esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
### Level and Month Selection  
Landsat 8 OLI Collection 2 provides two levels of data, Level-1 and Level-2, so selecting the appropriate level is crucial. Both Level-1 and Level-2 data were downloaded, FLAASH atmospheric correction was performed on the Level-1 data, spectral values were extracted from both levels, and a Pearson correlation analysis was conducted between the extracted spectral values and salinity to select the appropriate level. Our study found that Level-1 data exhibited a higher correlation with salinity than Level-2 data. LaSRC is used to generate Landsat-8 OLI Level-2 surface reflectance data products. LaSRC utilizes auxiliary climate data from MODIS. [PERSON] et al. [43] found, through field measurements of surface reflectance, that the error prediction for Landsat-8 OLI Level-2 surface reflectance data showed significant discrepancies in the coastal aerosol band, with a notable decrease in consistency between coastal aerosols and the blue band. It was observed that aerosol extraction at certain sites may be unreliable. This finding aligns with our correlation analysis results, as the CO band of Level-1 data showed a higher correlation with salinity than the Level-2 band. The result indicates that Level-2 data are less reliable compared to Level-1 data.  
Furthermore, since the effects of soil salinity inversion vary by month, selecting the appropriate month is crucial for accurate inversions. Salinity in the study area changes month by month, and as long as the internal patterns are understood, the timing of remote sensing imagery does not necessarily need to be confined to the sampling times. In selecting the month, three factors were considered, as follows: (1) the correlation between salinity and spectral bands, (2) the collinearity of the bands themselves, and (3) expert experience. The spectral values on August 26 had the highest correlation with salinity, while January 15 data showed lower collinearity. Expert experience and the literature suggest that January 15 data reduce the interference from vegetation and the low air humidity minimizes abnormal electromagnetic wave reflections, allowing for more effective salinity inversion. Thus, the data from August 26 and January 15 were used to build PLRSR, RF, and BPNN models, and the month was selected based on the performance of the models. Ultimately, it found that the RF model built with January 15 data performed the best, with a test set R\({}^{2}\) of 0.55, RMSE of 3.4, MAE of 2.79, SD of 2.99, and MSE of 11.62. In conclusion, January 15 data are more suitable for salinity inversion in the Yellow River Delta.
## 6 Discussion
### Final Considerations  
This section discusses the key elements that influenced the performance of the evaluated models, with particular emphasis on the impact of feature selection and class balancing techniques in enhancing accuracy, generalization, and overall model efficiency. Additionally, it outlines concrete recommendations for improving the effectiveness of Long Short-Term Memory (LSTM) networks based on the limitations identified during the experimental evaluation.  
Overall, the models based on Random Forest and XGBoost demonstrated a high level of effectiveness in detecting anomalies in network traffic, as evidenced by their superior performance metrics. These models are particularly well-suited for this task due to their capacity to handle high-dimensional data and to identify the most relevant features, which contributes to more robust and interpretable classification models in operational environments.  
By contrast, although LSTM networks offer strong potential for modeling sequential dependencies in temporal data, the results of this study indicate that their performance was suboptimal under the given experimental conditions. To enhance the predictive capabilities of LSTM-based models in future research, the following recommendations are proposed:* Conduct a comprehensive hyperparameter optimization process, utilizing strategies such as GridSearchCV or Random Search to identify the optimal architecture configuration.
* Expand the volume of training data, ensuring that temporal sequences are sufficiently representative and correctly labeled to improve the learning of complex patterns.
* Investigate the use of advanced or hybrid architectures, such as attention-based LSTM or convolutional LSTM (CNN-LSTM), to enhance the model's ability to capture intricate temporal features.
* Implement specialized preprocessing techniques for temporal data, including time window segmentation and the extraction of higher-order temporal features, to better structure input sequences.  
These improvements are essential to fully leverage the potential of deep-learning approaches in dynamic IoT/Cloud scenarios and to develop more adaptable and resilient intrusion detection systems.
## 3 Considerations for a Real WSN Deployment
### Use of Heterogeneous Nodes  
Provided that the life of the entire WSN can be enhanced, then specialization of a fraction of the nodes can considerably reduce the cost of the whole system. For example, a set of nodes can use larger antennas with better propagation patterns. This will allow our antenna constrained sensing nodes to be more easily reached, saving energy and employing fewer cluster heads. Better antennas also provide an additional benefit of a reduced hop-stretch.  
\begin{table}
\begin{tabular}{|l|c|c|c|} \hline
**Battery model** & \begin{tabular}{c} **Capacity** \\ **(mAh)** \\ \end{tabular} & \
\begin{tabular}{c} **Usable** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **Estimated lifetime** \\ **(years)** \\ \end{tabular} \\ \hline EMB er14250 3.6 V (selected) & 1,100 & 75 & 13.16 \\ Lithium-thionyl 2/3 AA 3.6 V & 1,700 & 75 & 20.34 \\ Duracell 34 mm \(\times\) 16.9 diam. 3 volts. DL12 AB1 Ultra M3 & 1,500 & 75 & 17.95 \\ Duracell Ultra 3 v. 27 \(\times\) 15.6 diam DCLR2 & 950 & 75 & 11.37 \\ \hline \end{tabular}
\end{table}
Table 3: Estimated battery life. Star configuration.  
An advantage of the heterogeneous approach is cost optimization, as the cost of the sensing nodes is minimized with the inclusion of better nodes. When a homogeneous configuration is proposed, we need to provide sufficient capabilities to all the nodes, increasing the unit cost of each node.
## 1 Introduction  
One of the functions of an inland terminal is the storage of the cargo units passing through it, in this case containers. Any kind of delay in the release of a container is associated with serious financial losses for the terminal, hence the importance of organizing the planning process for the storage of containers in the storage yard. Loaded containers are usually stored in the yard for several days; however, empty containers can be there for several weeks. The focus here is not only on optimizing the operation of the handling equipment, but also on managing the storage space in the yard. One of the biggest problems contributing to losses is reshuffling. Reshuffling requires extra handling operations to move the containers above to another location in order to access the container being reloaded. Each of the removed containers can still block further loading units. The following four movements are considered the optimal and necessary number of operations during the transshipment of one container:  
* Carrying the container when it is unloaded from the means of transport;
* Locating it in the yard at the most convenient place;
* Picking it up again from the yard;
* Placing it, at the moment of loading, on another means of transport.  
Such a number of operations is assumed by the land terminal operators in their cost calculations; however, sources based on the conducted research show that, on average, there are eight such movements, while there are cases when the number of operations increases up to fourteen. Such extreme cases occur mainly at terminals that are not equipped with a computerized storage management system. This is also the case for terminals with a lack of storage space and modern equipment. The use of transshipment planning methods allows one to minimize the number of redundant technological movements and, consequently, to shorten the container transshipment time associated with an increase in terminal throughput.  
The aim of the present paper is to compare the methods used for determining container storage locations in a container yard. The \"first available space\" method, FIFO and the author's model of allocating container operations are compared.
## 5 Parametric Study of the PMA Unit Cell
### Effect of Varying the Tuning Fork Split Gap  
The correlation with the opening end width of the double tuning fork and simulated result scenario is indicated in Figure 10b. With the increase or decrease of the split gap, the net capacitance changes inversely. Thus, the minor resonance crests move to higher frequencies. Nevertheless, the greater absorption peak remains almost unbothered, which provides an easy approach to adjust distinct absorption frequencies.
## 1 Introduction
AE signals received at the same position by two kinds of sensors are treated separately to examine the effect of the sensor on the AE features and on the results of classification. AE signals received at increasing distances (with and without waveguide) by the same type of sensors are analysed independently to examine the effects of travelling distance on the classification results. This study investigates from an experimental point of view the influence of sensor, propagation and damage on the AE features. In this paper, we are going to focus mainly on the evolution of amplitude, energy and frequency.
# Numerical Integration Schemes Based on Composition of Adjoint Multistep Methods
## 5 Experimental Investigation  
### Computational Efficiency  
The computational efficiency of the proposed methods was practically investigated using well-known nonlinear Rossler system, which exhibits chaotic behavior:  
\[\dot{x} =-y-z\] \[\dot{y} =x+ay\qquad,\] \[\dot{z} =b+z(x-c)\]  
where parameters values are \(a=0.2,\ b=0.2,\ c=5.7\).  
To investigate the efficiency of the proposed methods we used an efficiency plot tool, the maximal error is plotted along the \(x\)-axis and elapsed time is plotted along the \(y\)-axis. Since the absolute integration error of the method can't be measured due to the absence of an analytical solution, relative integration error with respect to the eight-order Runge-Kutta method is found.  
Proposed composition schemes were compared with methods-predecessors and the 3-step Adams-Moulton method of fourth order. With exception of the two-step Adams-Bashforth method, the Newton method was applied to resolve implicitness in all cases. In Figure 5 one can see that the composition scheme based on the two-step Adams-Bashforth method possesses very low efficiency in comparison to its predecessor despite better accuracy due to the Newton method's extra computational costs. Nevertheless, the composition scheme based on the two-step Adams-Moulton method appears to be more efficient than the two-step Adams-Moulton method and has the same computational efficiency as a popular 3-step Adams-Moulton method, which makes it suitable for practical application.
# Design of HIFU Treatment Plans Using Thermodynamic Equilibrium Algorithm
## 3 Results & Discussions
### Optimization Performance
In continuous GA, mutation and selection rates were set to 0.3 and 0.5, respectively, and for PSO, both \(p\_increment\) and \(g\_increment\) were set to 2. The minimum cost of all population versus generation is shown in Figure 9a. At the fifth iteration, TEA found the minimum to near one-thousandths and is attempting to increase the accuracy of the answer in the following iterations. GA and PSO converged to the global minimum at iteration 31 and 36, respectively, by the accuracy of the one-thousandth. Figure 8: (**a**) Initial systems, (**b**) systems at iteration 17, (**c**) systems at iteration 34, and (**d**) systems at final iteration. Figure 7: 3D plot of \(f_{1}\) (Ackley’s function). Problems \(f_{2}\) and \(f_{3}\) are Sphere function and Rosenbrock function, respectively. The minimum cost against the iteration for \(f_{2}\) and \(f_{3}\) achieved by TEA are compared with those by GA and PSO, as shown in Figure 9b,c, respectively. Both of the plots indicate that TEA performed best, followed by GA and PSO. Table 1 shows the results of optimizations performed by GA and TEA for 20 different test functions to compare the performances when dealing with various types of problems. Table 2 presents the optimization results for five different constrained test functions, also by GA and TEA to investigate the precision of the proposed algorithm in constrained optimization. The numbers of initial systems and iteration used in problem \(f_{1}\) to \(f_{25}\) were 100 and 50, respectively. Tables 1 and 2 show the minimum cost of all systems in problems \(f_{1}\) to \(f_{20}\) and \(f_{21}\) to \(f_{25}\), respectively. In all test functions, TEA yields better results than GA. In smooth functions, such as \(f_{2},f_{6}\), and \(f_{9}\), both algorithms have comparable performance. GA could find the answers within the given number of iterations and tried to improve the accuracy in further iterations. However, for complex functions which include several local optima, such as \(f_{11}\) and \(f_{13}\), GA was trapped
## 6 Conclusions  
In this paper, we have introduced a novel approach for sandstorm image restoration, emphasizing the importance of channel compensation, color transfer, and illumination adjustment in addressing the challenges posed by such extreme conditions. Through extensive experimentation, we have demonstrated that our algorithm effectively restores the true colors of sandstorm images, successfully eliminating color casts while preserving essential image details. Furthermore, the method exhibits robust sand-dust removal capabilities, even in severe sandstorm scenarios, which sets it apart from existing solutions. However, despite these promising results, some limitations remain. Specifically, the saturation line prior may not yield optimal performance in some environments, and the local block-based approach can be computationally demanding, especially when dealing with high-resolution images. Given these findings, we suggest that future studies could focus on two main aspects: investigating faster and more accurate approaches for local atmospheric light estimation and employing more robust priors to calculate transmission in regions where the saturation line prior fails. Moreover, combining detail and texture enhancement methods could improve the algorithm's performance.  
Conceptualization, S.Z. and F.S.; methodology, S.Z. and F.S.; software, S.Z. and F.S.; validation, S.Z., G.W. and J.H.; formal analysis, S.Z. and F.S.; investigation, S.Z., G.W. and J.H.; resources, S.Z. and F.S.; data curation, S.Z. and F.S.; writing--original draft preparation, S.Z.; writing--review and editing, S.Z., F.S., Z.J., G.W. and J.H.; visualization, S.Z.; supervision, F.S. and Z.J.; project administration, F.S. and Z.J.; funding acquisition, F.S. and Z.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Xinjang (Grant 2022D01C58), the National Natural Science Foundation of China (Grant 62261053), the Tianshan Talent Training Project--Xinjang Science and Technology Innovation Team Program (Grant 2023 TSYCTD0012), the Tianshan Innovation Team Program of Xinjang Uygur Autonomous Region of China (Grant 2023D14012), and the Scientific Research Plan of Universities of Xinjang (Grant XJEDU2019Y006).  
Figure 11: Failure cases of color correction.  
Figure 10: Failure cases of dust removal.
## 6 Challenges and Future Research Directions
### Challenges
#### 6.1.3 Channel Estimation and Feedback Overhead  
Accurate channel estimation is crucial for the effective implementation of SWIPT in mMIMO-NOMA systems. However, the large-scale nature of mMIMO systems results in significant feedback overhead, which can strain system resources and degrade performance. Moreover, the non-orthogonal nature of NOMA adds another layer of complexity to channel estimation, making it challenging to achieve high accuracy in practical deployments.
## 2 Materials and Methods
### One-Dimensional Convolutional Neural Network (I-D CNN)
#### 2.4.3 Validation Technique and Dataset Description  
An essential aspect of the experimental setup is the application of a 10-fold cross-validation technique to assess the model's performance and generalizability. Cross-validation is a statistical method used to estimate the skill of deep learning models. It is commonly used in applied deep learning to compare and select a model for a given predictive modeling problem.  
The dataset used in this study consists of 75 samples, each representing a different instance of tool wear condition. These samples were collected from a Busellato Jet 130 CNC (Busellato, Thiene, Italy) machining center, where each sample corresponds to a specific milling operation involving chipboard. The dataset was gathered using multiple sensors, including force sensors, acoustic emission sensors, noise sensors, vibration sensors, and electrical sensors, capturing a wide range of signal parameters during the milling process.  
Each data sample is characterized by its sequential nature, comprising 100 sequences, with each sequence containing 308 features. These features were extracted from the raw sensor data using techniques such as time-domain analysis, frequency-domain analysis, and time-frequency analysis. The preprocessing steps included normalization and standardization to ensure that all features were on a comparable scale, enhancing the performance and convergence of the deep learning models.  
In the 10-fold cross-validation approach, the dataset was partitioned into 10 equal-sized subsets. During the cross-validation process, each subset was used once as the validation set, while the remaining nine subsets formed the training set. This procedure was repeated 10 times, with each of the 10 subsets used exactly once as the validation data. Thus, each training session involved 68 samples for training and seven samples for testing, ensuring that all samples were tested for validation.  
This method provides a comprehensive evaluation of the model's performance across the entire dataset, ensuring that the results are not biased by the selection of training and validation samples. The cross-validation technique helped to assess the model's ability to generalize itself to unseen data, providing a robust estimate of its real-world performance.  
By utilizing the detailed dataset and rigorous cross-validation approach, the study ensured a thorough evaluation of the model's capabilities in classifying tool wear conditions, leveraging the rich sequential data to improve accuracy and reliability.
## 2 Analytical Model of the Drive and Transmission Mechanism
### Power Characteristics  
Power characteristics are a method of graphical visualization of the balance of powers. They show the dependence of the usable engine powers and the powers required to drive the vehicle as the dependence on its speed. Power characteristics are therefore a graphical representation of the results obtained by the solution of Equation (5).  
Similarly to the torque values, the power values at full fuel delivery are obtained directly from the external engine revolution characteristics overviewed in Table 2. In the equation for the determination of the vehicle speed (7), the engine revolutions corresponding to the given points of the external engine characteristics and other necessary parameters were applied. In this way, the values of the usable engine power at all gears can be obtained.  
To calculate the power (8) required to overcome the driving resistance, a modified version of Equation (5) can be used:  
\[P_{e}\eta_{m}=\frac{v}{3600}\left(gm(sin\propto+fcos\propto)+\frac{\rho}{2}c_ {x}Sv^{2}+ma\phi+\frac{m^{\prime}(g\psi^{\prime}+\propto\phi^{\prime})}{1+f^{ \prime}tg\gamma}\right) \tag{8}\]  
where \(P_{e}\) is engine power, \(\eta_{m}\) is the mechanical efficiency of the drive and transmission mechanism, \(v\) is the vehicle speed, \(g\) is gravitational acceleration, \(m\) is the vehicle mass, \(\alpha\) is the terrain slope angle, \(f\) is the rolling resistance coefficient, \(\rho\) is air density, \(c_{x}\) is the air resistance coefficient, \(S\) is the vehicle frontal area, \(\vartheta\) is the rotating mass resistance coefficient, \(a\) is vehicle acceleration, \(\psi\) is the road resistance coefficient, \(m^{\prime}\) is the trailer mass, and \(\gamma\) is the coupling angle.  
At the same time, there is a relation (9) between the engine power, its torque and revolutions:  
\[P_{e}=T_{e}\omega_{e=}T_{e}\pi\frac{n_{e}}{30} \tag{9}\]  
where \(T_{e}\) represents engine torque, \(\omega_{e}\) the engine angular velocity and \(n_{e}\) the engine revolutions.  
Figure 4 shows the characteristics of the usable powers of the IVECO F1 CE0489C engine at full fuel supply for particular gears together, with the values of power required to overcome the driving resistance at different terrain slope angles visualized by the dashed line. Based on the analysis of the power characteristics, it is possible to get a very good overview of the vehicle's driving characteristics, such as its maximum speed in straight-line driving, its climbing ability at individual gears, the required gear to overcome the resistance considering the given terrain slope angle, the maximal speed for a certain terrain slope angle, etc. Power characteristics also provide information about the driving speed at which it is appropriate to shift gears.  
Figure 3: Analytical torque characteristics of the IVECO F1 CE0489C engine.
## 3 Results
### Perfect Absorption under Oblique Incidence
At \(\theta=80^{\circ}\), the zero is not located on the  
Figure 2: (**a**) Absorption of the metamaterial using \(N=1\) HR as a function of the frequency for incidence angles \(\theta=20^{\circ},80^{\circ},87.7^{\circ}\); (**b**) Angle-dependent absorption obtained using transfer matrix method (TMM; continuous lines) and Equation (10) (circles) at frequencies \(f=250,300,350\) Hz; (**c**) Map of the absorption as a function of the frequency and incidence angle. real axis, but as it is close to it, its effect is still visible and a broad peak of absorption is observed in the absorption at \(f_{r}=300\) Hz, as we have shown previously. Finally, if we continue increasing the incidence angle, we can see that the trajectory of the zero turns towards the real frequency axis, eventually crossing it once again at \(f=350\) Hz and \(\theta=87.7^{\circ}\): the structure becomes again critically coupled and a peak of perfect absorption is produced. Finally, note that the TMM model predicts that all waves are reflected at grazing angles (\(\theta\to 90^{\circ}\)), then, no zero or pole exists in the complex frequency plane. Thus, as shown in Figure 3c, we observe that the trajectory of the zero finishes at the same complex frequency as its corresponding pole, both collapsing at the same point and resulting in no absorption for grazing incidence. To summarize, using a rigidly-backed metamaterial composed of one resonator, a single pair of zero-pole is observed in the complex frequency plane, and we have shown that the trajectory of the zero-pole in the complex frequency plane follows an arch when the incidence wave is tilted. Thus, a maximum amount of two peaks of perfect absorption can be obtained. Then, if the number of resonators increases to \(N\), the system will present \(N\) resonances, each one corresponding to a zero-pole pair.