esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Solution to the Natural Waves Problem  
Let us consider natural waves, which propagate from the region \(y=-\infty\) in the direction of \(y=+\infty\). The \(y\)-dependence is \(\exp(ik\beta|y|)\). \(\mathrm{Re}\beta>0\) and \(\mathrm{Im}\beta>0\), \(\beta\) are unknown propagation constants of the natural wave. As the graphene conductivity is a complex-valued function, the graphene plane absorbs the electromagnetic field and the propagation constants are complex values.  
To obtain the field of natural wave, one can represent it as plane waves in each domain \(z>0,0>z>-h\) and \(z<-h\)[33]:  
\[H_{x}=\left\{\begin{array}{ll}\epsilon\exp(ikz\gamma(\beta)),\ \ z>0,\\ f\cos(k_{1}z\gamma(\beta/\sqrt{\epsilon}))+\sin(ik_{1}z\gamma(\beta/\sqrt{ \epsilon})),\ \ 0>z>-h,\\ g\exp(-ikz\gamma(\beta)),\ \ z<-h.\end{array}\right. \tag{20}\]The term \(\exp(ik\beta|y|)\) is omitted. For the square root, we take the branch \(\text{Re}\gamma<0\) or \(\text{Im}\gamma>0\). Then the amplitude of the field vanishes if \(y\to\infty\) or \(|z|\to\infty\).  
After enforcement of the boundary conditions on the graphene plane (1) and (2), as well as at the vacuum-dielectric interface (2) and (4), one can obtain relationships between amplitudes in each domain and the transcendental equation relatively propagation constant.  
As mentioned above, the Fourier amplitudes in (5) and, consequently, the terms within the integral in (19), have poles at the points, which we denoted as \(\beta\). Thus the equation relatively propagation constant is  
\[\frac{(\eta+i\Gamma(\eta))\sin(k_{1}\eta(y-\theta))}{\eta}=\infty \tag{21}\]  
or  
\[a(\theta)-b(\theta)-1=0. \tag{22}\]
# Electron Beam Characterization of REBCO-Coated Conductors at Cryogenic Conditions
###### Abstract  
Particle accelerators with superconducting magnets operating at cryogenic temperatures use a beam screen (BS) liner that extracts heat generated by the circulating bunched charge particle beam before it can reach the magnets. The BS surface, commonly made of high-conductivity copper, provides a low impedance for beam stability reasons, low secondary electron yield (SEY) to mitigate the electron-cloud (EC) effect, and low electron-stimulated desorption yield (ESD) to limit the dynamic pressure rise due to EC. Rare-earth barium copper oxide (REBCO) high-temperature superconductors (HTSs) recently reached technical maturity, are produced as coated conductor tapes (REBCO-CCs), and will be considered for application in future colliders to decrease the BS impedance and enable operation at around 50 K, consequently relaxing the cryogenic requirements. Aside from HTS properties, industry-grade REBCO-CCs also need qualification for EC and dynamic vacuum compatibility under accelerator-like conditions. Hence, we report the SEY and ESD measured at cryogenic temperatures of 12 K under low-energy electron irradiation of 0-1.4 keV. We also verify the sample compositions and morphologies using the XPS, SEM, and EDS methods. The energy and dose dependencies of ESD are comparable to those of technical-grade metals and one sample reached \(\mathrm{SEY}_{MAX}=1.2\) after electron conditioning.  
REBCO; HTS; SEY; ESD; SEM; EDS; XPS; cryogenic temperatures; electron conditioning
# Plant Height Estimation in Corn Fields Based on Column Space Segmentation Algorithm
## 2 Materials and Methods
### Point Cloud Preprocessing and Segmentation Analysis for Corn Populations
#### 2.6.2 Point Cloud Preprocessing and Segmentation Methods  
In this study, the point cloud data of maize seedlings were preprocessed and segmented using two primary methods: the color-based region-growing algorithm and the column space approximate segmentation algorithm. These steps were critical for separating individual plants and preparing the data for subsequent analysis.  
The point cloud data were first processed using the color-based region-growing algorithm to segment the maize seedlings into individual plants. Key parameters, such as distance threshold, color threshold, and the minimum and maximum number of clustered points, were established. The algorithm performed well in simpler scenarios, achieving clear single-plant segmentation. However, under conditions of dense planting or occlusion, the segmentation results were less effective, requiring additional adjustments to the parameters.  
The direction angle of each point in the point cloud was calculated by analyzing the angle between the point's normal vector and the horizontal plane. This analysis helped to isolate stalk points from leaf points. Points with a direction angle between 1 and 2 radians were retained as part of the stalk, while others were excluded. This threshold-based approach effectively separated stalk point clouds, enabling more accurate plant height extraction.  
To address the limitations of the region-growing algorithm in dense field conditions, a column space approximate segmentation algorithm was developed. This method combined the column space segmentation approach with the axis-aligned bounding box (AABB) method to approximate the segmentation of individual plants. By adjusting the edge length of the bounding boxes, the algorithm ensured effective segmentation of the point cloud into single plants. This method is particularly suited for estimating plant height at the field scale, despite the challenges of overlapping plants and shading in dense canopies.  
Figure 4: Point cloud preprocessing.
# Variable Selection Using Deep Variational Information Bottleneck with Drop-Out-One Loss
## 4 Experimental Analysis
### Real Experiments
\begin{table}
\begin{tabular}{c c c c} \hline
**Dataset** & **Method** & **Most Relevant Variables** & **Most Irrelevant Variables** \\ \hline \multirow{8}{*}{BHP} & Lasso [1] & LSTAT, DIS & \(\textit{irre7},\textit{irre8}\) \\  & Elastic Net [32] & CRIM, RM & \(\textit{irre9},\textit{irre11}\) \\  & SCAD [3] & CRIM, RM & AGE, \(\textit{irre3}\) \\  & RF [13] & CRIM, RM & \(\textit{irre6},\textit{irre9}\) \\  & AdaBoost [33] & CRIM, RM & \(\textit{irre6},\textit{irre9}\) \\  & GL [7] & CRIM, ZN & \(\textit{irre9},\textit{irre13}\), \\  & SpAM [4] & RM, AGE & \(\textit{irre1},\textit{irre3}\), \\  & Knockoff [11] & RM, PTRATIO & \(\textit{irre11}\), INDUS \\  & Lasso Net [14] & NOX, DIS & \(\textit{irre12}\), \(\textit{irre13}\) \\  & **DeepVIB (G)** & LSTAT, PTRATO & \(\textit{irre13}\), \(\textit{irre4}\) \\  & **DeepVIB (E)** & LSTAT, CRIM & \(\textit{irre9},\textit{irre5}\) \\ \hline \multirow{8}{*}{CHP} & Lasso [1] & MedInc, AveBedrms & \(\textit{irre7}\), Population \\  & Elastic Net [32] & MedInc, Latitude & \(\textit{irre3}\), Population \\  & SCAD [3] & AveRooms, AveBedrms & \(\textit{irre6}\), \(\textit{irre7}\) \\  & RF [13] & MedInc, AveOccup & \(\textit{irre6}\), \(\textit{irre9}\) \\  & AdaBoost [33] & MedInc, Longitude & \(\textit{irre5}\), \(\textit{irre6}\) \\  & GL [7] & MedInc, Longitude & \(\textit{irre1}\), \(\textit{irre4}\) \\  & SpAM [4] & MedInc, AveRooms & \(\textit{irre18}\), \(\textit{irre20}\) \\  & Knockoff [11] & MedInc, Latitude & AveRooms, AveBedrms \\  & Lasso Net [14] & MedInc, HouseAge & \(\textit{irre7}\), \(\textit{irre20}\) \\  & **DeepVIB (G)** & MedInc, Latitude & \(\textit{irre3}\), \(\textit{irre6}\) \\  & **DeepVIB (E)** & MedInc, AveOccup & \(\textit{irre5}\), \(\textit{irre12}\) \\ \hline \multirow{8}{*}{Diabetes} & Lasso [1] & BMI, BP & \(\textit{irre15}\), \(\textit{irre19}\) \\  & Elastic Net [32] & BMI, BP & \(\textit{irre4}\), \(\textit{irre16}\) \\  & SCAD [3] & s1, s5 & age, \(\textit{irre1}\) \\  & RF [13] & BMI, s5 & sex, s4 \\  & AdaBoost [33] & BMI, s5 & age, \(\textit{irre18}\) \\  & GL [7] & BMI, s6 & \(\textit{irre7}\), \(\textit{irre12}\) \\  & SpAM [4] & BMI, s4 & s3, \(\textit{irre14}\) \\  & Knockoff [11] & BMI, s5 & \(\textit{irre17}\), \(\textit{irre18}\) \\  & Lasso Net [14] & BMI, BP & \(\textit{irre5}\), \(\textit{irre20}\) \\  & **DeepVIB (G)** & BMI, sex & \(\textit{irre3}\), \(\textit{irre5}\) \\  & **DeepVIB (E)** & BMI, s5 & \(\textit{irre7}\), \(\textit{irre9}\) \\ \hline \end{tabular}
\end{table} 
Table 5: Variable selection results on real data.
# Precise Orbit Determination for GNSS Maneuvering Satellite with the Constraint of a Predicted Clock
## 2 Methodology
### Constraining of Predicted Clock  
In POD of maneuvering satellite, we fit the satellite clock model to the precise clock estimates before maneuvering to determine the model coefficients and then extrapolate the model to the maneuvering and post-maneuvering periods to generate the predicted satellite clocks. The predicted clocks are then introduced as fictitious observations to constrain the satellite clock parameters in the RPPP POD processing, namely  
\[\overline{x}_{clk}(t_{i})=x_{clk}(t_{i})+v(t_{i}) \tag{3}\]  
where \(\overline{x}_{clk}(t_{i})\) is the predicted clock and \(v(t_{i})\) is the prediction error at epoch \(t_{i}\); \(x_{clk}(t_{i})\) is the satellite clock parameter.  
Obviously, the weights of the fictitious observations depend on the prediction error at each epoch. To weight the fictitious observations properly, the precision of the predicted satellite clocks versus prediction time is assessed for GPS and BDS at first. The GFZ 30 s final precise clock products of 2017 are collected and used in this analysis [25]. Specifically, we first fit the clock models to the 24-h clock products to determine the model coefficients in (2) and extrapolate the models to generate predicted clocks for the next 4 h. It is noted that the preprocessing should be carried out to get rid of the large data gaps, outliers as well as the clock jumps [26]. The constant coefficient \(a_{0}\) is computed with the latest satellite clock estimates in products to avoid possible deviation across the processing boundary [24]. Then, the predicted clocks are compared to the corresponding products, resulting in prediction errors. The RMS of the prediction errors versus prediction time is computed for the GPS Rb, GPS Cs, BDS GEO, BDS IGSO and BDS MEO clocks, the results of which are presented in Figure 1. As shown in this figure, the RMS of prediction errors increases over the prediction time for all clocks. The GPS Rb clocks show the highest prediction accuracy, while that of the GPS Cs clocks is the lowest. The BDS GEO and IGSO satellite clocks exhibit similar precision, which is a little lower than that of the BDS MEO satellites. Then, the weighting function of the fictitious observations can be determined by fitting to the RMS profiles in Figure 1, which is expressed as  
\[\gamma(\tau)=\begin{cases}0.8\times\sqrt{\tau}&GPS,Rb\\ 0.1+0.1\times\tau&GPS,Cs\\ 0.1+0.17\times\tau&BDS,MEO\\ 0.1+0.24\times\tau&BDS,GEO/IGSO\end{cases} \tag{4}\]  
where \(\tau\) is the prediction time, the unit of which is hours.  
Figure 1: Root mean square (RMS) of the prediction errors of different satellite clock types under different time latencies.
## 4 Analysis and Discussions
### Government Perspective  
In this subsection, we compare SWs and the amount of government subsidy to explore consequence in the perspective of government organizations.  
**Theorem 6**.: _The social welfare and the amount of government subsidy is higher in Policy C in both the games._  
We refer Appendix H for the proof of Theorem 6. Theorem 6 demonstrates that the government expenditure and SW are always higher in Policy C. Therefore, the outcomes of Theorem 4-6 are very much alike. Higher subsidy cause higher profits, as well as GL, and SW consequently increased. Graphical representation of SW, the amount of government subsidy (GI) (\(GI_{i}^{C}=\rho_{i}^{C}D_{i}^{C}\); \(GI_{i}^{RE}=\eta_{i}^{RE}\kappa\tau_{i}^{RE^{2}}\); and the ratios of relative change of GL with total amount of government subsidy (\(\Delta\theta G_{i}^{j}=\frac{\rho_{i}^{j}-\rho_{i}^{N}}{GI_{i}^{j}}\)) in six scenarios is depicted in Figure 3a-c.  
The above figures support the statement of Theorem 6. However, if we investigate at the macro level, then one cannot draw a straightforward conclusion in favor of Policy C. By correlating Figure 2e with Figure 3c, the ratio of investment efficiency reflects the different consequence. In the perspective of the manufacturer and government organization, Policy C may lead to an inadequate investment decision. However, one cannot ignore the influence of consumers until they enjoy the higher privilege in Policy C because government support directly passes to the consumer. The total amount of expenditure for the government is too high in Policy C, which does not yield a higher relative improvement in GL.
## 2 Materials and Methods
### AuNPs as Photocatalysts  
The photocatalytic activities of the biosynthesized and chemically synthesized AuNPs were evaluated by monitoring the reduction of dyes, such as Victoria blue R (VBR), malachite green (MG), and methylene blue (MB). The solution containing 25 mg/L of AuNPs and 100 mg/L of dyes was stirred at 100 rpm for 20 min in the dark before it was exposed to visible light to reach adsorption equilibrium. Subsequently, the solution was irradiated with visible light for 12 min at 100 rpm. Then, 2 mL of the suspension was taken at constant intervals and centrifuged. The photocatalytic degradation of dyes was monitored using a UV-vis spectrometer at 610 nm for VBR, 660 nm for MG, and 619 nm for MB. The dye degradation percentage or removal efficiency was analyzed using the following formula.  
\[\text{Dye degradation}\left(\%\right)=\frac{C_{0}-C_{e}}{C_{0}}\times 100 \tag{2}\]  
where \(C_{0}\) and \(C_{e}\) correspond to the dye concentrations before and after photocatalysis, respectively.
# Remote Sensing of Ecosystem Water Use Efficiency in Different Ecozones of the North China Plain
## 2 Data and Methods
### Methods
#### 2.3.5 Variation Coefficient  
The stability of WUE was quantified by using the variation coefficient (CV).  
\[CV_{WUE}=\frac{\sigma_{WUE}}{\overline{WUE}} \tag{7}\]  
where \(CV_{WUE}\) represents the coefficient of variation of each pixel of WUE in North China from 2001 to 2017, \(\sigma_{WUE}\) is the standard deviation of WUE values, and \(\overline{WUE}\) is the multiyear mean of WUE in North China from 2001 to 2017. The smaller \(CV_{WUE}\) is, the more stable the time series is, and vice versa, the more fluctuation there is. In this study, fluctuation will be divided into five classes: high fluctuation (\(CV_{WUE}>0.5\)), moderate-high fluctuation (\(0.15<CV_{WUE}<0.5\)), medium fluctuation (\(0.1<CV_{WUE}<0.15\)), medium-low fluctuation (\(0.05<CV_{WUE}<0.1\)), and low fluctuation (\(CV_{WUE}<0.05\)).
# Spoken Language Identification System Using Convolutional Recurrent Neural Network
## 6 Results and Discussion
### Spoken Language Identification Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Study and Ref.** & **Features** & **Classifiers** & **Corpus** & **Classes** & **Languages** & **Results (\%)** \\ \hline \multirow{4}{*}{[PERSON] et al. [2]} & \multirow{4}{*}{Spectrogram} & \multirow{4}{*}{CRNN} & European & \multirow{4}{*}{6} & English, French, German, & \multirow{4}{*}{Accuracy = 91.00} \\  & & & & Statements and News Channels & & & & \\  & & & on YouTube & & & & \\ \hline \multirow{4}{*}{[PERSON] and [PERSON]. [11]} & \multirow{4}{*}{Rhythm} & \multirow{4}{*}{R-vector with SVM} & \multirow{4}{*}{Mozilla} & STEC & 2 & English and Korean & ER = 2.26 \\ \cline{4-6}  & & & & Mozilla & 3 & Chinese, English, & \multirow{4}{*}{ER = 53.27} \\ \cline{4-6}  & & & & & & & \\ \cline{1-1} \cline{5-6}  & & & & & & & \\ \hline \multirow{4}{*}{[PERSON] et al. [20]} & \multirow{4}{*}{MFCC-SDC} & \multirow{4}{*}{LSTM-RNN} & \multirow{4}{*}{NIST LRE} & \multirow{4}{*}{8} & Dart, English, French, & \multirow{4}{*}{Accuracy = 70.90} \\  & & & & & & & \\ \cline{1-1} \cline{5-6}  & & & & & & & \\ \hline \multirow{4}{*}{[PERSON] and [PERSON] [27]} & \multirow{4}{*}{DNN-BN} & \multirow{4}{*}{LSTM} & \multirow{4}{*}{AP17-OLR} & \multirow{4}{*}{10} & Tibetan, Japanese, Kazakh, & \multirow{4}{*}{ER = 50.00} \\  & & & & & & & \\ \cline{1-1} \cline{5-6}  & & & & & & & \\ \cline{1-1} \cline{5-6}  & & & & & & & \\ \hline [PERSON] et al. [21] & Mel-spectrograms & CRNN & EU Repo & 6 & English, French, German, & \multirow{4}{*}{Accuracy = 83.00} \\  & & & & & Greek, Italian, and Spanish & \\ \hline \multirow{4}{*}{[PERSON] et al. [33]} & \multirow{4}{*}{
\begin{tabular}{c} Log-Mel \\ spectrograms \\ \end{tabular} } & \multirow{4}{*}{CNN} & Mozilla & 4 & Estonian, Tamil, Turkish, & \multirow{4}{*}{Accuracy = 80.21} \\  & & & & & & \\ \cline{1-1} \cline{5-6}  & & & & & & \\ \cline{1-1} \cline{5-6}  & & & & & & \\ \hline \multirow{4}{*}{**Proposed System**} & \multirow{4}{*}{**GTCC-MFCC**} & \multirow{4}{*}{**CRNN**} & \multirow{4}{*}{**Mozilla**} & \multirow{4}{*}{7} & Arabic, German, English, & \multirow{4}{*}{**Accuracy = 92.81**} \\  & & & & & & \\ \cline{1-1} \cline{5-6}  & & & & & & \\ \cline{1-1} \cline{5-6}  & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of proposed CRNN language identification architecture with other studies using different corpora with varying numbers of classes. As shown in Table 9, we clarified the features, corpus, languages, the proposed classifiers used in each study, and the number of classes to make a general comparison of the available studies in the spoken LID field. Compared to these studies, we found that our proposed system applied to languages of industrial countries, including Arabic, obtained the best results.
# Difficulties and Challenges of Anomaly Detection in Smart Cities: A Laboratory Analysis
## 4 Attack Description
### Stages 2 and 3: Selective Forwarding  
In a selective forwarding attack, a compromised gateway is used to discard part of the packets that it should forward to the next hop. To do so, attackers must have had access previously to one or more nodes with forwarding capabilities. It should be noted that this is possible in the context of a smart city, where nodes are often placed unprotected in the streets. Then, once attackers have taken control of a gateway, they can reprogram it to discard some received packets instead of forwarding them. In our case, as shown in Figure 2, we have implemented two different selective forwarding attacks in the third and fourth stage. The attacks have targeted the Z1 network compromising its gateway. In the first attack, the gateway randomly discards 50% of the received packets coming from any other Z1 node. In the second stage, the gateway randomly discards 75% of the packets.  
In order to implement this attack in the Z1 gateway interface, we have modified the _packet_input_ function in the Contiki (Contiki is the Z1 nodes' operating system) file \"/core/net/ip/tcpip.c\" (tcpip.c file contains the implementation of the TCP/IP protocol), as it can be seen in Listing 1.  
```
staticvoid packet_input(void) { //Modifiedsectiontopperformaselvecforwardingattack unsignedshorthreshold_rand_max; unsignedshorthandom_number; random_number=random_rand(); //Nextlinecontrolsthepercentageofdiscardedpackets(50%inthiscase) threshold_rand_max=RANDM_RAND_MAX*0.5; if(random_number<threshold_rand_max){ //Thenodeperformstheattackanddoesnotforwardapacket uip_len=0; } //Endofthemodifiedsectiontopperformaselvecforwardingattack
#ifUIP_CONF_IP_FORWARD if(uip_len>0){ [...]
#endif/*UIP_CONF_IP_FORWARD*/ }
```
## 2 Materials and Methods  
### EnergyPLAN Simulation Tool  
The nature of this work requires the use of a bottom-up approach, as both energy sectors and technologies must be analysed in detail. EnergyPLAN, HOMER or H2 RES are examples of the bottom-up model options that can be found in the literature. Among them, the free and user-friendly software EnergyPLAN v16.2 (Aalborg University, Aalborg, Denmark) has been selected as it is one of the most used tools to perform energy planning and support energy decision makers [49].  
The EnergyPLAN tool applies a purely deterministic simulation approach with a description of all sectors: electricity, heating, cooling, industry, and transportation. Developed by Aalborg University, EnergyPLAN has been specifically designed to assist in the design of planning strategies under the theory of Choice Awareness [50]. Instead of applying numerical solvers to resolve optimisations, the software uses an heuristic approach, performing hourly energy balances and taking a set of predefined dispatching priorities [51]. This fact, together with the sectorial aggregation principle, significantly reduces the computational effort, being able to run a one-year period in just few seconds.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Current System**[38] & **PTECan**[48] \\ \hline Wind On-shore [MW] & 222.6 & 1700 \\ Wind Off-shore [MW] & 0.0 & 505.3 \\ PV On-shore [MW] & 107.6 & 1650 \\ PV Off-shore [MW] & 0.0 & 27 \\ PV Self-Consumption [MW] & 38 & 829 \\ Wave Drive [MW] & 0.0 & 5.0 \\ Small Hydropower[MW] & 1.2 & 2.6 \\ Biogas [MW] & 1.6 & 17.8 \\ Geothermal [MW] & 0.0 & 20.0 \\ H\({}_{2}\) turbines [MW] & - & 210 \\ \hline EES–User [GW/GWh] & - & 2.6/2.5 \\ EES–Distributed [GW/GWh] & - & 1.1/1.1 \\ EES–Large Scale [MW/GWh] & - & 313/5.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Current structure and PTECan proposal for renewable energy and energy storage capacities in Tenerife.  
Figure 3 includes an overview of the software structure. As inputs, the user can define the main aspects of the analysed energy system: the different energy demands, the renewable energy sources, the capacities of the energy stations, the costs, and the regulation strategies. Among the outputs, EnergyPLAN provides the energy balance and the resulting annual production, fuel consumption, electricity import/export, CO\({}_{2}\) emissions, etc. [51]. Additional description of the software can be found in [52].  
EnergyPLAN has been widely applied in energy planning exercises with a special focus on sustainable energy solutions [51] at national [53], regional [54], and local levels [55]. The literature also reports that EnergyPLAN is one of the most common tools applied to evaluate isolated systems [49]. Although these systems might bring additional constraints such as reliability and robustness of the power grid, EnergyPLAN can successfully address them using alternative methods based on specific indicators. Several application examples taking, as a test case, isolated island systems, like the present research work, can be found in the literature [55, 56].