esa-sceva/satcom-chunk-collection huggingface dataset:
# A Predictive Method for Temperature Based on Ensemble EMD with Linear Regression
## 1 Introduction
They effectively capture complex relationships and pattern changes within time series through self-attention mechanisms, which makes them suitable for applications such as stock market prediction and climate change modeling. Various improved models, such as GPHT, ShapeFormer, TFT, and FLUID-GPT [30], have been proposed to enhance the forecasting performance and applicability of existing models. These models can handle long sequence data, capture long-range dependencies, and provide interpretable insights. However, transformer models can be computationally intensive when dealing with time series and require substantial amounts of data, so considerations of computational resources and data availability are necessary in practical applications. Reference [29] proposes using Transformer models for predicting dynamic systems that represent physical phenomena. By employing Koopman-based embeddings, dynamic systems are projected into vector representations, which are then predicted by the Transformer model. The proposed approach outperforms traditional methods that are commonly used in scientific machine learning, which demonstrates its effectiveness. Reference [30] introduces the FLUID-GPT model for predicting particle trajectories and erosion in industrial-scale steam pipelines. FLUID-GPT combines GPT-2 and CNN, requiring only five initial conditionsfor accurate erosion prediction. Compared to BiLSTM, FLUID-GPT exhibits significant improvements in both its accuracy and efficiency. Although current temperature prediction methods have achieved certain success in various scenarios and time scales, there are still some challenges. The complexity and uncertainty of temperature prediction make forecasting difficult, and the mismatch between different scales limits the applicability of the existing models. This paper combines multiple methods for temperature prediction and has achieved good results in experiments. The remainder of this document is structured as outlined below: A brief overview of relevant concepts, terminologies, and the EEMD and LR techniques is provided in Section 2. Following that, Section 3 showcases the combined approaches for short-term temperature prediction using ensemble EMD with LR. Moving on to Section 4, we focus on the introduction of the experimental data utilized in this research. Section 5 details the experimentation through simulation and the subsequent empirical analysis of the proposed methodologies. The concluding section summarizes key findings and highlights potential future endeavors.
## 3 Results and Discussion
### Air Inlet Velocities  
The effect of various air inlet velocities on ballistic trajectory was observed by computing the velocity magnitude at point 1 in the +4\(D\) separator model (Figure 9a). An increase in air inlet velocity also increases the kinetic energy of the ballistic trajectory, which then improves plastic separation, and vice versa. However, increasing the inlet velocity also increases shear stress, thus applying more friction to the ballistic trajectory. Shear stress is directly related to inlet fluid velocity and increases with flow velocity [30]. Shear stress increases with an increase in air inlet velocity for all duct shape models (Figure 9b). The triangular duct hole with 1.5\(D\) showed the highest value of shear stress at different inlet velocities. Similarly, the vorticity magnitude increased with an increase in air inlet velocity (Figure 9c). Again, the triangular duct hole with 1.5\(D\) showed the highest shear stress value at different inlet velocities. These results suggest that increasing the air inlet velocity improves the kinetic energy of the ballistic trajectory but increases fluid friction and turbulence in the separator model. In addition, the triangular duct hole with 1.5\(D\) showed better results with the increase in the air inlet velocity as compared with other duct shapes. Therefore, a suitable air inlet velocity must be selected to increase the separation of PET and PVC materials without increasing the fluid friction in the separator model [30].
# Enhanced Teaching-Learning-Based Optimization Algorithm for the Mobile Robot Path Planning Problem
## 3 TLBO and DE Algorithms
### Tlbo  
TLBO simulates the teaching-learning phenomenon in a classroom to solve complicated optimization problems with appreciable efficiency. It is a swarm-based evolutionary algorithm that utilizes a group of so-called individuals to produce a global optimum. In TLBO, the current best solution is considered to be the teacher individual, and the other solutions are treated as student individuals. A group of solutions are created in a random way. They then evolve by two fundamental modes of learning: namely, the teacher phase and the learner phase. The classical TLBO is executed through the following steps:  
_Step 1._ Initialize the parameter settings, including the population size, \(P\), maximum iteration, \(G\), problem dimension, \(D\), etc.;  
_Step 2._ Create initial individuals then calculate their objective values;  
_Step 3._ Employ the teacher phase to update the current population;  
_Step 4._ Employ the learner phase to update the current population;  
_Step 5._ If any algorithm termination criterion is satisfied, terminate the search process and output the current best individual; otherwise, go to Step 3.  
Detailed mathematical formulations of TLBO are stated as follows.  
1. Initial phase The solution in TLBO is expressed by an array, \(x=(x_{1},x_{2},\cdots,x_{d},\cdots,x_{D})\), in which \(d\in\{1,2,\cdots,D\}\). Given a variable range, \([x_{d}^{l},x_{d}^{u}]\), \(P\) random individuals are initialized by the following equation: \[x_{d}=x_{d}^{l}+rand(0,1)\cdot\left(x_{d}^{u}-x_{d}^{l}\right)\] (3) where \(rand(0,1)\) is a uniform random number in the interval [0, 1].
2. Teacher phase The mean position of the current population is calculated. Let \(\overline{x}=(\overline{x}_{1},\cdots,\overline{x}_{d},\cdots,\overline{x}_{D})\) be the mean solution, and \(x^{*}\) represents the current best solution. The teacher phase is implemented based on the position difference between \(\overline{x}\) and \(x^{*}\). In this regard, the equation is stated as follows: \[x^{\prime}=x+rand(0,1)\cdot(x^{*}-\partial\cdot\overline{x})\] (4) where parameter \(\partial\) is the teaching factor that lies in the set [1, 2]. If the mutant individual performs better than its parent, a greedy selection is then introduced, and \(x\) would be replaced by \(x^{\prime}\).  
(3) Learner phase  
The learner phase is implemented based on the position difference between the current solution and one randomly selected individual. Let \(f(\cdot)\) represent the objective function formula. The offspring is produced as follows:  
\[x^{\prime}=\begin{cases}x+rand(0,1)\cdot(x^{1}-x)&\text{if }f(x^{1})<f(x^{2})\\ x+rand(0,1)\cdot(x^{2}-x)&\text{otherwise}\end{cases} \tag{5}\]  
where \(x^{1}\) and \(x^{2}\) denote two randomly selected individuals. In addition, the greedy selection is applied in an analogous way to the teacher phase.
# Electromagnetic Radiation Shielding Using Carbon Nanotube and Nanoparticle Composites
## 2 Overview of Shielding Approaches for Different Types of Radiation
### Design of Radiation-Shielding Materials  
Radiation shielding design depends on the frequency of the radiation [5; 13; 29]. Generally, two types of shielding are considered, lower-frequency electromagnetic non-ionizing radiation and higher-frequency ionizing radiation. Carbon nanotube hybrid fabric with nanoparticles can be designed for shielding each type of radiation. Electromagnetic waves in microwave radiation are considered first.  
When microwave radiation contacts an electrically conductive shielding material, the radiation can be reflected, absorbed, or attenuated as illustrated in Figure 2. Reflection occurs when most of the wave energy is initially reflected at the interface between the air and the shielding material depending on the material's conductivity and impedance mismatch. Microwave radiation can propagate within the absorber, undergo multiple internal reflections, and be completely absorbed. Free electrons in the conductive shielding material can interact with the electric field of the wave, partly blocking its propagation beyond a shallow depth called the skin depth. The wave energy is absorbed by the material through the generation of eddy currents, converting the electromagnetic energy into heat. Eddy currents create their own magnetic fields, which can oppose the original magnetic field, thereby reducing the overall electromagnetic interference. They also lead to energy losses due to heating the shielding material.  
Radiation of the second type includes X-ray and gamma rays, which are photons having wavelengths on the atomic scale [13]. The atomic cross-section of the atom defines  
Figure 2: Illustration of shielding radiation in CNT-nanoparticle composites. Electromagnetic shielding is shown with an incoming electromagnetic wave encountering an electrically conductive material. The wave may be partly reflected due to the sudden impedance change at the boundary. The wave may also penetrate the material and be attenuated by induced currents and resistive heating within the conductor. Multiple reflections may also occur at the upper and lower surfaces of the material. Part of the wave may also be transmitted through the shield. Typically, a mm-thick sheet of electrically conductive material can shield microwaves and radio waves.  
the probability of photon interaction, as per the processes shown in Figure 1. A high electron density material is needed to shield gamma rays.
## 3 Mathematical Model Formulation  
In this section, the fractional Bagley-Torvik equation (FBTE) is formulated subject to suitable initial conditions to construct the RPS solution utilizing the RPS algorithm based on the truncated residual error function. Consider the FBTEs described in Equations (1) and (2) at \(\alpha=3/2\); thus, to achieve our goal in applying the FRPS method, let us first convert the FBTE, \(\omega^{\prime\prime}(t)+bD_{0^{+}}^{3/2}\omega(t)+c\omega(t)=\varphi(t)\), into an equivalent system of fractional-order \(\alpha=1/2\) by setting \(\omega(t)=x_{1}(t)\), \(D_{0^{+}}^{1/2}\omega(t)=D_{0^{+}}^{1/2}x_{1}(t)=x_{2}(t)\), \(D_{0^{+}}\omega(t)=D_{0^{+}}^{1/2}D_{0^{+}}^{1/2}x_{1}(t)=D_{0^{+}}^{1/2}x_{1} (t)=D_{0^{+}}^{1/2}D_{0^{+}}x_{1}(t)=D_{subject to the nonhomogeneous initial conditions  
\[x_{1}(0)=\mu_{0},\;x_{2}(0)=0,\;x_{3}(0)=\mu_{1},x_{4}(0)=0. \tag{9}\]  
According the FRPS method [24; 25; 26; 27], let us assume that the solutions of IVPs (8) and (9) can be written by  
\[x_{i}(t)=\sum_{k=0}^{\infty}a_{ik}\frac{t^{kx}}{\Gamma(k\alpha+1)},0<\alpha \leq\frac{1}{2},i=1,2,3,4, \tag{10}\]  
where the truncated series solution of \(x_{i}(t)\) is  
\[x_{i}^{n}(t)=\sum_{k=0}^{n}a_{ik}\frac{t^{kx}}{\Gamma(k\alpha+1)}. \tag{11}\]  
Now, the residual functions can be defined by  
\[ \begin{array}{l}\mathit{Resx}_{i}(t)=D_{0^{+}}^{\alpha}x_{i}(t)-x_{i+1}(t),i= 1,2,3,\\ \mathit{Resx}_{4}(t)=D_{0^{+}}^{\alpha}x_{4}(t)+bx_{4}(t)+cx_{1}(t)-\varphi(t ),\end{array} \]  
and the \(n\)th-truncated residual functions by  
\[ \begin{array}{l}\mathit{Resx}_{i}^{n}(t)=D_{0^{+}}^{\alpha}x_{i}^{n}(t)-x_ {i+1}^{n}(t),i=1,2,3,\\ \mathit{Resx}_{4}^{n}(t)=D_{0^{+}}^{\alpha}x_{4}^{n}(t)+bx_{4}^{n}(t)+cx_{1}^ {n}(t)-\varphi(t),\end{array}  \tag{12}\]  
whereas \(\mathit{Resx}_{i}(t)=\lim\limits_{n\to\infty}\mathit{Resx}_{i}^{n}(t)=0\), \(i=1,2,3,4\), and \(D_{0^{+}}^{\mathit{k}\alpha}\mathit{Resx}_{i}(0)=D_{0^{+}}^{\mathit{k}\alpha} \mathit{Resx}_{i}^{n}(0)=0\) for each \(k=1,2,\ldots,n\). However, \(\mathcal{D}_{0^{+}}^{(n-1)\alpha}\mathit{Resx}_{i}^{n}(t)_{t=0}=0,\;n=1,2, \ldots,i=1,2,3,4\). The following algorithm shows us the FRPS strategy to determine the coefficients \(a_{ik},i=1,2,3,4,k=1,2,\ldots,n\), of Equation (11) in order to predict and obtain the RPS solution of FBTEs (1) and (2).
## 3 Proposed TH-CPSK-CEMC System
### Multiuser Interference  
In the depicted scenario (cf., Figure 1), multiple TNs are distributed randomly within a short-range environment. The transmitted molecules, when reaching the RN, may lead to multiuser interference. This interference arises when molecules emitted by different TNs arrive at the RN simultaneously and collide. To model this scenario realistically, our study assumes that the distances between each TN and the RN vary randomly, ranging from \(1\,\mathrm{cm}\) to \(10\,\mathrm{cm}\). This approach helps simulate a diverse set of conditions reflective of potential real-world applications, where the spatial arrangement of TNs relative to an RN can significantly impact communication performance due to the stochastic nature of molecular diffusion and the potential for multiuser interference.  
In our study, to comprehensively understand the dynamics of MC systems within a multiuser context, we focus on modeling the system based on the perspective of a specific user (for example, User 1). The spatial arrangement and relative distances between users play a crucial role in delineating the interference patterns observed within the system. Users positioned closer to the receiver than User 1 have a higher likelihood of their signals arriving earlier, thereby elevating the risk of collision with User 1's transmitted signal. Conversely, users situated further away than User 1 tend to have their signals reach the receiver later, potentially leading to interference with signals transmitted after User 1's signal. Consequently, the variations in distances between users intricately influence the temporal distribution of signal reception. In light of this, the received signal for User 1, denoted as \(U^{1}(t)\), encapsulates the interaction of these factors and can be expressed as follows:  
\[U^{1}(t)=\Big{(}Q^{1}(t)*C(\tau_{1},t)\Big{)}+\sum_{i=2}^{N}\Big{(}Q^{i}(t)*C( \tau_{i},t)\Big{)}, \tag{3}\]  
where the first term corresponds to the signal received from User 1, convolved with the channel's impulse response, \(C(t)\), and the second term aggregates the interference caused by all other users, from 2 to \(N\). The symbol \(*\) denotes the convolution operation. Equation (3) can be expanded into a more detailed representation, as shown in Equation (4), accounting for the diffusion dynamics, as follows:  
\[U^{1}(t)=\int_{0}^{t}\Bigg{(}\frac{Q^{1}(\tau_{1})}{\left(4\pi D(t-\tau_{1}) \right)^{3/2}}\cdot e^{-\frac{\tau_{1}^{2}}{4D(t-1)}}\Bigg{)}d\tau_{1}+\sum_{i= 2}^{N}\int_{0}^{t}\Bigg{(}\frac{Q^{i}(\tau_{i})}{(4\pi D(t-\tau_{i}))^{3/2}} \cdot e^{-\frac{\tau_{1}^{2}}{4D(t-\tau_{1})}}\Bigg{)}d\tau_{i}, \tag{4}\]  
where \(\tau_{i}\) represents the propagation delay of the \(i\)th TN signal and \(D\) is considered constant throughout the observation period.  
Figure 5: Channel quantum response for the three distances simulated in this paper.
# Experimental Investigation of the Fatigue Life of a Bridge Crane Girder Using S-N Method
## 5 Conclusions  
Based on the experimental measurements evaluation, analysis of the obtained results, visual inspection, and experimental-computational verification of the possible accumulation of fatigue damage of the crane, it could be concluded that the girders of the bridge crane were satisfactory for further operation. However, maintenance of the motion gear and the rail connection to the girders was required. By analyzing the results of the strain gauge measurements, the influence of the height course of the crab's trajectory (comparison of different stress levels at locations A1/A2 and B3/B4, respectively) in combination with the attachment of the rails to the girders on the stress state at the analyzed locationswas confirmed. For this reason, the authors recommended checking the rail to girder attachment or replacing welded joints with clamps. Based on the analysis of the strain gauge measurements and more than 50 years of continuous operation, we recommended checking the welds of the steel structure of the bridge crane girders along their entire lengths and the state of the crane's track.  
In the analyzed case, the fatigue life of the bridge crane girders was not output mainly because the crane had worked at a low number of operating cycles, i.e., 18 cycles per 24 h. The duration of one cycle is about 5 min, i.e., the total operating time for one working day is about 1.5 h. In the case of the same operating load but with a higher number of cycles (e.g., a trans-shipment station), the effect of the detected welding of the rail to the girder would very likely lead to fatigue damage and consequently to a failure of the bridge crane.  
Conceptualization, M.P. and J.K.; methodology, M.P. and J.K.; software, M.P. and M.H.; validation, M.P., P.L. and M.H.; formal analysis, M.P. and J.K.; investigation, M.P. and P.L.; resources, J.K.; data curation, P.L. and M.H.; writing--original draft preparation, M.P., P.L. and M.H.; writing--review and editing, M.P., P.L., M.H. and J.K.; visualization, P.L. and M.H.; supervision, M.P.; project administration, M.P.; funding acquisition, M.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Slovak Research and Development Agency under the contract APVV 15-0435 and by the Ministry of Education, Science, Research and Sport of the Slovak Republic under the contracts VEGA 1/0516/22 and VEGA 1/0500/20.  
Not applicable.  
The authors declare no conflict of interest.
# A Study on the Remaining Reserves of the Weiyuan Shale Gas Block in Three-Dimensional Development
## 5 Countermeasures to Improve Recovery
### Reserve Utilization and Recovery Analysis  
In terms of reserve utilization, the well control area of the lower-half branch well of the H8 platform is 1.68 km\({}^{2}\), and the original geological reserves of the formation are about \(12.05\times 10^{8}\) m\({}^{3}\) with an original reserve abundance of 7.17 \(\times\) 10\({}^{8}\) m\({}^{3}\)/km\({}^{2}\). The current geological reserve is about 10.07 \(\times\) 10\({}^{8}\) m\({}^{3}\), the reserve abundance is 5.99 \(\times\) 10\({}^{8}\) m\({}^{3}\)/km\({}^{2}\), and the overall recovery degree of the producing layer is 16.4%. After the deployment of the three-dimensional development wells, with a fixed production rate of 6 \(\times\) 10\({}^{4}\) m\({}^{3}\)/d for 20 years of production, the predicted remaining geological reserves are approximately 8.31 \(\times\) 10\({}^{8}\) m\({}^{3}\), the predicted remaining reserve abundance is 4.95 \(\times\) 10\({}^{8}\) m\({}^{3}\)/km\({}^{2}\), and the overall stratigraphic recovery is 31.0%, with an increase in the recovery factor of 14.6%, which will result in an increase in the production for the platform of 1.76 \(\times\) 10\({}^{8}\) m\({}^{3}\) (Figure 10). At the same time, the stratigraphic pressure of each small layer shows different degrees of decline compared with the current stratigraphic pressure. Among them, the Longyi\({}_{1}\)[4] minor layer has the most obvious formation pressure depletion in three-dimensional development, and the predicted formation pressure is 19\(\sim\)20 MPa, while the Longyi\({}_{1}\)[1] layer is the least affected, and the formation pressure basically remains unchanged. If only the horizontal section of the W1 and W3 wells is used as the boundary of the control range, the predicted formation pressure of the Longyi\({}_{1}\)[4] minor layer in the W4 and W5 wells after 20 years of production is only 7.73 MPa, and the pressure depletion is more than 13 MPa (Figure 11).  
Figure 9: Three-dimensional development diagram of “M”-type pattern well pattern.  
Figure 11: Pressure changes in the layers.  
Figure 10: Distribution of remaining reserves: (**a**) remaining geological reserves of Longyi\({}_{1}\)\({}^{1}\) layer; (**b**) remaining geological reserves of Longyi\({}_{1}\)\({}^{2}\) layer; (**c**) remaining geological reserves of Longyi\({}_{1}\)\({}^{3}\) layer; (**d**) remaining geological reserves of Longyi\({}_{1}\)\({}^{4}\) minor layer; (**e**) remaining geological reserves of Longyi\({}_{1}\)\({}^{4}\) minor layer; (**f**) remaining geological reserves of Longyi\({}^{14}\) minor layer.
## 3 Results
### Stream Flow Dynamics  
The purpose behind the river flow calibration and validation was to compare simulated and observed hydrographs at the catchment outlet as adjustment of parameters for GW calibration has a direct impact on surface flows. The outflow hydrograph has the potential of representing the integrated  
Figure 10: _Cont._effect of the hydrological response of the catchment as a whole. Flow measurement campaigns were launched under the BMBF project named as \"Boat monitoring\" [55], and flow was measured at different chainages along the river Tollense. River flow was calibrated at four chainages at 270, 1365, 7048, and 23,341 m from the downstream end of the Tollense river. The statistic quality criteria concerning river dynamics (mean absolute error, root mean square error, correlation coefficient, and standard deviation residuals) are summarised in Table 7. The simulated results were satisfactory and were able to predict the general characteristics of the discharge time series at specified chainages. Simulated hydrographs underestimate the river flows during the observed low flow periods, while overestimate the river flows during the observed peak flow discharges in the river Tollense. The probable reason behind this discrepancy is the artificial drainage in the catchment. Artificial drainage was constructed based on DEM lowest points and does not fully represent the real installed drainage system in the catchment. GW levels lower than artificial drainage makes the drainage ineffective and results in small GW contribution to the river. For periods with GW levels higher than constructed drainage in MIKE SHE, drainage becomes effective and may contribute more to the river Tollense in comparison to reality. Moreover, average rainfall over the entire area in each polygon assigned to its respective climate station, average hydraulic conductivity of an entire geological layer, and drainage time constants also effects the GW contribution to the SW flows, and cause differences between observed and simulated river discharges.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Stream Flow Observation Points** & **MAE (m)** & **RMSE (m)** & **R (Correlation)** & **STDres** \\ \hline Chainage 270 m from d/s & 0.998 & 1.190 & 0.974 & 1.190 \\ Chainage 1365 m from d/s & 1.680 & 1.730 & 1 & 0.412 \\ Chainage 7048 m from d/s & 0.813 & 0.822 & 0.999 & 0.118 \\ Chainage 23,341 m from d/s & 0.182 & 0.224 & 0.999 & 0.132 \\ \hline \end{tabular}
\end{table}
Table 7: Statistics of the objective function for river flow calibration.  
Figure 11: _Cont._