esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Datasets and Methodology
### Model Setup and Lightning Data Assimilation Procedure
The physical meaning of Equation (1) and nudging is that (a) the mixing ratio nudged by WRF increases as the flash rate increases, and (b) a convective environment is already represented in the model when graupel is simulated and the nudging term must decrease as the WRF graupel mixing ratio increases. Setting \(A=0.95\) in Equation (1) gives a water vapor mixing ratio saturated or close to saturation, depending on the flash rate and graupel mixing ratio. When lightning is observed, the atmospheric column is saturated or close to saturation between the lifting condensation level (LCL) and \(-20\)\({}^{\circ}\)C, the latter being the top of the charging zone [29]. For this reason, we change the modeled mixing ratio only when it is necessary to bring it close to saturation. In [PERSON] et al. [29], this substitution is done between 0 \({}^{\circ}\)C and \(-20\)\({}^{\circ}\)C. However, in recent studies [18; 82], a vertical level below 0 \({}^{\circ}\)C has been employed as the lower level for LDA. In particular, in [PERSON] et al. [18], the LCL is employed as the lower level for assimilation. When using 0 \({}^{\circ}\)C, the level at which water vapor adjustment starts can be too high, resulting in worse performance compared to the LCL height. In this study, the assimilation is performed between the LCL and \(-20\)\({}^{\circ}\)C and LDA is performed every 10 min within the assimilation period (first 6 h of each run). The idea behind this setting is to maximize the impact of LDA on the forecast. [PERSON] et al. [30], using a similar WRF setting, showed that choosing \(A=0.85\) (\(B=0.17\), i.e., \(A+B=1.02\)) resulted in worse performance compared to \(A=0.95\) (\(B=0.07\)), the latter giving more weight to LDA. We performed similar tests for some cases of November 2019 (not shown), and the results reveal that \(A=0.95\) (\(B=0.07\)) gives a better precipitation forecast compared to \(A=0.85\) (\(B=0.17\)). Using values of \(A\) greater than 0.95 gives more weight to LDA, but the model becomes computationally unstable for some cases. For these reasons, \(A=0.95\) (\(B=0.07\)) has been used in this study. The coefficients \(C\), \(D\), and \(\alpha\) were set in [PERSON] et al. [29] after careful research. A different setting of these values would require a large number of simulations and is out of the scope of this paper. In this work, WRF was set up with 42 vertical levels, while in a similar study [30], we used 60 vertical levels. Increasing the number of levels would likely lead to a better forecast but would also increase the computational cost. In this work, the number of vertical levels is a compromise between forecast accuracy and computational resources. In addition, to have an idea of the distribution of WRF vertical levels, we note that there are between 7 and 15 vertical levels in the layer \(0/-20\)\({}^{\circ}\)C, depending on the date.
## 2 Materials and Methods
### Long Short-Term Memory Networks  
The cell structure of the LSTM is shown in Figure 3. Three gates are placed in a unit including the input gate, the oblivion gate and the output gate. The gate determines whether the entered information is useful. Only the information that meets the algorithm authentication is left. We use LSTM to dig deep into long-term dependencies and trends in a limited sample of data.  
Figure 2: Schematic diagram of one-dimensional convolution algorithm.  
The updated recursive Equation of LSTM is Equations (8)-(12):  
Input gate:  
\[I_{t}=\sigma(W_{xi}X_{t}+W_{hi}H_{t-1}+b_{i}) \tag{8}\]  
Forget gate:  
\[F_{t}=\sigma\Big{(}W_{xf}X_{t}+W_{hf}h_{t-1}+b_{f}\Big{)} \tag{9}\]  
Output gate:  
\[O_{t}=\sigma(W_{x0}X_{t}+W_{ho}H_{t-1}+b_{o}) \tag{10}\]  
Memory unit structure:  
\[C_{t}=F_{t}\cdot C_{t-1}+I_{t}\cdot\tanh(\,W_{xc}X_{t}+W_{hc}H_{t-1}+b_{c}) \tag{11}\]  
\[H_{t}=O_{t}\cdot\tanh(C_{t}) \tag{12}\]  
in Equations (8)-(12), \(I_{t}\), \(F_{t}\) and \(O_{t}\) represent the gating information of the input, forgetting and output gates, respectively. \"\(\cdot\)\" represents dot product operation, \(W\) is the weight matrix, \(b\) is the bias weight vector and \(\sigma\) stands for _sigmoid_ activation function. \(H_{t}\) gives \(t\) moment output values and the result by tanh nonlinear function gives a value between \(-1\) to \(1\); \(C_{t}\) is the long-term state of the cell, namely, the long-term memory of the LSTM neural network.  
We use LSTM for feature classification to overcome the gradient disappearance and gradient explosion in the training process, and by introducing the attention mechanism of neurons with memory function to learn the joint features of data separated by a long distance, the memory ability of the long-term series is better realized.
# Overall Design of Satellite Networks for Internet Services with QoS Support
## 1 Introduction
To provide Internet services with the QoS support via satellite networks, the overall architecture of the satellite network should be considered practically [1; 25; 26]. Furthermore, it is crucial to evaluate the practical network performance considering control and protocol overheads. It is also important to evaluate the user satisfaction in the application layer for various environments to provide the QoS support. Therefore, in this paper, we consider a widely-used MF-TDMA satellite network with mesh connections [11; 12; 25; 26]. We also design the overall satellite network to provide Internet services with the QoS support practically. In the proposed design, there are two service types such as delay-tolerant and delay-sensitive services allowing the long propagation delay of the satellite link. To evaluate the user satisfaction with services, we define the performance metrics in the application layer and measure the network performance for various environments by varying each factor such as the service type, the traffic load, the burstiness of the traffic, the number of users, and the resource allocation method in the link layer. We then derive major factors to be considered in the QoS policy. The main contributions of our paper are as follows:  
1. Practical overall design of the MF-TDMA satellite network for Internet services with the QoS support. 2. Practical and meaningful performance analysis considering control and protocol overheads. 3. Performance evaluation of the user satisfaction for various environments in the application layer and derivation of major factors to be considered in the QoS policy. The remainder of this paper is organized as follows: Section 2 presents the system model and the proposed overall network design. Section 3 describes a performance evaluation of the proposed overall network design. Finally, the conclusion is presented.
# Dynamic Packet Duplication for Industrial URLLC
## 5 Results and Discussion  
This section shows the results obtained by the test explained in Section 4. The first part of the test consists in obtaining a collection of KPIs in order to train the ML part. This is explained in Section 4.2. Once the ML model has been trained, the second part of the test consists in evaluating the accuracy of the predictor, that is, the accuracy of predicting the latency by MN.  
Moreover, new simulations upon the scenario presented in the previous section have been done, comparing the latency performance and resources consumption between not using PD (single connection), always duplicating the packet and the dynamic packet duplication based on the prediction model (which has been trained before, as explained in Section 4.2), where the duplication is performed when the latency predicted by the model is higher than a threshold. Upon this test, latency threshold of 2 ms was chosen.
## 3 Engineering Application
### Missing Value Imputation  
The missing data rates of the raw dataset \(\Psi\) and the cleaned valid data set \(\Psi^{\prime}\) were 0.243 and 0.292, respectively. The existence of missing values greatly impacts the subsequent evaluation and prediction procedures. The \(H^{\prime}\), \(P^{\prime}\) and \(V^{\prime}\) of the valid data set \(\Psi^{\prime}\) were inputted into the proposed GRUD-WGAN model to fill the missing values. The main parameters are listed in Table 2. The result of data imputation is shown in Figure 11, where imputation values are marked as red points. It can be seen that the amplitudes of imputation values are similar to the actual values nearby. The distribution of the imputation values is also similar to the actual values, as shown in Figure 12. This indicates that the generator successfully learned the distribution of actual data. The complete data set after data imputation was defined as \(\Psi_{C}\). The validity of the proposed GRUD-WGAN and other data imputation methods is further compared and discussed in Section 4.  
Figure 11: The results of data imputation: (**a**) water head \(H\); (**b**) active power \(P\); and (**c**) vibration amplitude of the top cover \(V\).
## 3 Case Study: LCA EoL of SRC and CRC
### Goal and Scope  
The aim of this case study is to present the environmental impact range of EoL (modules C1-C3 in DIN EN 15804) [55] scenarios of SRC and CRC, determined as a future outlook for 50 and 100 years from now [10]. The cut-off method is used as no environmental impacts prior to the demolition are considered [56]. Hotspots and optimization approaches are identified and a comparison with SRC is made. The following scenarios are based on ISO 14040/44 [13,14]. The impact categories used are those in CML2001 (August 2016) and modelled with GaBi ts [57]: abiotic depletion potential (ADP elements [kg Sb eq.], ADP fossil [M]), acidification potential (AP [kg SO\({}_{2}\) eq.]), eutrophication potential (EP [kg phosphate eq.]), freshwater aquatic ecotoxicity potential (FAETP inf. [kg DCB eq.]), global warming potential (GWP [kg CO\({}_{2}\) eq.]), human toxicity potential (HTP inf. [kg DCB eq.]), marine aquatic toxicity potential (MAETP inf. [kg DCB eq.]), ozone depletion potential (ODP, steady state [kg R11 eq.]), photochemical ozone creation potential (POCP [kg ethene eq.]), and terrestrial ecotoxicity potential (TETP inf. [kg DCB eq.]). All mentioned impact categories will be analyzed with a special focus on GWP.  
For comparison reasons, the FU for this case study is defined as the mass of re-usable material of a double wall at its EoL with an equivalent load capacity and function (carbon-reinforced wall: 5 m \(\times\) 2.5 m \(\times\) 0.03 m vs. steel-reinforced wall: 5 m \(\times\) 2.5 m \(\times\) 0.06 m) (Figure 2) (according to [PERSON] and [PERSON], 2019 [58]). The reason for the defined FU is a previous conducted cradle-to-gate LCA [59], which provided the starting point of this study and a precise mass definition given by [PERSON] and [PERSON], 2019 [58].  
The mass differences between a carbon-reinforced double wall and a steel-reinforced double wall are shown in Table 1. The total weight of the steel-reinforced double wall doubles the total weight of the comparative carbon-reinforced double wall. The concrete cover per double wall varies from 1.42 t concrete per CRC to 2.63 t concrete per SRC, representing the (not) needed corrosion protection. Further, the carbon scrim included as reinforcement per double wall has a weight of 10 kg, whereof the steel reinforcement brings 230 kg (Table 1). The double walls are additionally connected to each other in reality using a composite grid. This is shown in Figure 2 as a black dotted line between the two walls. Such a composite grid can, for example, be made of AR glass textile [60] or steel. This composite lattice (connecting pins) was not named in [PERSON] and [PERSON]'s study (2019), and is therefore not included in the current model. Moreover, possible insulation is also not considered in this case study.
## 3 Results and Discussion
### Seasonal Variation of Estimated Rain Heights Using KDE
#### 3.3.3 Rain Height Distribution in Winter  
The peak rain height in Polokwane, Nelspruit, Pretoria, Mahikeng, Upington, and Bloemfontein decreased significantly to about 4500 m, with a probability density of 0.001 m, compared to summer and autumn. The average rain height range at the stations mentioned above is around 3000-5000 m, as shown in Figure 6a-f. The overall rain height distribution is narrower, suggesting less variability and more consistent rain events at lower altitudes. The peaks of the rain heights in Durban, Port Elizabeth, and Cape Town, as depicted in Figure 6g-i, are 4000 m, 4000 m, and 3500 m, with probability densities of 0.008, 0.007, and 0.008 m, respectively. Cape Town has the least of about 1500 m due to the seasonal patterns in weather and the location of the station. This suggests that winter rain events occur significantly in the southwestern coastal regions compared to other seasons. This  
Figure 5: Probability density functions of rain heights during autumn for (**a**) Polokwane, (**b**) Nelpruit, (**c**) Pretoria, (**d**) Mahikeng, (**e**) Upington, (**f**) Bloemfontein, (**g**) Durban, (**h**) Port Elizabeth, and (**i**) Cape Town.  
assumption was corroborated by [PERSON] [40] who inferred that the Western Cape region receives the bulk of its rainfall during the winter months.  
Figure 6: _Cont_.  
Conclusively, the winter season in South Africa shows a marked decrease in both the rain height and the frequency of rain events compared to summer and autumn. Most locations experience rain events at lower altitudes, with more concentrated distributions, suggesting less variability in storm heights. Coastal regions show particularly significant changes, with rain events occurring at much lower altitudes during winter, possibly due to the influence of colder weather systems. Generally, the winter season at most stations indicates small values of rain height and rain events compared to summer and autumn. The distributions are typical for most places where rain events occur and show more aggregated distributions, indicating that storm heights are less variable.
## 3 Proposed CNN + DWFs Methodology and Dataset
### Tool Wear State Classification Based on CNN  
The data set was randomly divided into three subsets, named as the train set, validation set and test set. The CNN network was trained with the train set and validation set. After the training process was completed, the test set that did not involve the training process was used for the test.  
In this paper, data processing, CNN training and tool wear state recognition were all implemented on a single PC. The processor model was Intel Core i7, the CPU memory capacity was 8 GB and the GPU memory capacity was 4 GB.
# A Hybrid PAPR Reduction Method Based on SLM and Multi-Data Block PTS for FBMC/OQAM Systems
## 3 Conventional PAPR Reduction Schemes  
### SLM Scheme  
In the SLM scheme [13], the input data block \(S=[S[0],S[1],\ldots,S[N-1]]\) is multiplied by \(U\) with different phase sequences \(P^{u}=[P^{u}_{0},P^{u}_{1},\ldots P^{u}_{N-1}]^{T}\), where \(U\) is the number of phase sequences, \(P^{u}_{v}=e^{\lvert\vec{v}^{u}_{v}}\) and \(\vec{\varphi}^{u}_{v}\in[0,2\pi)\) for \(v=0,1,\ldots N-1\) and \(u=1,2,\ldots U\), which produce a modified data block \(S^{u}=[S^{u}[1],S^{u}[2],\ldots S^{u}[N-1]]^{T}\). Using IFFT of \(U\) independent sequences \(\{S^{u}[v]\}\) to produce the sequences \(s^{u}=[s^{u}[0],s^{u}[1],\ldots,s^{u}[N-1]]^{T}\), the one \(\vec{s}=s^{\vec{u}}\) with the lowest PAPR is selected for transmission as:  
\[\vec{u}=\underset{u=1,2,\ldots U}{\text{argmin}}(\max_{u=0,1,\ldots,N-1}\lvert x ^{u}[n]\rvert) \tag{4}\]  
The implementation of SLM technique requires \(U\) IFFT operations. When utilizing the SLM scheme to reduce PAPR of FBMC/OQAM signals, the performance is slightly better than the original signal. The PAPR reduction in FBMC/OQAM systems is limited by using the conventional SLM scheme.  
Figure 2: The overlapped signal structure.
## 4 SD-EAR in Detail
### Optimum Route Selection
#### 4.2.1 Intra-Zone Route Discovery with Example  
If \(n_{j}\) belongs to the same zone \(Z_{p}\), then the SDN controller finds all of the routes from \(n_{i}\) to \(n_{j}\) through the depth first traversal of the graph starting from \(n_{i}\). Among all of the available paths, the optimum one is selected based on a fuzzy controller FUZZ-OPT-ROUTE, which is embedded in the controller. This situation is depicted in Figure 8a, while Figure 8b shows the steps with respect to Figure 8a. Without any loss of generality, we consider a polygonal zone.  
Intra-zone communication is loop-free  
The communication inside a zone is completely controlled by the SDN controller of the zone, which is aware of the topology of the entire zone. While discovering the optimum route between two nodes inside a zone, the controller of that zone applies depth-first traversal, which automatically eliminates loops. Therefore, intra-zone communication is loop-free.
## 2 Materials and Methods
### Theoretical Assessment
#### 2.2.6 Nutrient Leach-Out Reduction Potential of Biogas and Compost  
The nutrient leach-out reduction potential of biogas and compost production was estimated by screening the leach-out nutrients obtained in biofertilizer/compost from the total leach-out nutrients present in livestock manure. This study assumed that about 50% of the total generated manure was applied in the agricultural field or left on pastureland. However, most of the studies focus on nutrient loss through runoff, subsurface drainage, and leaching, mainly occurring from cropland due to fertilizer or chemical uses, and nutrient loss is influenced by many factors such as management practices, climate, soil properties, rainfall, and topography [74; 75; 76; 77]. This study used the average value of the referenced range for the N and P content in manure, biofertilizer, and compost (Table 2) and the N and P leach-out factors (Table 6) for calculating nutrient reduction potentials using Equation (11) [78].  
\[\mathrm{NL_{D}=LF_{N}+(I_{i}-RD_{N})} \tag{11}\]  
where \(\mathrm{NL_{D}= nutrient leach-out}\) in each district (kgha\({}^{-1}\))  
\(\mathrm{LF_{N}=leading}\) factor for nutrients (kgha\({}^{-1}\))  
\(\mathrm{I_{i}=intensity}\) for N or P of stock item, i, such as manure of each livestock type, biofertilizer, or compost, was divided by the area of the district (kgha\({}^{-1}\)).  
\(\mathrm{RD_{N}=recommended}\) dose of nutrient applications in the field (kgha\({}^{-1}\)), which it was considered as 30 and 15 kgha\({}^{-1}\) for nitrogen and phosphorus application, respectively, to the field from the farmyard manure source [79].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Emission Factor (kg** & **Type of Fertilizer** & **Country/Region** & **References** \\
**CO\({}_{2 eq}\)/kg Fertilizer** & **Type of Fertilizer** & **Country/Region** & **References** \\ \hline
1.3–1.8 & Ammonium phosphate & Sweden and Western Europe & [66] \\
1.4/1.7 & Europe/Russia, USA & [65] \\ \hline
1 & Single superphosphate & Sweden & [66] \\
0.6 & Single superphosphate & United Kingdom & [67] \\ \hline
0.4–0.54 & & Europe, Russia, USA & [65] \\
1 & Triple superphosphate & Sweden & [66] \\
1.2 & & United Kingdom & [67] \\ \hline
0.14–0.25 & Potassium chloride & China & [68] \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Emission Factor** & **Waste Type** & **Authors** \\
**(kgCO\({}_{2 eq}\)/kg Compost)** & **Waste Type** & **Authors** \\ \hline
0.172–0.186 & Municipal waste & [69] \\
0.18 & Biowaste & [70] \\
0.239 & Household waste & [59] \\
0.145–0.173 & Dairy manure & [71] \\
0.413 & Municipal waste & [60] \\
0.423 & Cattle manure & [61] \\
0.164 & Organic waste & [72] \\
0.381 & Grass and green waste & [62] \\
0.229 & Livestock manure & [25] \\
0.323 & Solid waste & [73] \\ \hline \hline \end{tabular}
\end{table}
Table 5: GHG emission factors for composting waste.
# Multi-Objective Parameter Optimization of Flexible Support System of Optical Mirror
## 4 Experimental Analysis
### Force Output Consistency Experiment  
The consistency of force output is an important performance parameter of FSSs. The difference between the maximum and minimum force output should not exceed 1 N. The force output consistency is the degree to which the optical mirror gravity can be distributed uniformly on each support shaft of the FSS. When the support cylinder is arranged uniformly on the support floor and the pressure of the FSS is equal, the force output consistency is directly affected mainly by the internal resistance of the support cylinder. During the experiment, a load-bearing sensor is installed on the support shaft, and the optical mirror with a diameter of 1280 mm and a weight of 43 Kg is placed on the FSS. Then, the pressure of the FSS is increased to 0.5 MPa, and the output force of each support shaft is measured by reading the feedback value of the sensor.  
As shown in Figure 18, the experiment on the FSS is repeated three times. The black curve is the theoretical output value, and the remaining are the measured force output values. An analysis of the collected data reveals that the difference between the maximum and minimum values of force output in the three experiments are 0.87 N, 0.93 N, and 0.77 N, respectively. These are less than the limit range of 1 N. That is, the designed FSS satisfies the requirements of force output consistency. It can be seen that the designed FSS is suitable for large optical mirror support with a diameter of more than 1000 mm and a total weight of less than 360 Kg.  
Figure 17: Internal resistance measurement experiment.  
Figure 18: Force output consistency experiment.
# Dynamic Spectrum Access for Internet of Things Service in Cognitive Radio-Enabled LPWANs
###### Abstract  
In this paper, we focus on a dynamic spectrum access strategy for Internet of Things (IoT) applications in two types of radio systems: cellular networks and cognitive radio-enabled low power wide area networks (CR-LPWANs). The spectrum channel contention between the licensed cellular networks and the unlicensed CR-LPWANs, which work with them, only takes place within the cellular radio spectrum range. Our aim is to maximize the spectrum capacity for the unlicensed users while ensuring that it never interferes with the licensed network. Therefore, in this paper we propose a dynamic spectrum access strategy for CR-LPWANs operating in both licensed and unlicensed bands. The simulation and the numerical analysis by using a matrix geometric approach for the strategy are presented. Finally, we obtain the blocking probability of the licensed users, the mean dwell time of the unlicensed user, and the total carried traffic and combined service quality for the licensed and unlicensed users. The results show that the proposed strategy can maximize the spectrum capacity for the unlicensed users using IoT applications as well as keep the service quality of the licensed users independent of them.  
IoT; cognitive radio; dynamic spectrum access; matrix geometric solution; mean dwell time; carried traffic +
Footnote †: journal: Sensors