esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Optimization of ML Models and Results
### Model Performance
#### 4.2.1 Results of Models for X-PPV Prediction  
To better demonstrate the performance of the first three models to predict the X-PPV, the results of the prediction of the validation set using GWO-GBR, GA-GBR, and PSO-GBR are shown in Figure 8. In addition, the parameters of these models are shown in Table S1.  
As shown in Figure 8a-f, the R\({}^{2}\) of GWO-GBR, GA-GBR, and PSO-GBR were 0.8072, 0.7642, and 0.7488, respectively. The R of GWO-GBR, GA-GBR, and PSO-GBR were 0.9044, 0.8751, and 0.8898, respectively. The RMSE of GWO-GBR, GA-GBR, and PSO-GBR were 0.0959, 0.1061 and 0.1095, respectively. The MAE of GWO-GBR, GA-GBR, and PSO-GBR were 0.0796, 0.0859, and 0.0911, respectively. According to Figures 6a-c and 8b,d,f, the hybrid models had higher R\({}^{2}\) and R and lower RMSE and MAE compared to the standalone models. This indicates that the hybrid model had a higher performance in terms of X-PPV regression prediction than the standalone model. The evaluation indicators R\({}^{2}\) and R of GWO-GBR were higher than the other models, and the RMSE and MAE of GWO-GBR were lower than the other models. This shows that GWO-GBR had the best performance for X-PPV regression.  
As shown in Figure 8b,d,f, the confidence band and prediction band of GWO-GBR, GA-GBR, and PSO-GBR for the X-PPV prediction were similar. This illustrates that the uncertainty performance of X-PPV prediction models was similar [27]. Therefore, the GWO-GBR model had the best performance for X-PPV regression.  
Figure 7: The results of the rank method for X-PPV, Y-PPV, and Z-PPV.
# PM2.5 Concentration Prediction Based on CNN-BiLSTM and Attention Mechanism
## 3 Experiment
### Rating Indicators and Experimental Settings  
Mean absolute error (MAE), root mean square error (RMSE), and \(R^{2}\) are commonly used indicators to measure the accuracy of prediction, and they are also important scales for evaluating models in deep learning. Therefore, the three indicators are used for comparing the performance of the models, and they are defined as Equations (6)-(8).  
\[MAE=\frac{1}{N}\sum_{i=1}^{N}\lvert P_{i}-O_{i}\rvert \tag{6}\]  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\left(P_{i}-O_{i}\right)^{2}} \tag{7}\]  
\[R^{2}=1-\frac{\sum_{i=1}^{N}\left(P_{i}-O_{i}\right)^{2}}{\sum_{i=1}^{N}\left( \overline{O_{i}}-O_{i}\right)^{2}} \tag{8}\]  
where \(N\), \(P_{i}\), and \(O_{i}\) represent the number of a dataset, predicted values, and observed values of PM2.5 concentration, respectively. The smaller value of \(MAE\) and \(RMSE\) means that the smaller error between the predicted value and the observed value of PM2.5 concentration, the higher accuracy of the prediction model. Additionally, the value of \(R^{2}\) stands for the matching degree between these two values, the closer \(R^{2}\) is to 1, the better the prediction performance of the model, and the closer to 0 the lower the prediction accuracy.  
The proposed model is implemented by the computer with AMD Ryzen 7 3800X 8-core Processor CPU, NVIDIA GeForce RTX 2060 SUPER, and 16G running memory, using the TensorFlow neural network framework. The details of the hyperparameter used in the experiment are shown in Table 2. The step size of the convolution kernel is 1, and the Glorot uniform initializer is used to initialize the weights of the neural network. The initial value of the bias unit is set to 0.  
For the time series model, the size of input and output variables are very important parameters. The oversize length of variables will increase the computational complexity, and the undersize length of variables will make the model unable to extract effective features. Owing to the strong periodicity of PM2.5 concentration and other related variables, it is determined that the period is ten days mentioned above. A large number of experiments  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Value** \\ \hline Filter size for CNN & 12 \\ Kernel size for CNN & 64 \\ Padding & same \\ Activation function & relu \\ Unit number for BiLSTM & 4 \\ Dropout rate for BiLSTM & 0.2 \\ Dropout rate for Flatten & 0.5 \\ Neuron number for Attention & 720 \\ Optimization function & Adam \\ Learning-rate & 0.001 \\ Batch size & 150 \\ Epoch number & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameters of CNN-BiLSTM-Attention model.  
are implemented on the CNN-BiLSTM-Attention model to obtain the appropriate size of variables for PM2.5 concentration prediction, and the results are given in Table 3. Different values of MAE and RMSE indicate that when the input size is 24*30 and the output size is 48, the error reaches its lowest, whether it is MAE or RMSE. Therefore, 768 variables in each sample are used as input in the multi-source experiment.
## 5 Neural Networks Approximation in PI Algorithm
### PI Algorithms for Robust Control
```
(1) Decompose the uncertainty properly so that \(\Delta f(x)=c(x)h(x)\) and \(\Delta g(x)=g(x)m(x)\), select constant parameter \(\beta\), \(\beta^{\prime}\) such that \(|\beta^{\prime}|<|\beta|\), and then calculate the nonnegative function \(f_{max}(x)\) and \(g_{max}(x)\) according to (19); (2) For auxiliary system (17), select an initial stabilization control policy \(u_{0}(x)\); (3) Policy evaluation: Give a control policy \(u_{i}(x)\), the cost \(V_{i}(x)\) is solved from the following Bellman equation \[V_{i}[x(t)]=\int_{t}^{t+T}[f_{max}^{2}(x)+g_{max}^{2}(x)+\beta x^{T}x+a_{i}^{ T}(x)a_{i}(x)]dt+V_{i}[x(t+T)];\] (34) (4) Policy improvement: Calculate the control policy using the following update law \[\bar{a}_{i+1}(x)=-\frac{1}{2}\bar{g}^{T}(x)\
abla V_{i};\] (35) Check if the condition \(2v^{*T}(x)v^{*}(x)\leq\beta^{\prime 2}x^{T}x\) is satisfied. Return to step (1) and select the larger constants \(\beta\) and \(\beta^{\prime}\) when it does not hold. ```  
**Algorithm 2** PI algorithm of robust control for nonlinear systems with mismatched uncertainties  
**Remark 3**. : _In Step (3) of Algorithm 1 or Algorithm 2, solving \(V_{i}[x(t)]\) from (30) or (34) can be transformed into a least squares problem [17]. By reading enough data online along the system trajectory, the cost function \(V(x)\) can be calculated by using the least square principle. However, the cost \(V_{i}[x(t)]\) has no specific expressions. In next subsection, along the system trajectory, online reading of sufficient data on the interval \([t,t+T]\), the cost \(V_{i}[x(t)]\) can be approximated by neural network in PI algorithms. Moreover, implementation of the algorithm does not need to know the system dynamics function \(f(x)\)._
# On the Semantics of Hybrid ASP Systems Based on Clingo
## 5 Answer Set Solving Modulo Linear Equations
### clingo[LP]  
A third AMT system covered by our formalization is clingo[LP]. In this case, the abstract theory \(\mathfrak{R}\) is about linear equations over reals. It is identical to \(\mathfrak{L}\) but structured by \((\mathcal{X}_{\mathfrak{R}},\mathcal{D}_{\mathfrak{R}},\mathit{vars}_{\mathfrak{L }},[\,\cdot\,]_{\mathfrak{L}})\) where \(\mathcal{X}_{\mathfrak{R}}\) is an infinite set of real variables and the domain \(\mathcal{D}_{\mathfrak{R}}\) is the set of real numbers \(\mathbb{R}\). Previous versions of clingo[LP] treated either all theory atoms as external, \(\mathcal{E}=\mathcal{T}\), or founded, \(\mathcal{E}=\emptyset\). Since it imposes no restriction on the occurrence of linear equation atoms; the counter-intuitive behavior identified in [14] may emerge. The current version behaves like clingo[DL] and treats body atoms as external and head atoms as founded. Another commonality with clingo[DL] is a selection among the \((\mathfrak{R},\mathcal{E})\)-answer sets that are printed. In the case of clingo[LP], the selection is configurable by the user via a directive with a linear term:  
\[\delta\texttt{minimize}\{w_{1}*x_{1};...;w_{n}*x_{1}\}\]  
where \(\{w_{1},\ldots,w_{n}\}\subseteq\mathbb{R}\) and \(\{x_{1},\ldots,x_{n}\}\subseteq\mathcal{X}_{\mathfrak{R}}\). Then, the partial order \(\leq_{\texttt{1p}}\) between two \(\langle\mathfrak{R},\mathcal{E}\rangle\)-answer sets \((Y,v)\) and \((Y^{\prime},v^{\prime})\) is defined as:  
\[(Y,v)\leq_{\texttt{1p}}(Y^{\prime},v^{\prime})\text{ iff }\] \[Y=Y^{\prime}\text{ and }\text{domain}(v)=\text{domain}(v^{ \prime})\text{ and }\] \[w_{1}*v(x_{1})+\cdots+w_{n}*v(x_{n})\leq w_{1}*v^{\prime}(x_{1}) +\cdots+w_{n}*v^{\prime}(x_{n})\]  
Similarly, we write \((Y,v)<_{\texttt{1p}}(Y^{\prime},v^{\prime})\) when \((Y,v)\leq_{\texttt{1p}}(Y^{\prime},v^{\prime})\) and \(v\
eq v^{\prime}\).  
**Definition 7** (clingo[LP]-answer set).: _A \(\langle\mathfrak{R},\mathcal{E}\rangle\)-answer set \((Y,v)\) is called a clingo[LP]-answer set of \(P\) if \((Y,v)\) is \(<_{\texttt{1p}}\)-minimal among the \(\langle\mathfrak{R},\mathcal{E}\rangle\)-answer sets of \(P\)._  
If no minimize directive is provided, the linear term \(x_{1}+\cdots+x_{n}\) with \(\{x_{1},\ldots,x_{n}\}=\mathcal{X}_{\mathfrak{R}}\) is assumed by default. That is, the answer set with the smallest sum of variables is selected. For instance, given the program \(\delta\texttt{sum}\{x\}>=0\), clingo[LP] only outputs the answer set where \(x=0\).
# Data Conversion Strategies for Effective Aviation Technical Support as a Service
## 4 Discussion  
### Numerical Example for Life Cycle Cost-Based Decision-Making Model  
This section presents an example of how to analyze and compare the life cycle costs of centralized and decentralized architectures using specific input data. The analysis demonstrates the practical application of the mathematical models discussed earlier and illustrates the decision-making process based on operational costs, initial investments, and scalability requirements. By simulating real-world scenarios with predefined parameters, the example provides a clear understanding of how the two architectures perform over time and under varying conditions.  
The calculation for this example is based on the model presented in Section 3.2, using the following input data: \(C_{init}^{(1)}=1,200,000\) USD; \(C_{init}^{(2)}=800,000\) USD; \(\alpha_{1}=70,000\) USD/year; \(\alpha_{2}=120,000\) USD/year; \(\beta=0.05\); \(\gamma=20,000\) USD/log-unit; \(S=500\), e.g., users or transactions.  
The Figure 5 illustrates the cumulative costs of centralized and decentralized system architectures over a 20-year period, providing insights into the decision-making process for choosing between the two.  
The Figure 5 compares the total costs over time between centralized and decentralized architectural approaches. The break-even point occurs at approximately 6 years. Initially, the decentralized architecture has a lower total cost due to its smaller initial investment. However, due to the exponential growth of operational costs in the decentralized approach, it eventually becomes more expensive than the centralized solution. By year 20, there is a significant cost difference favoring the centralized approach due to the exponential growth of decentralized operational costs.  
Scalability plays a critical role in this decision. As scalability requirements grow, the decentralized system becomes increasingly inefficient due to its exponential cost growth. In  
Figure 5: Total cost comparison of centralized vs. decentralized architectures.  
contrast, the centralized system handles scalability more predictably, making it the better option for operations expected to scale significantly.
###### Abstract  
The next-generation satellite Internet Protocol (IP) router is required to achieve tens of millions of route lookups per second, since satellite Internet services based on low Earth orbit (LEO) constellations have become a reality. Due to the limitation of hardware resources on satellites and the high reliability requirements for equipment, a new satellite IP route lookup architecture is proposed in this paper. The proposed architecture uses a Bloom and cuckoo filter-based structure called cuckoo Bloom hybrid filter (CBHF), which guarantees only one off-chip memory access per lookup, to accelerate the Prefix-Route Trie (PR-Trie) algorithm. The proposed architecture has been evaluated through both a behavioral simulation in C++ language and a hardware implementation in Verilog hardware description language (HDL). Our simulation and implementation results show that the proposed satellite IP route lookup architecture can achieve a single-port throughput beyond 13 Gbps on a field programmable gate array (FPGA) board with a single DDR3 memory chip when operating at 200 MHz. In addition, the resource utilization in the FPGA shows that the proposed architecture also supports triple modular redundancy (TMR) to enhance reliability.  
satellite IP route lookup; Bloom filter; cuckoo filter; CBHF; PR-Trie; FPGA Article
# Relational Positioning Method for 2D and 3D Ad Hoc Sensor Networks in Industry 4.0
## 3 Relational Positioning Method  
In the current positioning system, we propose to determine the location of a node \(n_{0}\) in a given area that is assumed to be a subset of a region \(\mathbf{R}\), defined by the convex hull contained by a number of ANs. Although our main concern is with 3D localization, for explanatory purposes, we initially consider a planar 2D scenario in which we discretize the region according to an immaterial regular square lattice with a basic separation unit \(L\). We also assume that the locations of ANs and the node to be located coincide with some vertices \(\mathbf{v}\) in the lattice. In the absence of further information, the uncertainty of the location of the node equates to \(\log(N_{c})\), where \(N_{c}\) denotes the number of cross points in the lattice, and the node of interest is considered to be equally likely to be located at any of those cross points of the lattice. The proposed method allows us to reduce the location uncertainty through likelihood proximity estimates.
###### Abstract  
This research is devoted to the microstructure evolution and deformation behavior of the Al-1.2 Mg-0.7 Si-1.0 Cu-0.1 Sc-0.2 Zr alloy during the isothermal multidirectional forging (MDF) in a large cumulative strain and temperature range. The structure investigation of the studied alloy revealed several phases precipitated during solidification, among which (\(\Delta\)(Li\({}_{2}\)Cu), \(\Delta\)(Li\({}_{5}\)Cu\({}_{2}\)Mg\({}_{8}\)Si\({}_{6}\)), Mg\({}_{2}\)Si, Sc-bearing W(AlScu) and V(AlSi\({}_{5}\)Sc\({}_{2}\)) phases were observed. The MDF at 150-350 \({}^{\circ}\)C and a maximum cumulative strain of 14.4 significantly refined grain structure providing a mean grain size of 1.2-2.1 \(\mu\)m. The Li\({}_{2}\) structured Al\({}_{3}\)(Sc,Zr) disregosdids with a mean size of 10 \(\pm\) 1 nm were formed during two-step homogenization annealing. Due to Zener pinning of the nanoscale dispersions and fine-grained structure, the alloy exhibited near-superplastic behavior in a temperature range of 460-500 \({}^{\circ}\)C and strain rate range of 2 \(\times\) 10\({}^{-3}\)-1 \(\times\) 10\({}^{-2}\) s\({}^{-1}\) with the maximum elongation to failure of \(\sim\)300%. After a strengthening heat treatment, the forged alloy exhibited the yield strength of 326 \(\pm\) 5 MPa, ultimate tensile strength of 366 \(\pm\) 5 MPa, and elongation of 10 \(\pm\) 3%. The hot deformation behavior was described using the Arrhenius type model. The developed model demonstrated high predictability accuracy with a maximum average absolute relative error of 6.6%.  
aluminum alloy; multidirectional forging; grain refinement; microstructure evolution; mechanical properties; mathematical modelling
# Quantum Recurrent Neural Networks: Predicting the Dynamics of Oscillatory and Chaotic Systems
## 3 Methodology
### Quantum Long Short-Term Memory (QLSTM)  
Quantum Long Short-Term Memory (QLSTM) is a quantum-enhanced version of the traditional LSTM networks. QLSTM integrates VQCs into the LSTM architecture, aiming to leverage the computational advantages of quantum mechanics. The structure of a single QLSTM unit is shown in Figure 4.  
In a QLSTM, two key memory components are present: the hidden state \(h_{t}\) and the cell or internal state \(c_{t}\). The functioning of a QLSTM cell can be mathematically described by the following equations:  
\[f_{t} =\sigma(\text{VQC1}(v_{t})), \tag{26}\] \[i_{t} =\sigma(\text{VQC2}(v_{t})),\] (27) \[\tilde{c}_{t} =\tanh(\text{VQC3}(v_{t})),\] (28) \[c_{t} =f_{t}*c_{t-1}+i_{t}*\tilde{C}_{t},\] (29) \[o_{t} =\sigma(\text{VQC4}(v_{t})),\] (30) \[h_{t} =\text{VQC5}(o_{t}*\tanh(c_{t})),\] (31) \[\tilde{y}_{t} =\text{VQC6}(o_{t}*\tanh(c_{t})),\] (32) \[y_{t} =\text{NN}(\tilde{y}_{t}). \tag{33}\]  
In Equations (26)-(33), \(\sigma\) represents the sigmoid function, and \(*\) denotes element-wise multiplication. The input to the QLSTM cell at each time step is the concatenation \(v_{t}\) of the  
Figure 4: Structure of a single unit of QLSTM.  
previous hidden state \(h_{t-1}\) and the current input vector \(x_{t}\). The VQCs mentioned in the equations refer to Variational Quantum Circuits.
## 2 Materials and Methods
### Data Sets of Gross Primary Productivity
#### 2.2.3 FluxSat-GPP  
The third data set of monthly global GPP (0.05\({}^{\circ}\)) was derived based on the MODIS MCD43C4v006 Nadir Bidirectional Reflectance Distribution Function-Adjusted Reflectance (NBAR) product, SIF, PAR, plant and soil classification data, FLUXNET GPP, meteorological and hydrological fields. These data sets were used as input to a neural-network model to estimate GPP on a global scale [25].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name** & **Spatial** & **Temporal** & **Time Span** & **Data Source** \\ \hline GOSIF-GPP & 0.05\({}^{\circ}\) & monthly & 2001–2018 & [30] \\ NIRv-GPP & 0.05\({}^{\circ}\) & monthly & 2001–2018 & [24] \\ FluxSat-GPP & 0.05\({}^{\circ}\) & monthly & 2001–2018 & [25] \\ MODIS land cover (MCD12C1 v006) & 0.05\({}^{\circ}\) & yearly & 2011 & [26] \\ the Köppen–Geiger climate map & 1 km & static & 2007 & [27; 28; 32] \\ ERA5-land (air temperature, soil moisture and VPD) & 0.1\({}^{\circ}\) & monthly & 2001–2018 & [33] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main data sets used in the study. Bilinear interpolation was used to convert the ERA5 land climate data from 0.1\({}^{\circ}\) to 0.05\({}^{\circ}\). The time-span of the analysis (2001–2018) was confined by the availability of the GPP-data products.
# Void Avoiding Opportunistic Routing Protocols for Underwater Wireless Sensor Networks: A Survey
## 2 Routing Protocols
### Opportunistic Routing
#### 2.3.2 OR Classification  
The existing OR protocols in UWSNs can be classified based on their positioning information into two main classifications: geography-based and pressure-based. In the first category (geographic-based), selecting the forwarding set candidates and making the forwarding packet decisions in OR requires information about the geographic location of sensor nodes. While in the pressure-based category the depth information of nodes is needed to select the next forwarding set candidates and make the forwarding packets decisions. This classification with the state-of-the-art reviewed protocols can be seen in Figure 4.  
Figure 4: Classification of OR protocols for UWSNs based on position information.
# An Adaptive Neuro-Fuzzy Propagation Model for LoRaWAN
## 2 Brief Review of Common Models
### [PERSON], [PERSON] and Ray Tracing Models  
[PERSON] and [PERSON] [5] proposed a semi-deterministic model that added new modeling parameters into [PERSON]'s model. These parameters were added to account for the multiple-screen diffraction, caused by the buildings and structures. [PERSON] et al. [6] took a new approach by using a simplified ray-tracing method. They assumed multiple-reflected/diffracted waves were highly attenuated and, therefore, only accounted for first-reflected/diffracted rays. The other compromise of this deterministic model was perhaps due to the limited computational power, since the reflection/diffraction losses were approximated with constant values. There are several 2D and 3D ray-tracing algorithms, based on the geometrical optics, uniform theory of diffraction and geometrical theory of diffraction. These models have complemented [PERSON]'s efforts. For instance, it is possible to consider two- and three-fold reflections/diffraction, and reflection/diffraction factors depend on the angle of incidence and permittivity of materials. Further detailed summaries of these models with their implementations are provided in [7; 8; 9; 10]. Nevertheless, there are the following drawbacks to these deterministic models:Having precise building data is a prerequisite, meaning that, ideally, a 3D topographical model of the environment and structures is required [6; 11]. However, such a database may not be readily available, especially for outdoor environments.
* Computational complexity of the ray tracing is extremely cumbersome even in a relatively small indoor environment and the accuracy of the model depends on the accuracy of the structural model [12]. Therefore, it is not really a practical solution for outdoor large-scale modeling.
* Finally, ray tracing incorporates the \"small-scale\" fading which varies with the order of wavelength [13], and small-scale fading is not predictable for ranges beyond half-wavelengths [14]. These drawbacks are even more aggravated when the radio frequency is increased.
## 2 Materials and Methods
### Architecture Design and Implementation
#### 2.1.1 Hardware Design  
The hardware design of the quality perception system included a sensor node and sink node two modules. The physical photo of each module is shown in Figure 2.  
The main control chip of the hardware system was the STM32F103 RET6 chip. As for the presented sensor node, the temperature and humidity sensor, CO2 and O2 sensors adopt HDC1080, JX-CO2-102, and JXM-O2, respectively. As for the presented sink node, the 4G transport module adopted USR-LTE-7S4, which could work for 4G communication all over the internet. The Global Positioning System (GPS) module adopted the ATK-S1216F8-BD GPS/Beidou module centered around S1216F8-BD and adopted NMEA-0183 as communications protocols for data acquisition, communicating with the main control chip in a Universal Asynchronous Receiver/Transmitter (UART). The LoRa information transmission in the system was realized by the atk-LORA-01 wireless serial communication module.