esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Power Allocation Optimization and Problem Formulations
### Formulation of the DASR Maximization Problem  
This subsection presents a formula for the DASR maximization for the problem considered in this paper. To this end, the DASR maximization of the MMIMO system in the FDD transition mode can be written as follows:  
\[\begin{split}\underset{\tau_{\text{tr}}}{\text{maximize}}& \left(\frac{\tau_{\text{c}}-\tau_{\text{tr}}}{\tau_{\text{c}}}\right)\ K\ \log_{2}\bigl{(}1+\text{SINR}\bigr{)}\\ \text{subject to}& 1\leq\tau_{\text{tr}}\leq\text{minimum} \bigl{(}\tau_{\text{c}},N\bigr{)}\end{split} \tag{21}\]  
As was previously indicated, to fulfill the requiters for a high data rate, we need to maximize the possible DASR [74]. Hence, the main goal of this paper is to maximize the DASR. The expression in Equation (21) demonstrates that the length of the training sequence and the LCT, as well as the power allocation in the SINR, are the main factors that determine how much the DASR can be maximized. While the training sequence length and the LCT have a linear effect on the DASR, the power allocation, which is involved in the SINR, has a logarithmic effect on the DASR.  
To sum up, unlike the conventional method where the performance of the DASR is investigated with equal power allocation, in this paper, we focus on optimizing the DASR with optimum power allocation. To the best of our knowledge, the power optimization to maximize the DASR performance of MMIMO systems in the FDD mode with LCT has not been investigated. The analyses developed above are valid for any channel model and precoding type. The formulation of the DASR problem in Equation (21) is computationally complex. This is due to the fact that in order to obtain the expectations in Equation (9), extensive Monte Carlo simulations over various channel realizations, different values of \(N\), different correlation coefficients, different training sequence lengths, different SNR values, and different coherence times must be performed. This makes power allocation optimization very difficult without finding a feasible solution. Therefore, in this paper, we develop a computationally viable solution that is found by employing the theory of the random matrix method to address this problem. Specifically, an analytical expression of Equation (9) and the DASR are obtained in this study as \(N\to\infty\) using the theory of the random matrix method. Consequently, a low-complexity, simplified numerical evaluation of Equation (21) is obtained, which allows for power allocation optimization over a range of scenarios. The following section provides an analytical expression of the RZFP using the theory of the random matrix method.
## 5 The Proposed Protocol  
Patient \(P_{i}\) directly communicates with patient \(P_{j}\) with the assistance of both servers \(S_{m}\) and \(S_{n}\). The proposed protocol consists of four phases: system initialization phase, registration phase, login and authentication phase, and password and biometrics update phase. All the parties, including \(P_{i}\), \(S_{m}\), \(P_{j}\), \(S_{n}\), participate in the communication, so that \(P_{i}\) and \(P_{j}\) can compute a shared E2E session key. Since the communication between \(P_{i}\) and \(S_{m}\) is identical with the one between \(P_{j}\) and \(S_{n}\), for simplicity, only the communication between \(P_{i}\) and \(S_{m}\) is presented in the registration phase and in the password and biometrics update phase.
## 3 Methodology
### Hypergraph Contextual Attention Module (HCA)
#### 3.3.2 Gating Mechanism  
To further enhance the attention process, a gating mechanism is introduced. This mechanism filters out less relevant features and focuses on processing the most critical elements:  
\[g_{i}=\sigma(W_{g}v_{i}+b_{g}), \tag{11}\]  
where \(\sigma\) represents the sigmoid function, \(W_{g}\) is a weight matrix, and \(b_{g}\) is a bias term for gating. This gate (\(g_{i}\)) multiplicatively adjusts the attention weights, refining the selection process.
## 3 Inland Waters
### Rivers
#### 3.1.2 Discharge Estimates in the Congo Basin From Validated SARAL/AltiKa Measurements
Discharge estimates were obtained through the following steps: (1) same as first proposed by [29], compute distributed discharge between 2013 and mid-2014 by means of the rain-discharge model of the Institute of Hydraulic Research of Brazil (MGB-IPH) fed with rain fields from the JAXA/NASA Tropical Rainfall Measuring Mission (TRMM) mission; (2) compute height-discharge power laws compliant with the Manning equation; and (3) apply these rating curves to the SARAL/AltiKa series to derive discharge series. An example of such a discharge series is presented in the left hand inset of Figure 17. At this location on the Ubangui River, the Envisat/SARAL orbit and the Jason-2 orbit form a crossover right over the Ubangui River. Thus, a long continuous discharge series could be derived by applying the SARAL/AltiKa rating curve altogether to the Envisat, Jason-2 and SARAL/AltiKa times series of water level. Overall, SARAL/AltiKa altimetry shows to be a powerful tool to estimate water levels with errors around 20 cm at more than 300 locations throughout the Congo basin, hence discharge in such a basin almost devoid of ground monitoring.
# High-Fidelity OC-Seislet Stacking Method for Low-SNR Seismic Data
## 3 Results
### Field Data Tests  
Finally, we use field marine seismic data to validate the effectiveness of the proposed method. This dataset contains abundant fault diffraction waves. Strong noise in the data affects the accuracy of velocity scanning, with the CMP data and NMOed gathers shown in Figure 12a,b. In Figure 12b, we can see that the large offset events after NMO are not horizontal enough; reflection waves and diffraction waves interfere with each other. Traditional NMO stacking suppresses the energy of the diffraction waves (Figure 13a). Although conventional DMO stacking (Figure 13b) retains diffraction information, it suffers from amplitude fidelity issues. Figure 13c shows the proposed high-order OC-seislet stacking results. Compared to conventional DMO stacking, the proposed method effectively stacks both reflection and diffraction waves simultaneously, significantly enhancing the imaging clarity of the diffraction signals at fault positions. It also provides a clearer relationship between deeper scatter waves and reflection waves, offering higher-fidelity stacking results.  
Figure 11: Comparison of different stacking methods. (**a**) Conventional DMO stacking section (SNR = 9.173 dB); (**b**) stacking section using high-order OC-seislet with thresholding (SNR = 17.17 dB).
# Land Cover Classification Using a KOMPSAT-3A Multi-Spectral Satellite Image
## 2 Materials and Methods
### Classificaiton and Accuracy Assessment  
The study area was classified into five land cover classes: agriculture, barren, built-up, forest and water (Table 2). These classes represent the most dominant and important land cover in the area. Selection of only five classes was due to ease of selection and an associated decrease in the error of misclassification. Well known classification methods MahD, MinD, ML and SVM were applied to the study area using Environment for Visualizing Images (ENVI) 5.1 software (Exelis Visual Information Solutions, Boulder, CO, USA). The training and validation data sets were common for each of the classification algorithms.  
In ENVI, all the classifications are made based on the probability or the Euclidian distance form pure pixels. ML is one of the most commonly supervised classifiers. It is based on statistics for each class in each band, which are normally distributed, and calculates the probability that a pixel or object belongs to each class and then assigns the pixel or object to the class with the highest probability [11]. The MinD uses the mean vectors of each endmember and calculates the Euclidean distance from each unknown pixel to the mean vector for each class. All pixels are classified to the nearest class unless a standard deviation or distance threshold is specified, in which case some pixels may be unclassified if they do not meet the selected criteria [11]. MahD classification is a direction-sensitive distance classifier that uses statistics for each class, and assumes all class covariances are equal and, therefore, it is a faster method [7]. All pixels are classified to the closest region of interest class unless a distance threshold is specified, in which case some pixels may be unclassified if they do not meet the threshold [11]. SVM is a non-parametric supervised classification algorithm that produces good classification results from complex and noisy data. The good accuracy is dependent on how well the process is trained.  
Finally, the User's Accuracy (UA), Producer's Accuracy (PA), Overall Accuracy (OA) and Kappa statistics were calculated from the error matrix to compare the performance of classified maps.
# An Improved Dual-Sorting NSGA-II Method for Optimal Radiation Shielding Design
## 3 Results and Discussion
### Optimization Efficiency Analysis  
#### 3.3.1 Population Superiority Metrics  
To quantitatively evaluate the optimization efficiency across different genetic algorithm configurations, this study introduces a normalized population superiority metric \(V(C_{t})\). For optimization problems with two objective functions, \(x\) and \(y\), the normalized population superiority parameter \(V(C_{t})\) is defined by Equation (5):  
\[V(C_{t})=\int_{x_{t,min}}^{x_{max}}\frac{F(x)}{G(x)\cdot(x_{max}-x_{min})}dx \tag{5}\]  
where \(y=F(x)\) represents the Pareto curve, which is constructed by selecting the optimal solutions from all simulation results and performing a nonlinear fit from (\(x_{min}\), \(y_{max}\)) to (\(x_{max}\), \(y_{min}\)). Conversely, \(y=G(x)\) represents the step curve, comprised of optimal and suboptimal solutions in population \(C_{t}\) with the minimum objective value \(x_{t,min}\). A schematic diagram of the population superiority parameter is shown in Figure 7.  
As illustrated in Figure 7, the region where \(f(x)>G(x)\) represents all shield solutions dominated by the population \(C_{t}\). The relative distance between these dominated solutions and the Pareto-optimal solutions is quantified by \(\frac{F(x)}{G(x)}\). As the optimal and suboptimal solutions within population \(C_{t}\) converge toward the Pareto-optimal set, whether through tighter clustering or expansion across a broader region, the population superiority metric \(V(C_{t})\) increases accordingly. This metric simultaneously characterizes the proximity of shield solutions within a given population to the optimal solution and the completeness of the optimal solution set, thereby providing a comprehensive assessment of population superiority.  
Figure 7: Schematic diagram for computing the normalized population superiority metric \(V(C_{t})\), which evaluates both the proximity and completeness of the solution set relative to the Pareto curve.
# Nonlinear Effect and MAI Impact on SAC-OCDMA System Based on 2D Multi-Diagonal Code and Laser Array
## 2 System Architecture  
Figure 1 describes the architecture of the proposed 2D-MD SAC-OCDMA system for LR-PON applications with four users. The user data is modulated with a Mach-Zehnder modulator (MZM). Then, the modulated user signal is multiplied by 2D-MD OCDMA code that is generated by the OCDMA encoder. The channel is represented through 50 km of SMF length followed by an optical amplifier (i.e., Erbium-Doped Fiber Amplifier (EDFA) with a gain equal to 10 dB), a Dispersion Compensating Fiber (DCF) and a second optical amplifier with the same gain. The reachability of the system can be extended up to 600 km using a block loops equal to 12 and a SMF length of 50 km. A DCF, with length equal to 10 km and a negative chromatic dispersion coefficient (i.e., \(\mathrm{D_{c}}=-80\) ps/nm/km), is used to fully compensate the chromatic dispersion caused by the SMF. At the reception side, a splitter is used to share the optical composite signal among all users. Then, a SAC-OCDMA decoder based on 2D-MD OCDMA code is used to separate the target user signal from other users' signals. Finally, a photo-detector PIN is employed to recover the user transmitted data.  
Figure 1: Proposed 2D-MD SAC-OCDMA system architecture (MZM: Mach-Zehnder modulator, EDFA: Erbium-doped fiber amplifier, SMF: Single mode Fiber, DCF: Dispersion compensating fiber).
## 5 Results and Discussion
### Water Depth Estimation
[PERSON] [15] estimated water depth from decades of radiometry images by bottom albedo signal where it successfully retrieved depth values with a coefficient of determination R\({}^{2}\) of 0.70 for a deep depth range to 20 m. The RMSE is equal to 10.15 m, 10.55 m, and 15.02 m for green, red, and NIR bands, respectively, of the SLSTR instrument, and NRMSE with in situ measured depths of Allaqi Khor which was used to validate the model is equal to 0.251, 0.213, and 0.315 of green, red, and NIR bands, respectively, while RMSE of the OLCI instrument bands is equal to 11.96 m, 14.5 m, and 16 m for green, red, and NIR bands, respectively, and NRMSE with in situ measured depths of Allaqi Khor is equal to 0.25, 0.34, and 0.296 of green, red, and NIR bands, respectively. Reflectance of the band with higher absorption (green) will decrease with increasing depth relatively faster than reflectance of the band with lower absorption (NIR), because the green and red bands have a good penetration in the water column [14]. The NIR band of the spectrum attenuates more rapidly than the shorter wave length in the visible region, and the green band was found more significant for depth derivation. The promising results obtained in terms of correlation indicate a good relationship between the measured reflectance and depth.
# Enhanced Plant Leaf Classification over a Large Number of Classes Using Machine Learning
## 5 Proposed Methodology
The results showed stable accuracy and precision across folds, further confirming the model's stability. In many real-world scenarios, datasets with many classes often exhibit class imbalance. The HT algorithm can effectively handle skewed class distributions by continuously updating based on the incoming stream and focusing on the most relevant features. The HT algorithm gives the best classification results in this research work with the NBC and MP algorithms. Algorithm 1 demonstrates the pseudocode of the HT algorithm below. ```
1:Input: S: Stream of instances; P: Desired probability; T: Tie-breaking threshold
2: Initialize an empty Hoeffding Tree (HT) with a single leaf
3:for each instance X in S do
4: Traverse HT to find the appropriate leaf L for X
5: Update statistics at leaf L with X
6:if the number of instances at L reaches MIN then
7: Compute Information Gain (G) for each attribute
8: Let \(X_{a}\) be the attribute with the highest G and \(X_{b}\) be the attribute with the second highest G
9: Compute desired probability (P) using the Hoeffding bound
10:if\((X_{a}-X_{b}>P)\) or (\(P<T\)) then
11: Split L using the best attribute \(X_{a}\)
12: Create new leaves for each branch of the split
13: Distribute the instances of L among the new leaves
14:else
15: Continue without splitting
16:endif
17:endif
18:endfor
19:return the trained Hoeffding Tree (HT)
```  
**Algorithm 1** Hoeffding Tree Algorithm  
The Hoeffding Tree (HT) algorithm was the most effective in our study with an accuracy of 89.92%, and this was due to its incremental learning approach and its capability to deal with high dimensionality and imbalance data. The dynamic construction of the decision tree and the selection of features in HT also eliminated overfitting and achieved high accuracy for such features as margin, texture, and shape. Naive Bayes Classifier (NBC) was the second best with an accuracy of 89.63%. While its probabilistic model is fast,the independent assumption of features somewhat constrained the model from capturing higher-order interactions, thus slightly lagging HT. Multilayer Perception (MLP) had a classification accuracy of 89.48%. Despite its capability to model non-linear relationships between the features of the leaves, the algorithm had a high tendency of overfitting especially with the class imbalance. However, the J48 algorithm gave the lowest accuracy of 57.87%. The static decision tree construction led to its overfitting, and the problem of handling imbalanced classes also led to its poor performance. This was because J48 could not generalize well in this complex, multi-class dataset, as observed earlier, which is the case with our dataset.
# Optimization of Clustering in Wireless Sensor Networks: Techniques and Protocols
## 6 Recent Advancements in Clustering Optimization Algorithms
### Meta-Heuristic
#### 6.1.2 Swarm Intelligence Algorithms
In the next phase, the BS runs the clustering algorithm to find the best K-CHs, then implements the routing algorithm to construct the optimal routing tree. TPSO-CR was evaluated in two different scenarios called WSN#1 (with homogenous nodes) and WSN#2 (with heterogeneous nodes). The performance was compared with LEACH, EHE-LEACH, EEHC, LEACH-C, PSO-C, and GA-C and found to achieve a smaller number of non-clustered nodes compared to the other protocols in both test scenarios. TPSO-CR also achieved the highest throughput and less energy consumption than LEACH, EEHC, and EHE-LEACH. However, it consumed comparable levels of energy to LEACH-C, PSO-C, and GA-C;
* PSO-ECHS: A PSO-based energy-efficient cluster head selection algorithm PSO-ECHS, presented by Ref. [30], is a PSO-based energy-efficient algorithm for CH selection implemented in two phases: the CH election phase and the cluster formation phase. PSO is implemented in the CH election phase based on nodes' residual energy and distances to the BS. The nodes first transmit their location and residual energy information to the BS for the execution of the PSO algorithm. During CH selection, PSO-ECHS considers each particle represented by the coordinates of the SNs to be chosen as CH to be an optimal position for the CHs. The PSO-ECHS was evaluated in three different scenarios varying the position of the BS. The first scenario placed the BS in the middle of the study field, while the second scenario placed the BS at the top right end of the study field, and in the last scenario, the BS was located outside the study field. The number of nodes was varied during these experiments, and the performance was compared with that of LEACH, E-LEACH, LEACH-C, PSO-C, and LDC. The performance of PSO-ECHS was better than that of the other protocols when considering energy utilization and extension of network lifetime. PSO-ECHS also achieved the highest rate of packet delivery to the BS in the evaluated scenarios using different numbers (i.e., 300, 400, 500, or 700) of nodes.
# Efficient Zero-Sequence Impedance Measurement in Autotransformers Using Low-Voltage Excitation
## 2 YNa Autotransformer Zero-Sequence Impedance Field Test Method
### Two-Phase Disconnection Test on the High-Voltage Side  
The wiring diagram for the two-phase disconnection test on the high-voltage side is shown in Figure 6 (phases B and C are the test phases). The test requires the application of a three-phase power supply on the low-voltage side and open circuiting of the B- and C-phase windings on the high-voltage side, with the A-phase winding short circuited to earth, which corresponds to the composite sequence network shown in Figure 7. Based on \(I_{1}=I_{2}=I_{0}\), it follows that the relationship of the phase sequence current on the low-voltage side A is:  
\[\dot{I}_{a}=3I_{0} \tag{9}\]  
Figure 5: Sequence network of the YNa autotransformer low-voltage One-Open-Phase test.  
Figure 6: YNa autotransformer test diagram for the high-voltage side Two-Open-Phase method.  
The sequence voltage can be solved according to Equation (7), and then the zero-sequence impedance of the autotransformer is:  
\[Z_{\rm{H}(L||g)-0}=\frac{V_{1}+V_{2}-2l_{1}Z_{\rm{HL}-1}}{l_{0}}=\frac{2V\cos \varphi-2l_{a}Z_{\rm{HL}-1}}{l_{a}} \tag{10}\]
## 3 Results and Discussion
### Using the Proposed STG Method to Detect and Repair Cycle Slips
The percentages of the correct cycle slip detection and repair in all cases for PRN 9 for the datasets with sampling intervals of 1, 5, and 10 s using the STG method were 100%, 100%, and 99.9% for detection, and 100%, 99.%, and 98.5% for repair, respectively. For PRN 22, the percentages were 100%, 100%, and 99.7% for detection, and 100%, 99.4%, and 97.6% for repair, respectively. As Table 5 shows, the performance for PRN 22 is slightly worse than for PRN 9, due to its low elevation angle. The results indicate that increasing the sampling interval degrades the performance, because some STG combinations (Equations (11)-(13)) are related to the small ionospheric residuals which increase with the sampling interval. Meanwhile, there are some special CSPs insensitive to the ionospheric-free Equation (10). Fortunately, even in that case, considering the fact that the ionospheric-free phase combinations are usually used as an observation for the precise orbit determination of the LEO satellites, the special undetected CSPs will have a slight influence on the orbit determination. Certainly, it is better to use data with a 1-s sampling interval for LEO satellites in order to detect and repair all cycle slips.
###### Abstract  
Background: There are a few biomechanical studies that describe posterior fixation methods with pedicle screws (PS) and lateral mass screws (LMS); the combination of both screw types and their effect on an allograft spacer in a surgically treated cervical segment is unknown. Methods: Finite element model (FEM) analyses were used to investigate the effects of a hybrid technique using posterior PS and LMS. Stress distribution and subsidence risk from a combination of screws under hybrid motion control conditions, including flexion, extension, axial rotation, and lateral bending, were investigated to evaluate the biomechanical characteristics of different six-screw combinations. Findings: The load sharing on the allograft spacer in flexion mode was highest in the LMS model (74.6%) and lowest in the PS model (35.1%). The likelihood of subsidence of allograft spacer on C6 was highest in the screws from the distal LMS (type 5) model during flexion and extension (4.902 MPa, 30.1% and 2.189 MPa, 13.4%). In lateral bending, the left unilateral LMS (type 4) model screws on C5 (3.726 MPa, 22.9%) and C6 (2.994 MPa, 18.4%) yielded the greatest subsidence risks, because the lateral bending forces were supported by the LMS. In counterclockwise axial rotation, the left unilateral LMS (type 4) model screws on C5 (3.092 MPa, 19.0%) and C6 (3.076 MPa, 18.9%) demonstrated the highest subsidence risks. Conclusion: The asymmetrical ipsilateral use of LMS and posterior PS in lateral bending and axial rotation demonstrated the lowest stability and greatest subsidence risk. We recommend bilateral symmetrical insertion of LMS or posterior PS and posterior PS on distal vertebrae for increased stability and reduced risk of allograft spacer subsidence.  
cervical spine surgery; allograft spacer; lateral mass; pedicle screws; finite element model +
Footnote †: journal: Applied Sciences
## 2 Problem Modeling  
### Scenario Description  
In the confrontation scenario of Figure 1, we assume that the uplink between the satellite communication transmitting terminal and the receiving terminal includes multiple information transmission channels, and the communication parties use the multiple channels for information transmission (there may be idle channels). The jammer implements the technology of tracking interference or barraging interference on the communication parties through effective detection, but due to the limitation of the total interference power, it cannot barrage all the channels of the communication parties at the same time. The schematic diagram of multi-channel power allocation under the confrontation scenario is shown in Figure 2.