esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In this regard, although the initial values of the level and growth rate are usually estimated by using the least square regression method, the consideration of stationary mean adversely impacts the forecasting accuracy. However, it is computationally difficult and poses an even greater challenge to dynamically estimate and periodically update the values of the level (\(L_{T}\)) by modifying the conventional multiplicative Holt-Winters (CMHW) approach. Hence, in this paper a collaborative forecasting approach is proposed in which the component level (\(L_{T}\)) is estimated by a developed fuzzy-based model and embedded into the CMHW approach. To do so, at the initial stage a simulation study is conducted to determine optimal values of the smoothing constants; whereas, in the second stage, the parametric values of the multiplicative Holt-Winters approach are determined and the parameter level (\(L_{Tpred}\)) is predicted through the incorporation of a developed fuzzy-based prediction model. The estimated level (\(L_{Tpred}\)) is then embedded into the conventional Holt-Winters method for each incremental time step to forecast the demand. The adequacy of our proposed collaborative forecasting approach as well as the level prediction model is assessed by the relevant metrics such as goodness of fit, mean absolute deviation (\(MAD\)) and mean squared error (\(MSE\)). Subsequent sections of this paper are arranged as follows: Section 2 presents a literature review on different demand patterns, time-series characteristics, forecasting approaches and the applications of artificial intelligence (AI) in forecasting. Section 3 highlights the research method, while Section 4 discusses the developmental stages of the fuzzy logic-based level prediction model for seasonal demand forecasting. The application of our proposed approach is described in Section 5. The corresponding results and discussions are provided in Section 6. Finally, in Section 7 relevant concluding remarks are made.
# A New LoRa-like Transceiver Suited for LEO Satellite Communications +
## 4 Results and Discussion  
In this section, we evaluate the performance of our proposed receiver in performing accurate synchronization and decoding of the received DCSS signals. To this end, we perform Monte Carlo-based simulations, with a significant number of repetitions per SNR, using synthesized LoRa-like signals. The simulation results we present are obtained from a DCSS signal simulator that we developed in MATLAB. Thus, we simulated the interleaving and de-interleaving blocks, but also the channel coding/decoding parts, recalled hereafter. Our first test consisted of evaluating the robustness of DCSS technology against the CFO, STO, and the DR. Based on the latter robustness, especially against the significant Doppler variation, we then show the ability of this waveform associated with our original synchronization algorithm to demodulate LEO satellite signals.  
In the following, we consider in all the simulations:  
* A number of preamble up-chirps \(N_{p}=8\);
* A reference bandwidth \(B_{ref}=125\) kHz, which is the most commonly used bandwidth in LoRa-based networks;
* An oversampling factor \(\alpha=8\), which gives a typical sampling frequency in LoRa receivers \(f_{s}^{\prime}=1\) MHz, when the bandwidth of the signal is equal to \(B_{ref}\);
* \(\Delta\tau\) (respectively, \(\Delta f\)) uniformly distributed in \([-\frac{T}{2},\frac{T}{2}]\) (respectively, \([-\Delta f_{max},\Delta f_{max}]\)).
# A Review of Image Reconstruction Algorithms for Diffuse Optical Tomography
## 3 Forward Process
### Diffusion Approximation
Most studies on the optimization process introduced in Section 4 employed DA. Sophisticated open sources for the forward computation of light propagation and image reconstruction, such as TOAST++ [80; 81] and NIRFAST [82; 83], are currently publicly available and have contributed to countless studies on DOT applications and image reconstruction.
# State Feedback Control Based Seamless Switch Control for Microgrid Inverter
## 2 Application Background  
Figure 1 shows the structure of a small-scale MG comprised of a photovoltaic (PV), a battery storage system (BSS), loads and a switch (S) connecting the MG and the grid. PV is connected to AC bus by PV inverter and BSS is connected to AC bus by storage inverter. When S is closed, MG is in the GC state. On this condition, the PV and storage inverters both belong to current-controlled inverters to feed the active and reactive power. When S is open, MG changes into the SA mode. On account of the maximum power point tracker control of PV and the bidirectional operation characteristic of BSS, the storage inverter is the better choice for the voltage support [21]. Hence, the storage inverter should become a voltage-controlled inverter. In this condition, a suitable control for the storage inverter should be designed to guarantee the seamless switch. Furthermore, the power supply of critical loads and connection of PV should also be sustained during the switch transition.
## 5 Ground-Based Calibrations
### Flux Accuracy Calibration
#### 5.2.1 Calibration Methods
The method of obtaining the geometric factor error is to simulate the geometric factor value G\({}_{i}\) of each energy channel with the same input parameters and then obtain the geometric factor error \(\sigma_{G}\) according to the error propagation function.
# Research on Monitoring and Early Warning of the Mine Backfill System Based on Blockchain Technology
## 4 System Experiment and Analysis Evaluation
### Overlimit Warning Experiment
#### 4.3.2 Experimental Results  
Through KingView software, a mine filling simulation system, including a crushing system, batching system, cleaning system, and filling working face, is built to simulate the processes of gangue crushing and screening, filling slurry configuration, slurry transportation and filling, and pipeline inner wall cleaning. The specific construction situation is shown in Figure 7. The process of gangue crushing and screening is shown in Figure 7. The process of filling slurry configuration and transportation is shown in Figure 7. The mine filling simulation system sets the threshold values for the liquid level in water and the additive mixing bin, the material level in gangue and the gangue powder mixing tank, the slurry level in the mixing tank, and the slurry level in the filling slurry storage bin, the mixer current, and the crusher current. Then, we collected all the data generated during the operation of the mine filling system. After preprocessing, the neural network model was input to detect the overlimit data, and the non-generated overlimit situation was predicted to show the results obtained through data training.  
The experimental environment of the overlimit warning model is an Anaconda virtual environment based on TensorFlow. The Python language is used for programming. The number of network layers in the model, the categories and construction order of different layers, the hidden layer parameters, and the learning rate are selected by a random search method. Each batch processes 40 items of data, with each item undergoing 10 training cycles. Among them, 80% of the samples are used as training data sets, and 20% of the samples are used as simulation verification sets to detect the prediction effect of the model. The prediction results of the model are shown in Figure 8. The comparison between the predicted data and the actual data is shown in Table 1. The calculation shows that the mean absolute error of the model is 0.023, the mean square error is 0.001, and the correlation index R2 is 0.98, indicating that the regression effect of the model is good and the predicted results are more accurate.  
Figure 8: Model prediction result.  
Figure 7: Mine filling simulation system: (**a**) Crushing system interface (**b**) Batching system interface.
# Detection for Frying Times of Various Edible Oils Based on Near-Infrared Spectroscopy
[15] studied the effect of frying times on the TPC content in soybean oil and French fries and the composition of TPC. They found that TPC content in soybean oil and French fries both increased with frying times. [PERSON] et al. [16] believed that TPC content in sunflower oil was linearly correlated with frying times. After fried peanut oil intermittently, [PERSON] et al. [17] found that the polar component content in the oil sample continuously increased with frying times, and the growth rate was relatively stable. After frying 15 times, the polar component content was close to 27%, which was the limit stipulated in Hygienic Standard for Edible Vegetable Oils Used in Frying Food (GB7102.1-2003) [18]. [PERSON] et al. [19] investigated the quality change of soybean oil during the frying process, and they found that the acid value, polar compounds, and carbonyl group value were positively and linearly correlated with frying times. All the above studies indicated that TPC content is linearly correlated with frying times, which could be used for indirect evaluation of frying oil. Near-Infrared Spectroscopy (NIRS) technique is a non-destructive, environmentally friendly, and fast detection method that can be utilized in on-line detection [20]. NIRS has been recently applied in the quality assessment of frying oil [21]. [PERSON] et al. [22] proved that NIRS could be used to conduct a quick online quality detection of sunflower oil doped with mineral oil. [PERSON] et al. [23] built a PCA-PLS model based on mid-infrared spectroscopy and NIRS to evaluate the quality of butter oil, and the R-squared is greater than 99%. [PERSON] et al. [24] established a relational model between the NIR spectra of soybean oil and frying times, which could be used to effectively predict frying times, to evaluate the quality of soybean oil, and to achieve good results. Nevertheless, these studies focused on a certain kind of frying oil, making the universality of the method to be improved. Besides, the practicability of the detection methods also needs to be improved. In this study, spectral data of various edible oil samples were collected to build the relational model between the sample spectra and frying times, where experimental samples were prepared during the designed standard experimental processes. The relationship between different frying oil and frying times was investigated to provide a theoretical basis and technical reference for the effective evaluation of frying oil quality and safety.
# The Combined Compact Difference Scheme Applied to Shear-Wave Reverse-Time Migration
## 2 Principle of RTM and Combined Compact Difference Scheme
### Principle of Combined Compact Difference Scheme
\begin{array}{l}f_{i}^{\prime}+a_{11}\big{(}f_{i+1}^{\prime}+f_{i-1}^{ \prime}\big{)}+a_{12}h\Big{(}f_{i+1}^{\prime\prime}-f_{i-1}^{\prime\prime} \Big{)}+a_{13}h^{2}\Big{(}f_{i+1}^{\prime\prime}+f_{i-1}^{\prime\prime}\Big{)} +a_{14}h^{3}\Big{(}f_{i+1}^{(4)}-f_{i-1}^{(4)}\Big{)}\\ =\frac{a_{1}}{h}\big{(}f_{i+1}-f_{i-1}\big{)}+\frac{a_{2}}{h}\big{(}f_{i+2}-f_{ i-2}\big{)}\ldots+\frac{a_{n}}{h}\big{(}f_{i+n}-f_{i-n}\big{)}\\ f_{i}^{\prime\prime}+a_{21}h^{-1}\big{(}f_{i+1}^{\prime}-f_{i-1}^{\prime} \big{)}+a_{22}\Big{(}f_{i+1}^{\prime\prime}+f_{i-1}^{\prime\prime}\Big{)}+a_{ 23}h\Big{(}f_{i+1}^{\prime\prime}-f_{i-1}^{\prime\prime}\Big{)}+a_{24}h^{2} \Big{(}f_{i+1}^{(4)}+f_{i-1}^{(4)}\Big{)}\\ =\frac{b_{1}}{h^{2}}\big{(}f_{i+1}+f_{i-1}-2f_{i}\big{)}+\frac{b_{2}}{h^{2}} \big{(}f_{i+2}+f_{i-2}-2f_{i}\big{)}\ldots+\frac{b_{n}}{h^{2}}\big{(}f_{i+n}+f _{i-n}-2f_{i}\big{)}\\ f_{i}^{\prime\prime\prime}+a_{31}h^{-2}\big{(}f_{i+1}^{\prime}+f_{i-1}^{\prime }\big{)}+a_{32}h^{-1}\Big{(}f_{i+1}^{\prime\prime}-f_{i-1}^{\prime\prime} \Big{)}+a_{33}\Big{(}f_{i+1}^{\prime\prime}+f_{i-1}^{\prime\prime}\Big{)}+a_{ 34}h\Big{(}f_{i+1}^{(4)}-f_{i-1}^{(4)}\Big{)}\\ =\frac{c_{1}}{h^{8}}\big{(}f_{i+1}-f_{i-1}\big{)}+\frac{c_{2}}{h^{8}}\big{(}f_ {i+2}-f_{i-2}\big{)}\ldots+\frac{c_{n}}{h^{8}}\big{(}f_{i+n}-f_{i-n}\big{)}\\ f_{i+1}^{(4)}+a_{41}h^{-3}\big{(}f_{i+1}^{\prime}-f_{i-1}^{\prime}\big{)}+a_{ 42}h^{-2}\Big{(}f_{i+1}^{\prime\prime}+f_{i-1}^{\prime\prime}\Big{)}+a_{43}h^ {-1}\Big{(}f_{i+1}^{\prime\prime}-f_{i-1}^{\prime\prime}\Big{)}+a_{44}\Big{(}f _{i+1}^{(4)}+f_{i-1}^{(4)}\Big{)}\\ =\frac{d_{1}}{h^{4}}\big{(}f_{i+1}+f_{i-1}-2f_{i}\big{)}+\frac{d_{2}}{h^{4}} \big{(}f_{i+2}+f_{i-2}-2f_{i}\big{)}\ldots+\frac{d_{n}}{h^{4}}\big{(}f_{i+n}+f _{i-n}-2f_{i}\big{)}\end{array} \right. \tag{16}\]  
In this paper, we focused on the three-point format of Equation (16). Using the CSCD scheme to calculate the spatial partial derivative of the equation is similar to that of CCD, except for the difference coefficient matrix. The difference coefficient matrix at the left end needs to be expanded, and its sizes are \(4m\times 4m\) and \(4n\times 4n\), respectively. \(E\) and \(F\) are changed to the first-, second-, third-, and fourth-order derivative matrices of the displacement field space to be solved, with the sizes of \(4m\times n\) and \(m\times 4n\), respectively. \(B\) and \(D\) are the difference coefficient matrices at the right end of the equation, and the size is changed to \(4m\times m\) and \(n\times 4n\).
## 2 Literature Review
### Hybrid GA-Based ANN  
ANNs have many advantages in a variety of practical fields and applications; however, a slow rate of convergence and difficulty finding the global optimal solution are the major drawbacks of implementing ANNs. A GA assisted by an ANN can be an effective method of predicting and optimizing any complex process parameters for increasing the success of ANN modeling [47].  
The main advantage of GAs is their ability to avoid becoming trapped in a local optimum [48,49]. GAs contribute to ANN model performance through the network weight initialization. Because ANN training algorithms are based upon local search procedures, they are prone to local minima problems. To avoid this, initial network weights that lead to a global minimum should be introduced, and in a hybrid, GA-based ANN model, the network connection weights and biases are not randomly generated but optimized using the GA [50]. While searching for the global solution, the GA is proposed to function on the trained neural network; the random nature of the GA helps the model to get away from local optima and avoid overfitting. Therefore, the combination of ANN and GA can be used for an integrated process of modeling and optimization.  
The hybrid GA-based ANN technique was recently applied in the field of design. [PERSON] used a fuzzy ANN to establish the relationships between input form design parameters and adjectival image description; they then use a GA to search for a near-optimal design that satisfies the designer's required product image. [PERSON] et al. proposed a parametric approach that uses a three-layered ANN model, incorporated with a GA and the technique of generalized superellipse fitting for product aesthetic design [51]. [PERSON] et al. propose an integrated ANN, GA and the Taguchi quality design process to aid the search for the optimal solution with the most precise design parameters in order to improve product development [52]. The actual processes used to develop a hybrid GA-based model and GA optimization settings are discussed later in this paper.
# A Review on Internet of Things for Defense and Public Safety
## 3 Target Scenarios for Mission-Critical IoT
### Fire-Control Systems  
In fire-control systems, end-to-end deployment of sensor networks and digital analytics enable fully automated responses to real-time threats, and deliver firepower with pinpoint precision. For example, the U.S. Navy's Aegis Combat system provides C2 as well as an unprecedented ballistic missile defense [56]. Munitions can also be networked, allowing smart weapons to track mobile targets or be redirected in flight. Prime examples are the Tomahawk Land Attack Missile (TLAM) and its variants, navy's precision strike standoff weapons for attack of long range, medium range and tactical  
Figure 3: Promising target scenarios for defense and public safety.  
targets [57]. Furthermore, the military has invested in the use of long endurance UAVs to engage high-value targets and introduce multi-UAVs applications [58].
## 3 Results and Discussion
### Energy Diagram of the Plasmonic Trimer  
The energy diagram of the plasmon as a function of vertex angle \(\theta\) is shown in Figure 4. The plasmonic energy is given by \(\hbar\omega\), where \(\hbar\) is the reduced Planck constant. It should be pointed out that the energy of the doubly degenerated E' modes splits into two lines as \(\theta\) increases in both antibonding and bonding groups. In the antibonding group, the mode evolution trenches of the lifted degenerated modes are almost opposite to each other. The antibonding E'-B\({}_{2}\) mode shows first a slightly red shift and then a blue shift, while the antibonding E'-A\({}_{1}\) mode demonstrates a slightly blue shift and then a red shift at high vertex angles. The energy of the two modes is approximately equal when \(\theta\) is \(90^{\circ}\). As \(\theta\) further increases to \(180^{\circ}\), the antibonding E'-B\({}_{2}\) mode transfers to the \(\Sigma_{\rm{u}}\)\({}^{+}\) mode, which is asymmetric with the longitudinal direction and symmetric with the vertical direction, and the path of the E'-B\({}_{2}\)-\(\Sigma_{\rm{u}}\)\({}^{+}\) mode evolution is formed. The antibonding E'-A\({}_{1}\) mode reaches the \(\Pi_{\rm{u}}\) mode, which is symmetric with the \(x\) axis and asymmetric with the \(y\) axis, and the path of the E'-A\({}_{1}\)-\(\Pi_{\rm{u}}\) mode evolution is set up. The antibonding A\({}_{2}\)'-B\({}_{2}\) mode evolution path red shifts monotonously and reaches \(\Pi_{\rm{g}}\) mode. In the bonding group, the doubly degenerated E' mode is lifted as the vertex angle increases. However, unlike the antibonding group, the bonding E'-A\({}_{1}\) mode shows monotonously blue shift and finally reaches the \(\Sigma_{\rm{g}}\)\({}^{+}\) mode, where the optical field distribution is symmetric with both longitudinal and vertical directions when the vertex angle reaches \(180^{\circ}\). Interestingly, the energy of the plasmon of the bonding E'-A\({}_{1}\) path surpasses that of the antibonding A\({}_{2}\)'-B\({}_{2}\) path when the vertex angle is higher than \(120^{\circ}\). On the other hand, the bonding E'-B\({}_{2}\) mode shows monotonous red shift and finally reaches the \(\Sigma_{\rm{u}}\)\({}^{+}\) mode. The lowest mode evolution path bonding A\({}_{1}\)'-A\({}_{1}\) remains stable. In general, the vertex evolution of the scattering extinction cross-section in energy state throughout the range of the vertex angle was studied. Eventually, it turns to the \(\Pi_{\rm{u}}\) mode, which is symmetric in the longitudinal direction and asymmetric in the vertical direction. The spectra are consistent with the energy diagrams shown in Figure 4.
## 3 Results
### Analysis of Energy Band Structure and Characteristics  
The increase in breakdown voltage mainly comes from the influence of different materials on the thickness of depletion layer. Since the voltage at the time of breakdown is much greater than the built-in potential of the heterojunction, the depletion layer width of the heterojunction device is given by the following Formula (1):  
\[W_{heterojunction}=\sqrt{\frac{2\epsilon_{1}\epsilon_{2}(N_{A1}+N_{D2})^{2}V}{ qN_{A1}N_{D2}(\epsilon_{1}N_{A1}+\epsilon_{2}N_{D2})}} \tag{1}\]  
Among them, \(\epsilon_{1}\) and \(\epsilon_{2}\) are the dielectric constants of SnO and Ga\({}_{2}\)O\({}_{3}\), respectively; \(N_{A1}\) and \(N_{D2}\) are the p-type doping concentration of SnO and n-type doping concentration of Ga\({}_{2}\)O\({}_{3}\), respectively; \(V\) is the applied voltage; and \(q\) is the electron charge.  
Similarly, when the applied voltage is much greater than the built-in potential, the Formula (2) for the depletion region width of the PN junction of the same material is given:  
\[W_{pn}=\sqrt{\frac{2\epsilon_{2}V(N_{A1}+N_{D2})}{qN_{A1}N_{D2}}} \tag{2}\]  
Among them, \(\epsilon_{2}\) is the dielectric constant of Ga\({}_{2}\)O\({}_{3}\); \(N_{A1}\) and \(N_{D2}\) are the p-type and n-type doping concentrations of Ga\({}_{2}\)O\({}_{3}\); \(V\) is the applied voltage; and \(q\) is the electron charge.  
To better compare the effects of homogeneous junction and heterojunction on the width of depletion layer, the following comparison Formula (3) is given and simplified:  
\[\frac{W_{heterojunction}}{W_{pn}}=\sqrt{\frac{\frac{N_{A1}+N_{D2}}{\epsilon_{1} }}{N_{A1}+N_{D2}}} \tag{3}\]  
As can be seen from the above formula, \(\epsilon_{2}<\epsilon_{1}\), that is, the dielectric constant of CBL material is greater than that of Ga\({}_{2}\)O\({}_{3}\) material. Under the same applied voltage, the depletion region formed by heterojunction is wider than that of homogeneous junction. A wider depletion layer means being better able to withstand higher voltages and having a greater breakdown voltage of the device. This can be seen from the depletion layer width of the simulation results. As shown in Figure 3c, the dielectric constant of SnO is greater than that of Ga\({}_{2}\)O\({}_{3}\), and the thickness of the depletion layer generated by its heterojunction is thicker than that of the original device. Under the same breakdown field strength, the greater the depletion thickness, the greater the voltage applied to the depletion layer and the higher the breakdown voltage of the device.