esa-sceva/satcom-chunk-collection huggingface dataset:
# A Green Computing Business Aggregation Strategy for Low Earth Orbit Satellite Networks
## 5 Simulation Analysis
### Simulation Scenario Analysis with STK  
The simulation scenario constructed using STK is shown in Figure 4. This scenario is based on 18 orbital planes, with each orbital plane containing 40 satellites, forming a dense coverage network. This design ensures that, regardless of the satellite positions, connectivity can be provided to different areas of the Earth's surface.  
The control satellites operate at an altitude of 2000 km, with an orbital period of approximately two hours. Based on the analysis conducted using the STK simulation, the satellite network topology over a two-hour period was studied. First, the access duration of all satellites was analyzed, revealing an average access duration of approximately 10 min. Therefore, the two-hour period was divided into 12 intervals of 10 min each.  
Based on the statistical average, the number of satellites achieving stable network configurations in each time interval was determined to be five. Therefore, time intervals with a stable network topology involving five satellites were selected for analysis. This number represents the average count of satellites with stable network topologies, calculated across multiple time intervals within a single operational cycle. However, as satellite networks evolve and more satellites are deployed, this number will naturally increase, allowing for a more comprehensive representation of real-world Low Earth Orbit Satellite Networks (LSNs).  
\begin{table}
\begin{tabular}{l l} \hline
**Parameter** & **Value** \\ \hline Control satellite altitude & 2000 km \\ Business satellite altitude & 500–1500 km \\ Number of control satellites & 120 \\ Number of business satellites & 720 \\ Number of businesses & 1000–9000 \\ Elevation angle & 15.2–51.9 \\ \(P_{T}^{a}\) & 10–50 W \\ \(P_{R}^{a}\) & 5–25 W \\ \(P_{T}^{a}\) & 60–415 W \\ \(P_{TX}^{a}\) & 5–8 W \\ \(P_{RX}^{a}\) & 3–6 W \\ \(P_{C}^{a}\) & 45–55 W \\ \(P_{tx}\) & 3–5 W \\ \(P_{rx}\) & 1–3 W \\ \(B_{GS_{i}}\) & 20–40 MHz \\ \(B_{S_{i}S_{k}}\) & 20–40 MHz \\ \(C_{S_{i}S_{k}}\) & \(2\times 10^{8}\) bit/s \\ \(f_{f}\) & \(2\times 10^{10}\) cycle/s \\ \(N_{CS_{i}}\) & \(-140.4\) dBm \\ \(N_{S_{i}S_{k}}\) & \(-110\) dBm \\ \(\kappa\) & 300 cycle/bit \\ \(\tau^{\prime}\) & 1 s \\ \(\tau^{\prime}_{ij}\) & 0.6 s \\ \(B_{ij}\) & \(1\times 10^{3}\) bit–\(1\times 10^{6}\) bit \\ \hline \end{tabular}
\end{table}
Table 1: LSN parameters.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Learning rate (lr) & 0.001 \\ Epsilon decay & 0.995 \\ Discount factor (\(\gamma\)) & 0.99 \\ Episodes & 500 \\ \hline \end{tabular}
\end{table}
Table 2: Double DQN Strategy Parameters.
## 4 A Two-Stage Optimal Scheduling Model for Wind-PV-Thermal-Storage System
### Day-Ahead Optimal Scheduling Model
#### 4.1.2 Economy Scheduling Objective Function
The second is the power dispatching cost, including the start-up and climbing cost of thermal power units and the restricted generating cost of renewable energy units. \[f_{\text{GO},t}=C_{\text{F}1,i}+C_{\text{F}2,i}+C_{\text{WL},i}+C_{\text{PVL},i} \tag{18}\]  
\(\bullet\) The Start-Stop Cost and Slope Climbing Cost of Thermal Power Plants  
When large-scale renewable energy is connected to the grid, its intermittency and volatility will lead to frequent start-stop and slope climbing of thermal power units, which will increase operating costs. The start-stop and climbing cost function is as follows:  
\[C_{\text{F}1,i}=u_{\text{F},i,t}(1-u_{\text{F},i,t-1})S_{\text{F},i,t} \tag{19}\]  
\[C_{\text{F}2,i,t}=\gamma_{\text{F}}\left|\frac{dP_{\text{F},i,t}}{dt}\right| \tag{20}\]  
where \(u_{\text{F},i,t}\) is the number of starting thermal power units in time period \(t\), \(S_{\text{F},i,t}\) is the start-stop cost of the thermal power unit in the period time \(t\), and \(\gamma_{\text{F}}\) is the slope climbing cost function coefficient of thermal power units. \(\bullet\) Restricted Generating Cost of Renewable Energy Units  
\[C_{\text{WL},t}=\sum_{j=1}^{N_{\text{W}}}c_{\text{WL}}P_{\text{W},\text{Q},j} \tag{21}\]  
\[C_{\text{PVL},t}=\sum_{k=1}^{N_{\text{PV}}}c_{\text{PVL}}P_{\text{PV},\text{Q},j} \tag{22}\]  
where \(c_{\text{WL}}\) and \(c_{\text{PVL}}\) are the unit restricted generating cost, \(P_{\text{W},\text{Q},j}\) is the restricted power output of \(j\)-th wind unit, and \(P_{\text{PV},\text{Q},k}\) is the restricted power output of the \(k\)-th wind unit. The third item is the adjustment cost of BESS. \[f_{\text{E,}t}=\Delta P_{\text{BESS,}t}\rho_{\text{E}} \tag{23}\]  
where \(\Delta P_{\text{BESS,}t}\) is the adjustment power of storage at time \(t\), and the positive direction is discharging, and the negative value represents the energy storage device is in the charging state. \(\rho_{\text{E}}\) is the unit power cost of energy storage regulation, $/kW. The fourth item is the dispatching cost of day-ahead demand response. In the day-ahead stage, only PDR virtual units participate in scheduling, which can be expressed by the change of electricity sales revenue on the grid side:  
\[f_{\text{DR,}t}=P_{\text{PDR,}t}\rho_{t,0}-P_{\text{PDR,}t,\text{act}}\rho_{t} \tag{24}\]  
where \(\rho_{t,0}\) is the initial electricity price at time \(t\), and \(\rho_{t}\) is the electricity price at time \(t\).
# Design Studies of Re-Entrant Square Cavities
## 2 Design and Operation of RRSC and RF Section
### Design of R.F. Section  
In order to design the RF section at the proposed frequency of 60 GHz, three fundamental parameters must be determined--(i) the distance between adjacent cavities, (ii) drift tube radius, and (iii) beam radius.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**RRSC** & \multicolumn{4}{c}{**Dimension (mm)**} & \multicolumn{4}{c}{**Simulation Results**} \\ \cline{2-9}
**Type** & **a** & **b** & **c** & **w** & **g** & **f (GHz)** & **R/Q (\(\Omega\))** & **Q (Unloaded)** \\ \hline input/output & 3 & 1.8 & 0.44 & 0.6 & 0.395 & 60 & 86 & 1364 \\ intermediate & 3 & 1.7 & 0.44 & 0.6 & 0.38 & 60 & 85 & 1359 \\ \hline \hline \end{tabular} a \(\rightarrow\) cavity height; b \(\rightarrow\) re-entrant height; c \(\rightarrow\) beam tunnel radius; w \(\rightarrow\) cavity width; g \(\rightarrow\) beam interaction gap.  
\end{table}
Table 1: Dimensions of RRSCs and simulation results.  
Figure 1: Perspective view of RRSC: (**a**) input/output cavity, (**b**) intermediate cavity, and (**c**) cross section of an intermediate cavity.
# Ensemble Deep Learning on Time-Series Representation of Tweets for Rumor Detection in Social Media
## 3 Ensemble Learning
### Overview of the Proposed Model  
Our proposed model has two key components: the data pre-processing method and ensemble model. Firstly, raw Twitter conversations are processed to transform them into the required data format and then the transformed data are supplied to the ensemble model to perform classification. The ensemble model consists of six different neural networks (base learners) that are trained using the generated time-series data and their predictions are grouped such that a majority voting scheme is applied on them to determine the outcome as rumor or non-rumor.
# A Comparative Study of 3D UE Positioning in 5G New Radio with a Single Station
## 4 Performance Comparison
### Performance Comparison of Positioning accuracy in Different Contexts  
In this section, we use the RMSE as the metric for the estimation accuracy of the EM and subspace algorithms. The varying parameters in the following subsections are the SNR, antenna scale, and subcarrier space. Due to the fact that SRSs from different UEs are orthogonal in the time, frequency, and codes domain, the positioning of multiple UEs can be directly decomposed into single target positioning. Therefore, the simulation results shown in this section were executed as single target detection.
# Design and Analysis of Joint Source-Channel Code System with Fixed-Length Code
## 5 Results and Discussion
### Decoding Complexity Analysis  
We tested the decoding time of each scheme in a PC with an Intel core i5 3.1 GHz CPU and 4 GB RAM. Monte Carlo simulations were on the matlab2018. All the schemes are single-core based. The simulation decoding time under different SNRs are listed in Table 6.  
It can be seen that, for the JSCC schemes with a source block code, namely, the LBC-RSC scheme, the LBC-IrRNSC scheme, and our scheme, the decoding time varies from 8.7 s to 19.5 s. While the decoding time of VLC-IrRNSC scheme varies from 122.5 s to 217.9 s for different SNRs, the SSCC scheme consumes about 20 s for each frame. This indicates the propose scheme has a low decoding complexity.  
The decoding complexity mainly depends on the computation complexity of the iterative decoders and on the number of decoding iterations. In our scheme, the decoder of source block code is realized by Equation (1), and its decoding complexity depends on the size of the source alphabet and the codeword length. The channel decoder employs the BCJR algorithm. In our simulations, the size of the source alphabet is 16 and the codeword length satisfies \(L\leq 6\). Compared to the channel convolutional decoders which employ the BCJR algorithm on the long code trellis, the source block codes have a lower computational decoding complexity.  
The VLC decoder is modeled by the bit-level trellis, whose state space is the set of the internal nodes of the VLC binary tree. The sizes of the trellises can be considered a measure of the computational decoding complexity [35]. Because the maximum constraint length of the channel code is limited to \(D\leq 3\), the average state number of the channel code satisfies \(|T_{\text{C}}|\leq 4\). The VLC with free distance \(d_{f}~{}=~{}3\) has \(|\overline{T_{\text{S}}}|~{}=~{}48\) states, which is at least 10 times \(|\overline{T_{\text{C}}}|\). Hence, the decoding complexity of VLC decoder is much higher than the channel decoder and the source block code. Therefore, the VLC-IrRNSC scheme takes the longest decoding time.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Scheme**} & \multicolumn{4}{c}{**Decoding Time (s)**} \\ \cline{2-4}  & **4 dB** & **4.5 dB** & **5 dB** \\ \hline SSCC & 24.8 & 26.8 & 22.3 \\ VLC-IrRNSC & 161.4 & 217.9 & 122.5 \\ LBC – RSC \(L~{}=~{}5\) & 11.6 & 19.5 & 12.5 \\ LBC – IrRNSC \(L~{}=~{}6\) & 12.4 & 16.1 & 10.5 \\ Proposed \(L~{}=~{}6\) & 13.3 & 11.4 & 8.7 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Simulation decoding time of different source channel coding schemes.  
Figure 8: The RSNR performance of different systems over the AWGN channel.
## 4 Conclusions  
Sphere packing models, along with numerical modelling and simulation, have been combined to predict key pore-structure parameters and sound absorption properties of monomodal and bimodal macroporous structures. The predictions indicate the clear significance of permeability and acoustic properties on these key pore-structure-related parameters--typically, mean pore size, mean pore opening, and sample porosity, and the results were substantiated by making a comparison with relevant literature data. Bimodal macroporous structures have the effect of increasing the number of pore openings and pore volume, which results in a shift in absorption spectra to frequency minima, and a progressive flattening. The change in absorption spectra is consistent with greater penetration of pressure waves across the pores. The creation of dual-porosity dilated structures and changes in fluid velocity emphasise how pore-structure-related properties can be used to modulate the sound absorption properties of macroporous structures. It is evident that these structures have the potential for improving their sound absorption performance--optimal performance indicated by maintaining pore volume fraction (\(\varepsilon\)) between 0.68 and 0.76, and pore size to pore opening ratio (\(r_{p}/r_{w}\)) between 4.0 and 6.0. In the ongoing work, experiments are being conducted to fabricate bimodal, dual- and multi-porosity foams and determine single, multispecies and multiphase momentum, mass, and energy transport, as well as their vibroacoustic performance.  
CRediT's author statement for this research work can be found below. A.J.O., software: conceptualisation, methodology, writing-original draft preparation, investigation, data curation, resources. O.D.A. and I.B., visualisation, validation, formal analysis. O.A.O. and J.O.O., reviewing and editing, project administration, formal analysis. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to organizational privacy.  
The University of Nottingham, UK, Synopsis-Simpleware Ltd., Dulles, VA, USA, Bowers and Wilkins Group UK, Petroleum Technology Development Fund, Nigeria and Federal University of Technology Minna, Nigeria for the provision of office space, licenses, foams, X-ray CT datasets and technical support needed for the successful completion of this work.  
The authors declare no conflict of interest.
## 3 Results and Discussion  
### Effect of Inoculum Amount on Fermentation Without Filler Material  
The secondary seed liquid was added to the fermentation broth at inoculation amounts of 10%, 20%, and 30%, respectively. The OD\({}_{620}\) was determined (Figure 3a). The CDW was determined (Figure 3b). Figure 3a shows that from 132 h of fermentation to the fermentation equilibrium stage, the maximum OD value of 10% was 4.96, while the maximum OD value of 20% was 5.55. The maximum OD value of 30% was 6.11. Figure 3b shows that the maximum CDW for 10% was 14.70 g/L, the maximum CDW for 20% was 16.70 g/L, and the maximum CDW for 30% was 17.70 g/L, after reaching equilibrium. Table 1 shows that the growth rate of 10% inoculum to the logarithmic phase was 0.04783 h\({}^{-1}\); that of 20% inoculum was 0.04379 h\({}^{-1}\), and that of 30% inoculum was 0.03933 h\({}^{-1}\).  
The results showed that the 30% inoculation's CDW was 20.41% higher than the 10% max CDW and 5.99% higher than the 20% max CDW. Secondary seed cultures were used to inoculate the bioreactors for high-cell-density growth [48]. An increase in the inoculum decreases the growth rate in the logarithmic phase. If the inoculation amount is too low, the time from the lag period to the logarithmic growth period is prolonged, the fermentation cycle is prolonged, and the bacterial volume is small. If the inoculum amount is too large, the bacteria quickly consume the medium, causing them to quickly enter the equilibrium and decline phases; their growth rate is lower in the logarithmic phase; and fewer new cells are produced. Therefore, in the absence of filler material, appropriately increasing the inoculum amount can increase the bacterial yield. In a homemade reactor without filler material, 30% secondary seed inoculum should be used.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Inoculum Amount (\%)** & **Logarithmic Period Growth Rate (h\({}^{-1}\))** & **R\({}^{2}\)** \\ \hline
10 & 0.04783 & 0.9904 \\
20 & 0.04379 & 0.9758 \\
30 & 0.03933 & 0.9760 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Growth rates with different inoculation rates in the logarithmic phase.
## 2 Methods
### Numerical Method
#### 2.2.1 Scattering Models  
The most commonly applied scattering implementations described in [44] are the Hybrid Reflectance Model (HRM) and vector mixing (VM). The HRM is based on the decomposition of the reflected sound into specularly and diffusely deflected parts. In this method, a random number between 0 and 1 is used to determine whether the reflection is specular or scattered. This number is compared with the surface scattering coefficient(s) assigned to the surface. In case it exceeds the value of s, the scattered energy is assumed to be distributed according to Lambert's Law, i.e., the intensity of the reflected ray is independent on the angle of incidence but proportional to the cosine of the angle of reflection [2]. This model complies with the definition of the scattering coefficient based on ISO 17497-1, which defines it in a quantitative way as the fraction of the non-specularly reflected energy.  
Figure 2: Scheme of the plan for the two acoustic conditions in the hall: reflective and diffusive. The other surfaces in gray were set in an absorptive condition, except for the floor, which was made of a hard reflective surface. The 24 microphone positions are indicated in a crossed array that occupies half of the hall. Positions 1, 3, and 5 were used to perform the binaural simulations and then in the listening test.  
On the contrary, the VM is based on the linear interpolation of the specular and diffuse reflection [45], i.e., the direction of a reflection vector is calculated by adding the specular vector scaled by a factor (1-s) to a scattered vector following a certain direction that has been scaled by a factor s. This is the basic concept implemented in Odeon [41; 46], named \"vector-based scattering\", where the scattered vector follows a random direction, generated according to the Lambert distribution. Thus, the scattered reflections are also implemented from a qualitative point of view, which makes this model a more realistic approximation of the diffusive surface directivity.
## 4 Discussion
### Uncertainty in FPAR  
FPAR is one of the main uncertainty sources that linearly influence GPP [16, 50, 85]. The accuracy of satellite-based FPAR could be affected by many factors, e.g., land cover data, surface reflectance or NDVI data, and the algorithm [86]. According to [PERSON] _et al._[55], satellite-derived biophysical parameters are less sensitive to errors in the land cover classification than land cover-derived parameters. This is because they have only second order dependency on land cover type and the land cover type accounts for at most 10% of the variation in FPAR. We estimated NDVI\({}_{\text{max}}\) and NDVI\({}_{\text{min}}\) using the NDVIIs located at the 98% and 2% of the NDVI frequency distribution for all vegetated area without considering various land cover types.  
While using the same GLOPEM-CEVSA model settings, GPP\({}_{\text{MOD15}}\) was more accurate than GPP\({}_{\text{MOD17}}\) based on PSN, which indicated a high potential of the GLOPEM-CEVSA model for this study region. However, the higher accuracy in simulating GPPs implied that the adaptive SG filtered FPAR data could have a better performance than the FPAR data based on the linear interpolation approach using in the MODIS algorithm. In order to both fill unreliable and missing FPAR values and keep reliable values, the MODIS model applied linear interpolation of the nearest reliable values to recover temporal time series of FPAR [25]. This method probably removed some intrinsic seasonal variability and resulted in a lower correlation coefficient despite a lower bias. Conversely, we used the MODIS FPAR data filtered by the adaptive SG method in the TIMESAT software package [62, 87] as input into the GLOPEM-CEVSA model, which enabled us to capture more of the seasonal variability [88, 89]. We did not use the data quality information in the MODIS FPAR product (MOD15A2) as previous studies showed that the quality flag layer does not provide sufficient information regarding data quality [89], while the quality control information was considered in the MODIS algorithm [25].
## 2 Results and Discussion
### Characterization
In comparison, all show characteristic peaks of TiO\({}_{2}\)(101), TiO\({}_{2}\)(103), TiO\({}_{2}\)(004), TiO\({}_{2}\)(112), TiO\({}_{2}\)(200), TiO\({}_{2}\)(105), TiO\({}_{2}\)(211), TiO\({}_{2}\)(204), TiO\({}_{2}\)(116), TiO\({}_{2}\)(220), TiO\({}_{2}\)(107), TiO\({}_{2}\)(301), and TiO\({}_{2}\)(312) at 25.3\({}^{\circ}\), 36.9\({}^{\circ}\), 37.8\({}^{\circ}\), 38.6\({}^{\circ}\),48.0\({}^{\circ}\), 53.9\({}^{\circ}\), 55.1\({}^{\circ}\), 62.7\({}^{\circ}\), 68.8\({}^{\circ}\), 70.3\({}^{\circ}\), 74.1\({}^{\circ}\), 76.0\({}^{\circ}\), and 83.1\({}^{\circ}\). The 13 peaks are due to the formation of Ti oxides in the crystalline phase, which are in accordance with the TiO\({}_{2}\) crystallographic data (Reference code:03-065-5714). Although the presence of crystal structures is not a differentiated factor for the hydrophobicity or hydrophilicity of the surfaces, a significant difference was obviously found between the pristine Ti surface (a) and other three Ti surfaces with nano TiO\({}_{2}\) particles in Figure 3b-d.
# Risk-Aware Wireless Positioning in
## 6 Conclusions  
In this paper, we proposed a safety-aware positioning scheme based on the fingerprinting scheme, which efficiently decreases the average risk in the maritime environment. We defined a risk function, which is influenced by various factors such as location accuracy, depth, ocean current speed and geometry. Then, we found the optimal positioning signal transmission frequency that minimizes the user's average risk. The numerical result shows that our proposed positioning scheme decreases the average risk, compared to the conventional one, assuming a user with the same battery power budget.  
Conceptualization, K.Y.S. and J.H.L.; methodology, K.Y.S. and J.H.L.; software, K.Y.S.; validation, K.Y.S., J.Y.R. and J.H.L.; formal analysis, K.Y.S., J.Y.R. and J.H.L.; investigation, K.Y.S. and J.H.L.; resources, K.Y.S., J.Y.R. and J.H.L.; data curation, K.Y.S.; writing--original draft preparation, K.Y.S.; writing--review and editing, J.Y.R. and J.H.L.; visualization, K.Y.S.; supervision, J.Y.R. and J.H.L.; project administration, J.Y.R. and J.H.L.; funding acquisition, J.Y.R. and J.H.L.  
This work was supported by Hankuk University of Foreign Studies Research Fund of 2018 and by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. NRF-2016R1C1B2010281). This work was also supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP; Ministry of Science, ICT & Future Planning) (No. 2018-0229).  
The authors declare no conflict of interest.  
Figure 10: The average risk with respect to the sensitivity of the risk to the water depth (i.e., \(\beta\)).
# Remote Sensing for International Peace and Security: Its Role and Implications
## 5 Future Perspectives
### Conflict Management Perspective
#### 5.2.1 Conflict Prevention  
In the future, remote sensing should more actively be used to identify and monitor resources that might be the source of conflicts. [PERSON] et al. [116] discussed the role of big data including geospatial information in conflict prevention. However, specific international cooperation and policy are required to avoid breaching national boundaries during such applications.  
More research is needed on how to effectively use remote sensing techniques to prevent conflicts from happening. This may be an analysis of the regions known for recurring conflicts and identification of regions most likely to be hampered by future challenges of climate change. Furthermore, geographical indicators of possible sites with militant activity should be established [125].