esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Framework and Network
### Laguerre Gaussian Beam with Orbital Angular Momentum  
Beams carrying orbital angular momentum are called vortex beams, and Laguerre-Gaussian (LG) beams are one of the most common types of vortex beams. The expression of the light field of LG beams propagating along the \(z\)-axis in the source plane is [1]:  
\[\begin{array}{l}U_{p}^{l}(r,\theta,z)=\sqrt{\frac{2p!}{\pi(p+|l|)!}}\frac{1}{ w(z)}\Big{[}\frac{r\sqrt{2}}{w(z)}\Big{]}^{|l|}\mathrm{L}_{p}^{|l|}\Big{[} \frac{2p^{2}}{w^{2}(z)}\Big{]}\exp\Big{[}\frac{-r^{2}}{w^{2}(z)}\Big{]}\exp[ \frac{i\alpha r^{2}z}{2\left(z^{2}+z_{\mathrm{R}}^{2}\right)}]\times\\ \exp\Big{[}-i(2p+|l|+1)\mathrm{arctan}\Big{(}\frac{z}{z_{\mathrm{R}}}\Big{)} \Big{]}\exp(-\mathrm{i}\mathrm{l}\theta)\end{array} \tag{1}\]  
\[\begin{array}{l}L_{p}^{|l|}(x)=\sum_{m=0}^{p}\,{(-1)}^{m}\frac{(|l|+p)!}{(p- m)!(|l|+m)!m!}x^{m}\end{array} \tag{2}\]  
Figure 1: Schematic diagram of OAM transmission system.  
where \(l\) represents the topological charge, \(p\) denotes the radial index of the LG beam, \(r\) is the distance from a point in space to the central axis, \(\theta\) is the azimuthal angle, is the transmission distance, \(\kappa=2\pi/\lambda\) is the wave number, \(z_{\mathrm{R}}=\pi w_{0}^{2}/\lambda\) is the Rayleigh range, \(\lambda\) is the wavelength, \(w_{0}\) is the initial beam waist radius, \(w(z)=w_{0}\sqrt{1+\left(z/z_{R}\right)^{2}}\) is spot size, \(\mathrm{L}_{p}^{||}\) represents the associated Laguerre polynomial.
## 2 Materials and Methods
### Application on Real Wastewater  
A wastewater sample was collected from the El-Emoum drain, which neighbors Lake Maruit, Alexandria, Egypt, and which comprises numerous industrial sewage and agricultural wastes. The wastewater had a pH of 9.5, a TSS of 250 mg L\({}^{-1}\), and a TDS of 3240 mg L\({}^{-1}\). Owing to the low concentrations of dyes, an amount of IV2R dye was added to obtain 10 mg L\({}^{-1}\) of this dye. The dye solution was filtered to remove precipitates and suspended matters. The application of MWCNTs to remove the IV2R dye from the wastewater was carried out under optimized conditions (pH 2 for 120 min at 30 \({}^{\circ}\)C and 0.08 g of adsorbents in the solution). Deionized water comprising a similar concentration of dyes was prepared to use as a control to estimate the effect of adsorbent on IV2R dye removal. The absorbance was determined using a Schimadzu UV 2100 spectrophotometer. Furthermore, the pH was determined by a digital electrode pH meter, while the total suspended solids (TSS) and total dissolved solids (TDS) were measured by standard methods [42].
###### Abstract  
The study aimed at the simple prediction problem of finite soil pressure for a new foundation pit adjacent to an existing subway station. Failure modes of the finite soil were proposed. Considering the friction between the soil and the structural interface, the formulas for calculating the finite soil pressure were derived with the horizontal differential element method, and the distribution was discussed. Furthermore, a simple calculation method for the resultant force of the finite soil pressure and the height of the action point was proposed with the principle of area equivalence. With the example and model tests, the rationality of the above method was verified. The influences of parameters on the resultant force of the soil pressure and the relative height of the resultant force were analyzed. The main conclusions are as follows: (1) The finite soil pressure has a nonlinear distribution and is lower than the conventional soil pressure. (2) Compared with other existing methods and model test results, the proposed finite soil pressure calculation method and simplified method are rational. (3) The finite soil pressure increases with the ground overload and soil weight and decreases with the internal friction angle and the structure-soil friction angle. (4) The relative height of the resultant force of the finite soil pressure increases with the structure-soil friction angle and decreases with the internal friction angle. (5) The proximity and covering soil thickness of the existing subway station have a lesser influence on the finite soil pressure, whereas the internal friction angle, weight, structure-soil friction angle and ground overload have a greater influence on the finite soil pressure. (6) The structure-soil friction angle has a greater influence on the height of the action point. The height of the resultant force is one-third of the enclosure structure depth if the structure-soil friction angle is 0.  
1 Key Laboratory of Large Structure Health Monitoring and Control, Shijiazhuang Tiedao University,  
Shiiazhuang 050043, China  
School of Safety Engineering and Emergency Management, Shijiazhuang Tiedao University,  
Shiiazhuang 050043, China  
State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures,  
Shiiazhuang Tiedao University, Shijiazhuang 050043, China  
School of Civil Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China  
* Correspondence: [EMAIL_ADDRESS]  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Calculation Method of the Finite Soil Pressure for a New  
Foundation Pit Adjacent to an  
Existing Subway Station. _Appl. Sci._**2023**, _13_, 1994. [[https://doi.org/10.3390/app13031994](https://doi.org/10.3390/app13031994)]([https://doi.org/10.3390/app13031994](https://doi.org/10.3390/app13031994))  
**Keywords:** underground engineering; foundation pit; existing subway station; finite soil; resultant force; calculation method
## 4 SIP Protocol Analysis
### SIP Response  
According to [43], SIP Responses have a Status-Line that shows the SIP response behavior. As shown in Figure 6, SIP responses from 1 xx to 3 xx are not considered as failures. However, SIP responses from 4 xx to 6 xx are considered as user, system, and global failures. Although 4 xx responses are used for Client errors, some 4 xx errors do not resemble a problem as 486 (user busy error). This response is released when a user has an ongoing call. Moreover, 487 (request terminated) usually occurs when a user terminates a call. Therefore, we labeled the responses from 4 xx to 6 xx as call failures except for 486 and 487.
## 5 Results and Discussion
### Performance Comparison with Other Classes of Feature Selection Algorithms  
The proposed hybrid NSGA3 performance is further compared with other algorithms that are different to the family of the NSGA to verify the advantage of the proposal. Table 4 presents the results of the comparison between the proposed hybrid NSGA3 and other meta-heuristic algorithms. The results indicated that the proposal perform better than the compared algorithms. This has proven the advantage of the hybrid NSGA3 in selecting optimum subset features influencing vehicle collision in the IoV while maximizing accuracy. Regarding the convergence time, the hybrid NSGA3 converges faster than the compared algorithms. Therefore, the proposed hybrid NSGA3 outperforms the compared algorithms in both accuracy and convergence time.
# Effectiveness of Machine Learning in Assessing the Diagnostic Quality of Bitewing Radiographs
## 2 Materials and Methods
### Dataset  
After scoring all selected bitewing radiographs, feature selection was performed to identify prominent features leading to successful classification results. Accordingly, the labels that were included in the model training process were the following:  
1. Overlap within enamel.
2. Overlap within a restoration (clear margin).
3. Overlap at DEJ.
4. Overlap within dentin.
5. Overlap within a restoration (unclear margin).  
Since the machine learning task is an object detection task, the \"out-of-field\" label had to be excluded, as the contacts in this case are not present in the images and thus cannot be identified using bounding boxes. In addition, labels that were not errors, such as missing tooth/surface, and open contacts have also been excluded to avoid misclassification.  
Images were converted from TIFF to JPG format then manually labeled using Roboflow, a tool created by for-profit Roboflow company that facilitates collaborative image annotation and allows for easy split, augmentation, and exportation. The resulting dataset was then split into three sets: training set (583 images), validation set (167 images), and test set (84 images).  
It seems that since the data were randomly collected, and some labels of more common errors are over-represented in the dataset, as shown in Table 1. Hence, the model would probably excel at detecting these labels more than it would with others. On the other hand, we can see that \"overlap with restoration -unclear margin-\", \"overlap within dentin\", \"overlap at DEJ\", are all under-represented, thus it is expected from the model to not be as efficient at detecting these classes.  
To overcome the imbalance issue, oversampling and under sampling techniques were applied for over-represented labels, and randomly selected images were removed. To increase under-represented labels image augmentation was performed, where we increased the number of images that contained these labels by using shift, flip, brightness, and zoom image data augmentation.
# Optical Beamforming Networks for Millimeter-Wave Wireless Communications
## 3 OBFNs with TTD Architectures
### Fiber Dispersion Delay Architectures
In this architecture, multiple optical carriers are first modulated with \(N\) RF frequencies at the polarization modulator (PolM), then delayed by the fiber dispersion unit and selected by the programmable filter which induces \(N\) optical carriers to \(N\) different MPF paths; finally, RF signals with \(N\) center frequencies are generated at MPF paths and sent to an antenna element. This indicates that a 2D PAA with this OBFN architecture can achieve multi-beam beamforming, which is a promising technique for a mmWave massive multiple-input multiple-output (MIMO) system [9; 55]. However, there is an issue for this concept in that multi-frequency beamforming requires wideband PAAs which are not commercially available presently. Two-dimensional beamforming with one center frequency is easier to be implemented with a fiber dispersion delay architecture, as shown in Figure 6b [56]. Similarly, an electro-optic frequency comb is utilized to provide multiple wavelengths, whereas optical carriers are modulated by Mach-Zehnder modulators (MZM), are then delayed by two-stage TTDs including dispersion compensation fibers (DCFs) and tunable delay lines (TDLs), and are split into various cores of the multi-core fiber (MCF). Next, they are filtered by de-multiplexers and detected by PDs. Finally, the RF signals produced are distributed to different columns and rows of the PAA. The delays of optical carriers with different wavelengths are separately tuned by DCFs, while relative signal delays in various cores of fiber are controlled by TDLs. This architecture features a time delay variation of less than 1 ps with a drift in room temperature, which is much more stable than that (\(\sim\)15 ps) with a single-mode fiber. Similarly, the 2D OBFN with fiber dispersion delay architectures has a large size and high sensitivity to changes in the environment, since discrete devices and fibers are applied in this architecture. Figure 5: Schematic diagrams of OBFNs based on fiber dispersion with (**a**) one wavelength and \(N\) antenna elements [42] (Copyright 1993, IEEE, #5487530392418), and (**b**) \(N\) wavelength and \(N\) antenna elements [52] (Copyright #2017, IEICE, #22 RB0082).
## 5 Discussion
### Validation of Data Preprocessing
By balancing the original dataset with the SMOTE algorithm, better findings were obtained. During the training process, the second problem can be effectively improved by modifying the loss function. This can be completed by dynamically adjusting the number of hidden nodes or utilizing an adaptive learning rate. Additionally, specialized loss functions designed for imbalanced data, such as focal loss and class-balanced loss, can be employed to address the other two issues. By implementing these methods, the learning efficiency of the network for minority classes was significantly enhanced. In summary, despite potential negative impacts, such as sample noise and overfitting, the SMOTE algorithm has demonstrated its capability to significantly enhance performance in handling imbalanced datasets. This study has verified the rationality and validity of the data preprocessing methods. Notably, to fully exploit the advantages of the SMOTE algorithm, it is essential to consider both the characteristics of the dataset and the chosen model.
# A Cross-Layer Optimized Opportunistic Routing Scheme for Loss-and-Delay Sensitive WSNs
## 5 The Design of the COOR Scheme
### Transmission Power Optimization
Figure 10:4 Transmit power of node at different distance in COOR(R) method. **Theorem 5**. : _Considering the transmit power of the traditional routing method is \(P_{tddm}\), and the node \(n_{l}\) is reset to \(P_{t}(l)_{dbm}\) after updated by COOR(P) method. Then the transmission radius of node \(n_{l}\) can be obtained:_  
Figure 10:5  
**Proof. ** According to Equation (4), maintaining the PAR means the SNR of the communication link remains unchanged. Putting Equations (1) and (2) together:  
Figure 10:6  
Figure 10:7  
Figure 10:8  
Figure 10:9  
\(\square\)  
In the COOR(P) method, the receiving and forwarding structure of the network has changed, it is necessary to assume that the regions to be forwarded by the area \(A_{l,k}\) are \(A_{l+u,k}\), then the data volume of node \(n_{l}\) can be calculated as follows:  
Figure 9:0  
Figure 9:1  
Figure 9:2  
Figure 9:3 Packet quantity difference under \(N=2\), \(r=60\) in COOR(R) method. Figure 9:4  
Similar to Theorem 4, the heuristic method is adopted to find the approximate optimal transmit power \(P_{t}(l)_{w}\) of each node in the COOR(P) method. The change of node transition area under COOR(P) method is shown in Figure 9:5 In general, the farther a node is from the sink, the larger its transition area is.
# Opportunistic Multipath Routing in Long-Hop Wireless Sensor Networks
## 5 Conclusions  
Many studies have been proposed for the improvement of the packet delivery ratio as it is one of the most important factors in WSNs. However, the existing studies have not achieved a sufficient packet delivery ratio in a long-hop sensor network and some require excessive energy consumption to satisfy the requirement. Therefore, we propose an energy-efficient reliable routing protocol based on opportunistic routing. The proposed scheme maximizes the link reliability and bifurcates with only one broadcast based on the characteristic of opportunistic routing that grants a transmission opportunity to all of the neighbor nodes. In other words, the proposed scheme can reduce the number of required paths due to the maximized link reliability and can achieve a high packet delivery ratio with the least number of paths compared with the existing multipath schemes. Moreover, as each node performs a contention-based transmission, the proposed scheme can avoid the potential problems of multipath studies, such as path failure and wasted recovery cost, by exploiting the fixed paths. From the simulation results, the proposed scheme reduces the energy consumption from at least approximately 33% to up to approximately 65% and shows a higher packet delivery ratio compared with those of existing routing protocols under the condition with an 80% link success ratio in a long-hop network.  
S.K., B.-S.K., K.H.K. and K.-I.K. contributed to the protocol design and detailed algorithm. Also, they conducted the performance analysis.  
This research was supported by the Human Resources Program in Energy Technology of the Korea Institute of Energy Technology Evaluation and Planning (KETEP); granted financial resource from the Ministry of Trade, Industry, & Energy, Republic of Korea (No. 20174030201440); and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (Grant no. NRF-2018R1D1A1B07043731).  
The authors declare no potential conflict of interests.
# Switched Auto-Regressive Neural Control (S-ANC) for Energy Management of Hybrid Microgrids
## 4 Results and Discussion
### Case 2: Implementation of the Merged S-MPC and AR  
Case 2 investigated the integration of the AR model with S-MPC. AR models accurately predict future time steps by capturing the time series behavior of the system. Various analyses, including variations, cross-validation (CV) iteration-time series behavior for training and validation, CV iteration-training data for each CV iteration, and predictions ordered by test prediction number were used to evaluate the performance of the AR models.  
Figure 7a shows a visualization designed to provide insight into the behavior of the lagged target feature over time. We can identify patterns, trends, and correlations within the lagged target data by examining the plot. Understanding the characteristics of the lagged target can aid in developing and optimizing an AR linear regression model that uses this characteristic for prediction. By displaying the lagging feature of the target, we can observe its values across multiple time steps. This lets us determine whether the lagged target exhibits specific patterns, trends, or variations over time.  
Figure 6: Comparison of (**a**) power flows and (**b**) SOC of the battery using the optimal method (S-MPC) and non-optimal method.  
As illustrated in Figure 7b, the variations in the AR models illustrate their capacity to capture and model the system's complex dynamics. By analyzing the CV iteration-time series behavior, the adaptability of the AR models to various patterns and tendencies in the training and validation datasets was assessed. This analysis shed light on how the models learn and generalize from the available data, enabling accurate predictions for various time series problems.  
Case 2's successful integration of AR models with S-MPC illustrated the importance of incorporating time series behavior and forecasting capabilities into a control system. Combining S-MPC and AR models permitted enhanced adaptation to system dynamics and improved the prediction accuracy, thereby enhancing the MG's overall control performance.
# Simulation and Catastrophe Detection of Spontaneous Combustion Processes in Suffide Ores
## 3 Results
### Temperature Field Reconstruction in Sulfide Ore Heap  
The measured temperature variations are analyzed in MATLAB (version R2016a), with different colors representing different rates of temperature increase. The red indicates regions with higher temperature changes, while the blue represents regions with lower temperature changes. The simulated period for all cases is 40 min. The temperature field reconstruction, which depicts the temperature variation across the sulfide ore heap, is shown in Figure 7.  
Figure 6: Variation of sulfur dioxide gas concentration in the sulfide ore heap.  
By analyzing the measured temperature changes in MATLAB, the temperature field of the sulfide ore heap can be roughly divided into four stages based on the temperature reconstruction:  
Stage 1: Before 160 min, the temperature rise at the surface of the sulfide ore heap is more significant than the temperature rise in the interior.  
Stage 2: From 160 to 200 min, the temperature rise in the sulfide ore heap's interior is more significant than at the surface.  
Stage 3: From 200 to 240 min, the temperature rise at the surface of the sulfide ore heap increases significantly, surpassing the temperature rise in the interior.  
Stage 4: After 240 min, the temperature rise in the interior of the sulfide ore heap begins to increase significantly and exceeds the temperature rise at the surface.  
These divisions help understand the changing temperature dynamics within the whole unsteady process of the spontaneous combustion of sulfide ore heap.
# A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery
## 3 Applications of FFD in Early Fault Diagnosis of Rotating Machinery
### Sparse Representation Methods  
Sparse representation aims to represent a signal accurately via a limited number of linear combinations of basic signals. [PERSON] et al. [115] utilized sparse representation-based latent components  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Authors** & **Methodologies** \\ \hline [PERSON] et al. [95] & Wavelet transform \\ [PERSON] et al. [96] & Wavelet transform \\ [PERSON] et al. [97] & Wavelet transform + time-frequency analysis + blind source Separation theory \\ [PERSON] et al. [111] & Morlet wavelet Filter + envelope detection \\ [PERSON] [112] & Morphological + Complex Shifted Morlet Wavelets. \\ [PERSON] et al. [98] & High-frequency characteristics + self-adaptive wavelet de-noising \\ [PERSON] et al. [114] & Complex Morlet wavelet coefficients + sparsity measurement \\ [PERSON] et al. [109] & Wavelet transform + envelope analysis \\ [PERSON] et al. [99] & Adaptive wavelet stripping algorithm \\ [PERSON] et al. [113] & Maximum Kurtosis + Morlet wavelet \\ [PERSON] et al. [100] & Wavelet bicoherence \\ [PERSON] et al. [101] & Wavelets multiresolution analysis + the high-frequency resonance \\ [PERSON] et al. [105] & Discrete wavelet transform \\ [PERSON] et al. [108] & HAAR wavelet \\ [PERSON] et al. [106] & Discrete wavelet transform \\ [PERSON] et al. [107] & Discrete wavelet transform + motor current signature analysis \\ [PERSON] et al. [102] & Adaptive redundant multiwavelet packet \\ [PERSON] et al. [103] & Adaptive multiwavelet \\ [PERSON] et al. [110] & Adaptive multiwavelet \\ [PERSON] et al. [104] & EMD + autocorrelation de-noising + wavelet package decomposition \\ \multicolumn{2}{c}{Intrinsic character-scale decomposition + tunable Q-factor wavelet transform.} \\ \hline \hline \end{tabular}
\end{table}
Table 7: Applications of wavelet transform method in early fault diagnosis of rotating machinery.  
decomposition to extract incomplete or seriously overwhelmed fault components for the EFD of bearings and gears. [PERSON] et al. [116] used improved linear frequency-modulated function as an atom in atomic sparse decomposition for early weak fault detection. [PERSON] et al. [117] proposed a delayed correlation envelope technique based on sparse signal decomposition method to extract early weak fault information. [PERSON] et al. [118] used the sparse decomposition to analyze the early-stage bearing fault via adaptive impulse dictionary construction. [PERSON] et al. [119] used the compressive sensing technique for dimensionality reduction, and then the sparse representation classification algorithm was applied for rotating machinery fault recognition. [PERSON] et al. [120] used resonance-based sparse signal decomposition (RSSD) to decompose the signal into high resonance component and low resonance component. Finally PCA was used to extract the principal components and transform the signals into frequency domain to locate the early fault of rolling bearing in motor. These methods are summarized in Table 8.