurring at timestamp \(\mathrm{t}+1\) in the future, ConvTransE [12] is utilized as the decoder to calculate the probability of the existence between entities at timestamp \(\mathrm{t}+1\) under the relation \(\mathrm{r}\):  
\[\mathrm{P}_{\mathrm{t+1}}(\mathrm{o}|\mathrm{s},\mathrm{r})=\lambda(\mathrm{h }_{\mathrm{o,t+1}}\mathrm{Conv}\mathrm{T}\mathrm{a}\mathrm{E}(\mathrm{h}_{ \mathrm{s,t+1}},\mathrm{h}_{\mathrm{r,t+1}})), \tag{21}\]where \(\lambda(\cdot)\) is the sigmoid function. \(\mathrm{h}_{\mathrm{s},\mathrm{t}+1}\) and \(\mathrm{h}_{\mathrm{r},\mathrm{t}+1}\) are the embedding representations of entities and relations at timestamp \(\mathrm{t}+1\). \(\mathrm{h}_{\mathrm{o},\mathrm{t}+1}\) is the embedding representations of entities after global and local fusion.  
Following RE-GCN [12], the loss function during training is defined as:  
\[\mathrm{L}_{\mathrm{e}}=-\mathrm{\sum}_{\mathrm{t}=0}^{\mathrm{T}}\mathrm{ \sum}_{(\mathrm{s},\mathrm{r},0,\mathrm{t}+1)\in\mathrm{G}_{\mathrm{t}+1}} \mathrm{logP}_{\mathrm{t}+1}(\mathrm{o}|\mathrm{s},\mathrm{r}), \tag{22}\]  
\[\mathrm{L}_{\mathrm{r}}=-\mathrm{\sum}_{\mathrm{t}=0}^{\mathrm{T}}\mathrm{ \sum}_{(\mathrm{s},\mathrm{r},0,\mathrm{t}+1)\in\mathrm{G}_{\mathrm{t}+1}} \mathrm{logP}_{\mathrm{t}+1}(\mathrm{r}|\mathrm{s},\mathrm{o}). \tag{23}\]  
The final loss is \(\mathrm{L}=\lambda\mathrm{L}_{\mathrm{e}}+(1-\lambda)\mathrm{L}_{\mathrm{r}}+ \mathrm{\mu}\|\Theta\|_{2}\), where \(\mathrm{L}_{\mathrm{e}}\) is the loss for predicting entities, \(\mathrm{L}_{\mathrm{r}}\) is the loss for predicting relations, \(\lambda\) is the hyperparameter balancing different tasks. \(\|\Theta\|_{2}\) is \(\mathrm{L}_{2}\) norm, and \(\mathrm{\mu}\) is to control the penalty term. The overall training algorithm is shown in Algorithm 1.  
```
Input: The train set \(\{\mathbf{G}_{0},\mathbf{G}_{1},\mathbf{G}_{2},\mathbf{G}_{3},\cdots\mathbf{G} _{\mathbf{t}_{n}-1}\}\); the number of epoch \(\mathrm{n}\). Output: The minimum Loss on the train set.
```  
**Algorithm 1**Training of MSEN
## 1 Introduction
[25] evaluated the thermal and daylighting performances of aerogel-glazed skylight in comparison to double-glazed skylight using a test cell on the roof of a building in Changsha, China. The roof-top surface represents the generality of surfaces that are exposed to direct solar radiation in urban areas. In addition, in the tropical region, the intensity of the direct solar radiation that reaches the roof-top surface is higher than the rest of the Earth. Concomitantly, the annual rainfall is also high; therefore, buildings in the tropical region commonly employ pitched roofs as protection, not only from the solar radiation, but also from rain water. In Malaysia, which is subjected to the tropical climate, commonly, cement roof tiles are employed as part of the pitched roof owing to their low cost and high resistance to weather conditions, albeit with inauspicious thermal properties [37], which, in the case of the roof tiles, is typically not considered the main priority [38]. At present, research on the adoption of aerogel in the building envelope is predominated by the development and evaluation of aerogel-based composite insulation materials that can be installed in the roof, on the interior wall or on the window reveal, as well as aerogel-filled glazing for the window and skylight. Accordingly, research on the adoption of aerogel on or as part of the roof tile material, coupled with the adoption of aerogel-based composite insulation materials, is deficient, notwithstanding its prospect in inducing a consequential reduction in the heat transfer through the roof, in view of the fact that the roof assembly, in the tropical region, especially the roof tiles, are directly exposed to intense and prolonged solar radiation, in addition to the high amounts of rainfall, all-year round. In the present study, the application of silica-aerogel-incorporated composite cement paste as render on the roof-top surface is coupled with the installation of a novel silica-aerogel-incorporated composite insulation board below the roof tiles. Findings of the present study are most applicable to pitched roof assemblies in tropical regions and, additionally, are also relevant to those in other regions with similar climate where intense solar radiation is present.
## 3 Results
### Evaluation of SPME Procedure Parameters
#### 3.4.2 Evaluation of Extraction Parameters  
Agitation speed directly impacts the mass transfer of analytes from the sample matrix to the adsorbent. Speeds ranging from 0 to 1000 rpm were tested, and increasing the agitation up to 750 rpm improved extraction efficiency (Figure 5c). This effect is due to the reduction in the boundary layer around the SPME Arrow, promoting faster mass transfer of analytes to the SBA-15 surface [25; 26; 27]. However, speeds above 750 rpm led to vortex formation, reducing the contact time between the adsorbent and the sample, thus decreasing extraction efficiency. Therefore, 750 rpm was chosen as the optimal stirring  
Figure 5: Optimization of SPME Arrow procedures: (**a**) sample volume, (**b**) NaCl concentration, (**c**) agitation speed, (**d**) extraction temperature, (**e**) extraction time, and (**f**) desorption solvent ((1) acetonitrile, (2) acetone, (3) methanol, (4) acetonitrile/water = 9:1 (\(\
icefrac{\
u}{\
u}\)), (5) acetonitrile/water = 7:3 (\(\
icefrac{\
u}{\
u}\)), (6) acetonitrile/water = 1:9 (\(\
icefrac{\
u}{\
u}\))), and (**g**) desorption time.  
rate, providing sufficient turbulence for analyte transfer while maintaining good contact between the coating and the sample solution.  
Extraction temperature plays a crucial role in adsorption by influencing analyte diffusion and interaction with the adsorbent. Temperatures ranging from 25 \({}^{\circ}\)C to 70 \({}^{\circ}\)C were tested, with optimal extraction efficiency observed at 25 \({}^{\circ}\)C (Figure 5d). Lower temperatures reduced analyte diffusion rates into the SBA-15 coating, resulting in low adsorption efficiency, while temperatures above 30 \({}^{\circ}\)C caused desorption of the analytes from the SPME Arrow due to increased molecular motion, disrupting adsorption equilibrium. Thus, 25 \({}^{\circ}\)C was selected as the optimal temperature to balance adsorption efficiency, ensuring efficient extraction while minimizing evaporation.  
Extraction time was varied between 2 and 45 min to determine the optimal duration for maximum analyte recovery. Extraction efficiency increased with time as more analytes diffused into the SBA-15 pores during prolonged contact. However, beyond 15 min, extraction efficiency was saturated, indicating that equilibrium had been reached, and no further adsorption occurred (Figure 5e). Longer extraction times did not improve efficiency and could result in analyte desorption back into the solution. Thus, 15 min was selected as the optimal extraction time, balancing maximum efficiency with practical analysis time. Compared to the reported SPME coatings, the SBA-15/PAN coating showed not only a wider cover range for the five veterinary drugs but also the shortest equilibrium time [37; 38; 39; 40], which is beneficial for on-site applications.
# One-Class LSTM Network for Anomalous Network Traffic Detection
## 4 Experimental Setup
### Compared Methods
#### 4.1.2 Deep Baseline Models  
1. _Deep Convolution Autoencoder (DCAE)_ as per the formulation in [29]. The encoder part and decoder part of the DCAE architecture contain four portions. Every portion contains a convolutional layer, a batch normalization (BN) layer, an exponential linear unit (ELU) layer, and a down-sampling/up-sampling layer, which can provide a better representation of the convolutional filters. The DCAE is trained using a mean-squared error (MSE) loss function to enable the hidden layers to encode high-quality, nonlinear feature representations of the input data.
2. _One-class neural network (OC-NN)_ as per the formulation in [21]. A feed-forward neural network consisting of a single hidden layer with linear activation functions is trained with the OC-NN objective. The optimal value of the parameter \(v\in(0,\,1)\), which is equivalent to the percentage of anomalies in each dataset, is set according to the respective outlier proportions.
3. _Soft-Bound SVDD_ and _One-Class Deep SVDD_ as per the formulation in [24]. For the encoder, we employed the same network architectures as those used for the DCAE models. An initial learning rate \(\eta=10^{-4}\) with a two-phase learning rate schedule was employed following the implementation used in [24].
# Seismic Periodic Noise Attenuation Based on Sparse Representation Using a Noise Dictionary
## 2 Method
### Periodic Noise Representation  
Based on the stationarity of periodic noise, the basis function \(\varphi_{0}\) of periodic noise is obtained by extending the waveform \(w\) iteratively to the length of seismic data on a single trace and then energy-normalized:  
\[\varphi=\left[w,w,\ldots,w\right] \tag{7}\]  
\[\varphi_{0}=\frac{\varphi}{\left\|\varphi\right\|_{2}} \tag{8}\]  
where \(\left\|\bullet\right\|_{2}\) is the \(l_{2}\) norm. The size of \(\varphi\) is \(N\times 1\), which is equal to that of \(S_{j}\). The noise dictionary is constructed by basis functions of different phases:  
\[\mathbf{D}=\left[\varphi_{0}\quad\varphi_{1}\quad\varphi_{2}\quad\cdots\quad \varphi_{T_{j}-1}\right] \tag{9}\]  
where \(\varphi_{1},\varphi_{2},\ldots,\varphi_{T-1}\) are obtained by cyclically shifting \(1,2,\ldots,\widetilde{T}-1\) time samples. The size of \(\mathbf{D}\) is \(N\times\widetilde{T}\). The dictionary provides a sparse representation of periodic noise. In sparse representation theory [20], the signals can be efficiently explained as linear combinations of prespecified basis functions, where the linear coefficients are sparse. Based on sparse representation theory, the mathematical model of our noise representation is  
\[S_{j}=\mathbf{D}x \tag{10}\]  
where \(S_{j}(j=1,2,\ldots,m)\) is the actual periodic noise and \(x\) is its coefficient represented by the dictionary \(\mathbf{D}\). The size of \(x\) is \(\widetilde{T}\times 1\). An approach to solve Equation (10) is using the sparsity constraint via an \(L_{0}\) regularization term. Then, the periodic noise of multi-trace seismic data is obtained by solving the following optimization problem:  
\[\widetilde{S}_{j}=\underset{S_{j}}{\operatorname{argmin}}\big{\|}S_{j}- \mathbf{D}x\big{\|}_{2}^{2}\ \ s.t.\|x\|_{0}\leq 1 \tag{11}\]  
where \(\widetilde{S}_{j}(j=1,2,\ldots,m)\) is the approximate periodic noise and \(x\) is its coefficient represented by the dictionary \(\mathbf{D}\). In the optimization problem, the sparsity of this representation is 1. Because only one basis function corresponds to a single trace, the coefficient \(x\) has only one non-zero component. This is the reason for the condition on the right in Equation (11) \(\left\|x\right\|_{0}\leq 1\). Equation (11) is solved by the matching pursuit algorithm, which entails computing the inner products between the residual and the dictionary elements, updating the coefficient, and updating the residual iteratively [20,21]. Finally, the de-noised data are obtained by subtracting the periodic noise from the raw seismic data.