esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Analysis  
### Dataset  
We used the IQ-OTHNCCD Lung Cancer Dataset, collected for three months in the fall of 2019, to evaluate our image denoising hybrid model. It was presented by [PERSON] et al. [55] and can be downloaded from: [[https://www.kaggle.com/code/aditya](https://www.kaggle.com/code/aditya)]([https://www.kaggle.com/code/aditya](https://www.kaggle.com/code/aditya)) mahimkar/lung-cancer-prediction-on-image-data, accessed on 17 December 2022. We used 1294 grayscale images of the IQ-OTHNCCD dataset, classified into normal, benign, and malignant. Each image was resized to 512 pixels wide \(\times\) 512 pixels high for training and validation of our model. A total of 1035 images were used for training, and 259 images were used for testing. Among these images, 1096 were found to be affected by Gaussian blur noise, while 198 contained salt and pepper noise. In this work, the focus was primarily to address the dominant noise type, Gaussian blur noise.  
A systematic approach was adopted to comprehensively assess the denoising performance and account for potential variations. The Gaussian blur noise was added to the images at different levels, ranging from 5% to 60% noise intensity levels. This range of noise intensity allowed for testing the denoising method's effectiveness under various noise levels. The addition of noise symmetrically from 5% through to 60% serves several purposes, including assessing the error margin even when noise levels deviate from the baseline and realism in real-world image conditions where noise levels vary. This approach ensures noise levels are tested against noise levels that represent a broad spectrum at optimal noise levels. The Gaussian blur noise was added by setting the standard deviation of the Gaussian noise distribution to 0.15. For training purposes, each image was converted into patch sizes of 45 \(\times\) 45, resulting in 2200 patches per image.  
The choice of dataset was made considering clinical realism and relevance reflecting typical CT image characteristics encountered in a real-world setting. The fact that the dataset was collected from various specialist hospitals over three months in the fall of 2019 relates to the current challenges faced by CT images. Furthermore, the dataset includes CT scans of patients diagnosed with lung cancer in different stages and healthy subjects, providing a real-world view.
## 3 Optimizing the UCSP Using POGA-DP
### Time Slot Scheduling Based on Genetic Algorithms
#### 3.2.3 The Forced Mutation Operation with the Repair Mechanism  
We perform mutation operations based on a specific probability threshold. However, since most of the constraints in the UCSP are interrelated, many mutation operations are infeasible due to constraint violations. Therefore, forced mutation operations involve a repair mechanism to ensure that invalid solutions are not generated in the process. The repair mechanism works by randomly relocating conflicting courses to non-conflicting positions. As shown in Figure 3, consider a UCSP involving two classes, \(e_{1}\) and \(e_{2}\), with multiple  
Figure 2: Course swap diagram for administrative classes.  
course schedules within specified time slots. If a mutation changes the time slot of course \(c_{2}\) for \(e_{1}\) from slot 4 to slot 3, this will result in teacher \(t_{2}\) having to teach both \(c_{2}\) and \(c_{4}\) in the same time slot, which clearly violates the constraints. The repair mechanism involves randomly relocating the conflicting course \(c_{2}\) to other time slots until the conflict is resolved.
## 3 Results  
### Changes in IC Dimensions and Tooth-Canal Relationship before and after MARPE Treatment  
Samples in which the apices of the maxillary central incisors were located more superior to the oral opening of the incisive canal are analyzed in Table 2 (\(n=20\)).  
Medilateral and labiopalatal widths of the IC were significantly increased in all three levels after MARPE treatment (\(p<0.01\)), and the amount of increase was greater in mediolateral direction than labiopalalal direction. Anteriorposterior distance from the mesial point of maxillary central incisors to the anterior margin of the IC was significantly decreased only in L1 level (\(p<0.05\)).  
Ten samples where the root apex of maxillary central incisors was vertically separated from the IC are analyzed in Table 3 (\(n=10\)). Similar to the other 20 samples, the mediolateral and labiopalatal width of the IC significantly increased after MARPE treatment (\(p<0.05\)), except for IC LP width in the L1 level.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Measurements (\(n=20\))**} & \multicolumn{2}{c}{**L1**} & \multicolumn{2}{c}{**L2**} & \multicolumn{2}{c}{**L3**} \\ \cline{2-10}  & **T0** & **T1** & \(p\) **Value** & **T0** & **T1** & \(p\) **Value** & **T0** & **T1** & \(p\) **Value** \\ \hline IC ML width & 3.64 \(\pm\) 1.47 & 5.82 \(\pm\) 1.04 & 0.000 *** & 3.71 \(\pm\) 1.04 & 5.73 \(\pm\) 1.46 & 0.000 ***+ & 3.45 \(\pm\) 0.84 & 5.77 \(\pm\) 1.27 & 0.000 *** \\ \hline IC LP width & 3.27 \(\pm\) 0.69 & 3.60 \(\pm\) 0.73 & 0.008 ** & 3.09 \(\pm\) 0.65 & 3.61 \(\pm\) 0.50 & 0.001 ** & 2.90 \(\pm\) 0.57 & 3.38 \(\pm\) 0.62 & 0.004 ** \\ \hline \#11 Rm-Cat & 4.48 \(\pm\) 1.34 & 4.03 \(\pm\) 1.17 & 0.008 ** & 4.13 \(\pm\) 1.47 & 3.95 \(\pm\) 1.50 & 0.218 & 4.11 \(\pm\) 1.47 & 3.94 \(\pm\) 1.45 & 0.299 \\ \hline \#21 Rm-Cat & 4.53 \(\pm\) 1.25 & 4.02 \(\pm\) 1.15 & 0.013 * & 4.18 \(\pm\) 1.54 & 3.90 \(\pm\) 1.31 & 0.129 & 4.07 \(\pm\) 1.55 & 3.87 \(\pm\) 1.44 & 0.182 \\ \hline \multicolumn{10}{c}{\({}^{\star}p<0.05\), ** \(p<0.01\), *** \(p<0.001\). \({}^{\dagger}\) Wilcoxon signed-rank test.} \\ \end{tabular}
\end{table}
Table 2: Changes in IC dimensions and tooth–canal relationship after MARPE treatment (mean \(\pm\) standard deviation).  
Figure 4: Axial, sagittal, coronal view and the result of volume rendering of the IC on pretreatment CBCT (red).  
Change in the Positional Relationship between the Maximillary Central Incisors after MARPE Treatment (Table 4)  
Mediolateral distance between mesial points of maxillary central incisors was significantly increased in all three levels (\(p<0.001\)). The distance between the root apex of the maxillary central incisors significantly increased after MARPE (\(p<0.001\)), but the distance between the crown tips of the maxillary central incisors did not significantly increase, even after MARPE treatment (\(p>0.05\)).
## 4 Proposed Scheme
### Pseudonym ID and Domain Key Updating Phase
#### 4.6.1 Batch Leave  
Once a vehicle leaves domain \(D_{y}\), the nearest TA will update the pseudonym ID and domain key. For example, if four vehicles \(v_{2}\), \(v_{4}\), \(v_{6}\), and \(v_{8}\) leave domain \(D_{y}\), the TA executes the following steps:  
* Subtract \(vbs_{2}\), \(vbs_{4}\), \(vbs_{6}\), and \(vbs_{8}\) from \(\mu\) as follow \[\mu^{-}=\mu-(vbs_{2}+vbs_{4}+vbs_{6}+vbs_{8})\] (7)
* TA should select an updated domain key \(s_{d}^{-}\), and it must be multiplied by \(\mu^{-}\) to form the message from rekeying. \[\gamma_{d}^{-}=s_{d}^{-}\times\mu^{-}\] (8)
* TA randomly picks a value \(r_{i}^{-}\in Z_{q}^{*}\) and computes the corresponding \(R_{i}^{-}=r_{i}^{-}P\) for each existing vehicle in the domain. It then computes a pseudonym ID \(AID_{i}^{-}\) = \(ID_{i}\oplus H_{1}(s||R_{i}^{-})\) for each vehicle \(V_{i}^{-}\) in the domain.
* TA deliveries the pseudonym ID and updated domain key as a broadcast message. Once existing vehicles in the domain receive an updated domain key, \(s_{d}^{-}\) is obtained by performing the modulo operation once. Vehicle \(V_{i}\) cannot disclose the newly updated domain key \(s_{d}^{-}\) since its secret key is not in \(\mu\). Hence, once \"n\" vehicles want to leave the domain, TA updates the domain key by executing (n-1) additions and one subtraction operation.  
Hence, once \"\(n\)\" vehicles want to leave the domain, \((n-1)\) additions and one subtraction operation are executed by the TA to update the domain key.
# Application of the Reed-Solomon Algorithm as a Remote Sensing Data Fusion Tool for Land Use Studies
## 1 Introduction
### Image Data Fusion
The review of the classification methods is beyond the scope of this study, however interesting approaches related to data fusion are also considered in the context of the accuracy of classification results [70]. However, the multi-resolution segmentation (MRS) algorithm (implemented in eCognition(r), Trimble Geospatial Imaging software) also needs to be mentioned. However, several problems remain and concern the lack of full automation and the need for expert knowledge to define the classification rules [65]. Remote sensing images need approaches to reduce the high dimensionality of data. There are several methods and algorithms used to do this. Some of them (mentioned above) are applied at the preparatory stage, i.e., the data fusion step. Other methods, related to image classification, operate on raw data or omit this first step. The classical PCA method is nowadays replaced by other non-linear dimensionality reduction methods, and manifold and dictionary learning algorithms, especially deep learning using convolutional neural network trained with an unsupervised algorithm promoting feature sparsity [17].
## 1 Introduction  
In recent years, due to the demand for cost reduction and increased efficiency, as well as the promotion of intelligent warehousing technology, an increasing number of factories have begun adopting AGVs (automated guided vehicles) as part of their industrial logistics systems. An AGV is an unmanned transport device guided by a central system, which has a certain degree of autonomous decision-making [1, 2] and LIDAR detection capabilities [3, 4]. As a crucial component of the intelligent warehouse system, improving the pathfinding and coordination capabilities of AGVs is key to enhancing industrial logistics efficiency within the workshop.  
The multi-AGV pathfinding problem aims to determine a set of conflict-free paths for multiple AGVs while minimizing the overall cost of the path. This problem falls into the category of multi-agent pathfinding (MAPF) [5]. The current solutions to the MAPF problem can be broadly classified into centralized and distributed approaches.
# A Study on Sensitive Bands of EEG Data under Different Mental Workloads
## 4 Significance Analysis of Mental Workload and Feature Based on SVM Classifier  
### Data Preprocessing  
In order to ensure the validity of the experimental data, only the experimental data of 10 subjects is left for subsequent analysis according to the two criteria of \"high performance\" and \"fewer artifacts\" [30]. The \"high performance\" criterion means that the operational performance accuracy rate should be greater than 0.90. \"Fewer artifacts\" means that the artifacts in the original EEG data cannot exceed 5 min. EEG signals are very weak, and EMG, EOG, and ECG can interfere with EEG signals, so samples with artifacts greater than 5 min are removed. After that, the ICA is used to remove the EOG artifacts from the valid 10 subjects, and then filter and reconstruct the reference.  
ICA-Based EEG Signal EOG Elimination  
The applicable conditions of ICA are: (1) There is no time delay for the linear combination of signals; (2) the time course of the source is independent; and (3) the number of sources is smaller than or equal to the number of sampling points. EEG acquisition can be regarded as linear and immediate, by satisfying condition (1). Condition (2) is also reasonable, because it can be considered that EEG,ECG, EEG, etc., have different sources. Condition (3) is a bit fuzzy because we do not know the number of sources that are statistically independent of scalp potential, but a large number of ICA algorithm simulations can effectively separate a large number of time-dependent sources of EEG or brain topography.  
We input the EEG signal (\(x\)) as the data sampled from different electrodes and \(x=As\). By the fast fixed-point ICA algorithm we can find \(A\), \(s\). Then the artifacts can be eliminated to get \(s^{\prime}\).  
\[x^{\prime}=W^{-1}s^{\prime} \tag{16}\]  
Through (16), the EEG signal \(x^{\prime}\) can be obtained after removing the eye electricity.  
Figure 5 shows the EEG topographic map of the subject 5 under low load, moderate load, and overload before the artifact is removed.  
Figure 6 shows the EEG topographic map of the subject 5 under low load, moderate load, and overload after removing the artifact through ICA.  
Figure 7 shows the EEG topographic map of the subject 12 under low load, moderate load, and overload before using ICA to remove artifacts.  
Figure 5: Subject 5 uses ICA to remove artifacts before EEG topographic map.  
Figure 6: Subject 5 uses ICA to remove artifacts after EEG topographic map.  
Figure 8 shows the EEG topographic map of the energy of the subject 12 under low load, moderate load, and overload after using the ICA to remove artifacts.  
It can be seen from Figures 6 and 8 that the individual brain activities are distinct under different load conditions.
# Spiral Mobility Based on Optimized Clustering for Optimal Data Extraction in WSNs
## 1 Introduction
Furthermore, during the critical data transmission, sensor nodes dissipate more energy due to the long-distance transmission. The grid mobility pattern is an enhancement of random mobility; though grid mobility provides promising results in small-scale networks, the problem with this pattern of sensor nodes is that is has to wait for a longer period for the mobile sink in large networks [14]. The mobile sink covers the whole network area and moves on a defined mobility pattern, which results in huge data loss in farther areas. Furthermore, in the case of critical data reporting, the grid mobility pattern is not a suitable solution. In this paper, we develop a novel optimized clustering framework to investigate the effects of sink mobility. Furthermore, we propose two routing protocols based on the spiral mobility pattern: Spiral Mobility based on Optimized Clustering (SMOC) for optimal data extraction and Multiple sink based on SMOC (M-SMOC) for large-scale WSNs. In SMOC, a single mobile sink moves in a spiral pattern over the sensing field to collect data from sensor nodes and CHs, while in M-SMOC, four mobile sinks move in the spiral pattern over the sensing field to cover the whole network area and avoid the delay in long-range communication with CHs. The major contributions of our proposed protocols are as follows. * The proposed SMOC and M-SMOC routing protocols allow each node to communicate directly with the sink for the critical data reporting, if the distance between the node and CH is greater than the distance between the node and sink. The proposed spiral mobility pattern for the sink covers the whole network area and resolves the energy hole problem. * The proposed protocols are characterized by the controlled mobility pattern in an energy-efficient manner, which makes proposed protocols more preferable in a huge range of WSN applications as compared to existing routing protocols [15; 16; 17; 18; 19]. * We have simulated our proposed models and compared them with state-of-the-art routing protocols, which offer mobility and achieve encouraging results. We performed extensive simulations in order to analyze our proposed models and included some satisfactory outcomes in this paper. The rest of the paper is organized as follows. Section 2 contains a brief literature review. System models and the problem statement are explained in Section 3. Section 4 describes the network design and methodology of spiral mobility for single and multiple sinks. The proposed model and technical details of routing techniques are given in Section 5. Experimental methodology and simulation results are presented in Section 6. The performance evaluation is discussed in Section 7. Conclusion and future work are given in Section 8.
# Edge Server Deployment Strategy Based on Queueing Search Meta-Heuristic Algorithm
## 5 Experimental Assessment
### Analysis of Simulation Results  
In order to evaluate the performance of the QSA algorithm in edge deployment environments, this paper conducts multiple simulation experiments to capture the randomness of user and base station locations. The main performance metric of the QSA algorithm is still the hop count; the smaller its value, the better the global search capability is, and the lower the end-to-end latency of the user request.  
The initial populations for all algorithms were generated using the numpy.random 2.1.0 library in Python. To ensure the repeatability of the experiments and fairness of the comparisons, we fixed the sequence of random seeds to ensure that all algorithms used the same set of initial populations. For each experiment, we generated five different initial populations using different seeds and applied the same set of seeds to all algorithms in the comparative analysis. This approach allowed us to focus on the effect of algorithmic differences while controlling for initial population variability.  
To ensure the statistical significance of the experimental results, each experimental configuration was repeated for 10 runs. Each run used a different initial population that was generated by a fixed sequence of random seeds. By increasing the number of repetitions, we were able to more accurately assess the performance of the algorithm and reduce the impact of randomness on the results.  
All experimental results are averaged over 10 independent runs, and error bars indicate standard deviation. This treatment ensures the reliability of the results and provides a clear description of the variation in algorithm performance. Specifically, each experimental configuration uses 10 different seeds, each corresponding to one independent experimental run. This approach ensures that different algorithms are compared under the same initial conditions, thus eliminating the effect of initial seed differences on the results.  
To validate the superior performance of QSA, it is compared with five classical meta-heuristic algorithms: GA [24], SA [25], AEO [26], GWO [27], and PSO [28], as well as two newer metaheuristic algorithms, POA [29] and WarSO [30].
## 2 Materials and Methods
### Prediction Models and Accuracy Assessment
#### 2.6.1 Partial Least Squares Regression  
PLS regression is the most common linear regression used to predict soil properties. Developed by [PERSON] [92], it maximizes the variance of spectral variables by reducing the original dimension of the spectra into latent variables. It extracts useful components that are strongly correlated with the dependent variables and overcomes problems of multicollinearity between spectral variables [93]. PLS regression performs better than other linear regression methods because of the stability of its latent variables. However, it is sensitive to outliers in the dataset [94] and forces predictions towards the center of the calibration dataset. Thus, for samples with high leverage, prediction uncertainty increases [95]. Non-linear iterative PLS (NIPALS) and statistically inspired modified PLS are the two most common optimization algorithms of PLS regression. In this study, we used the NIPALS algorithm implemented in the scikit-learn 1.0.2 package (Python 3.7.4), which aims to linearize models that have non-linear parameters [92; 96]. The optimal number of components was selected using both the coefficient of determination (R\({}^{2}\)) and the RMSE of a 5-fold cross-validation (RMSECV) and relying on the grid search method (Table A2 in Appendix B). This method allows models parameters to be adjusted by searching a grid of all possible combinations and evaluating them using cross-validation (Figure 3) [97; 98].  
Figure 2: Flowchart of data organization and developed approach for combining Sentinel-2 bands with Sentinel-2 indices, Sentinel-1 indices, Sentinel-1 soil moisture, and laboratory spectral indices. Colors in tables of the data indicate the available dates for each soil sample. See Table 2 for definitions of the spectral indices. PLS: partial least squares, RF: random forest, DNN: Deep neural network, R\({}^{2}\): coefficient of determination, RMSE: root mean square error, RPD: ratio of performance to deviation, RPIQ: ratio of performance to interquartile distance.
# Current Status and Future Developments of Micromegas Detectors for Physics and Applications
## 4 Micromegas in Atlas for HL-LHC Upgrade
These modules are the largest Micromegas detectors built up to now, with a detection area of 2 to 3 m\({}^{2}\) per layer, thus, 8 to 12 m\({}^{2}\) per module. To avoid deformations of the outer panels due to the gas over pressure inside the quadruplet, interconnections link both external sides of the outer drift panels. A general view of an assembled module is shown in Figure 11. The drift height is given by precision frames at the edge of each gas gap, which are closed using a soft ethylene propylene diene monomer rubber (EPDM) o-ring. Figure 11: Section of a Micromegas quadruplet with details of the internal components on the edges, at the level of the interconnections and at the junction between PCBs. Together with the mesh, RO boards define the amplification gap of the detector, and thus the quality of materials and surfaces are critical for a detector working in nominal conditions in terms of the gain, efficiency, and position resolution. RO boards are manufactured in industry since specific materials and large capacities are needed to produce them. RO boards are made by pressing at high pressure a stack of different layers (printed circuit board, glue film, and Kapton(r) foil) and adding on top a network of insulating pillars by etching in a Pyralux(r) foil. The dimensions and values of interest are indicated in Table 1 and on Figure 12.  
Electronic amplification occurs between the mesh and the plane of resistive strips. Then, the signal is read on copper strips by capacitive transmission through the kapton (polyimide) foil. Pitches of resistive and copper strips are identical and aligned on each other. The resistivity of carbon resistive strips is about 800 k\(\Omega\)/sq, which allows the Micromegas detector to sustain the high-rate particle environment. This structure was studied during the MAMMA (Muon ATLAS MicroMegas Activity) R&D program [57; 58] where the adaptation of Micromegas detectors to the HL-LHC environment was explored. A resolution of 73 \(\upmu\)m was measured during a 120 GeV protons beam test with eight detector planes along the beamline. Each operated with argon (93%)/CO\({}_{2}\) (7%) gas, a drift field of 600 V/cm, and an amplification voltage of 500 V applied to the mesh [15; 16].
## 4 Results and Discussion  
The procedure described in Section 3 for calculating the optimal operating maps was implemented in MATLAB, after having carried out the dimensionless approach reported in Section 2. The following results were computed by considering a total vehicle mass equal to \(m=1750\) kg in plain (\(\alpha=0\) rad). The analyzed vehicle speed ranges from 5 to 200 km/h.