esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method
### Two-View-Based Deep Model
#### 3.3.4. Channel Attention Block  
Not all feature channels of a feature map are equally important for the current task. Some channels may contain highly informative features that are directly relevant to the task, while others may contain less informative or redundant features that can potentially distract the network from focusing on the relevant information.  
To address this, we incorporate a squeeze-and-excitation block after the last feature map generated by the backbone model. By doing so, we enable the model to adaptively weigh the importance of each channel, giving more attention to the informative channels and suppressing the less relevant ones. This dynamic attention mechanism assists the network in making better-informed decisions during the classification process. Figure 3 shows the channel attention block used.  
The channel attention block takes the input feature map \(x\in\mathbb{R}^{H\times W\times C}\), where \(C\) is the number of channels, \(H\) is the height, and \(W\) is the width. First, it applies a squeeze operation:  
\[g=GlobalAveragePool(x) \tag{1}\]  
\[f_{se}=ReLU\left(W_{1}.g+b_{1}\right) \tag{2}\]  
where \(g\in\mathbb{R}^{C}\), \(f_{se}\in\mathbb{R}^{C/c^{\prime}}\), \(W_{1}\) and \(b_{1}\) are the weights and biases of the FC layer, which squeezes \(g\) to \(f_{se}\) to incorporate interdependencies. Then, it applies the excitation operation:  
\[w=\sigma(W_{2}\cdot f_{se}+b_{2}) \tag{3}\]  
Figure 3: Channel attention block.  
Figure 2: This diagram illustrates ConvNeXt-L, our backbone model, highlighting customized layers (red boundaries and shading) and key features in the penultimate layer (green box) for our mammography image classification task.  
where \(W_{2}\) and \(b_{2}\) are the weights and biases of the FC layer that adaptively recalibrate \(f_{se}\), and \(w=[\alpha_{1}\,\ \alpha_{2}\,\ \ldots,\ \alpha_{C}\ ]^{T}\), where \(\alpha_{c}\) signifies the channel-wise excitation factor for channel \(c\). Once the channel-wise excitation factors \(w\) are computed, they are used to attend to the corresponding channels:  
\[Z=w\bigcirc\ \ x=[\ z_{1}\,z_{2}\,\ \ldots,\ z_{C}] \tag{4}\]
## 3 Results and Discussion
### Stability Analysis of Ionanofluids  
#### 3.5.1 Visual Inspection Analysis  
The four prepared ionic liquid-based MXene nanofluids' visual appearance over 7 days after preparation is presented in Table 1. Fluids from day 5 to day 7 showed dispersed nanoparticles over the inspection time. However, slight sedimentation at the bottom of the vial could be seen (day 5, day 6, day 7) due to the aggregation of nanoparticles that played an important role in maintaining the nanofluids' stability. Besides, the black color was becoming progressively transparent, which was linked to nanofluid stability. Moreover, the samples that displayed a dark black color over time were the most stable ionanofluids. Samples on days 5, 6, and 7 indicated evidence of slight aggregation and sedimentation. The ionic liquid-based MXene nanofluid presented good dispersion for the first four days, even without any surfactants. This may be attributed to the addition of [EMIM][OSO4] ionic liquids with a significant amount of surface charges resulting in electrostatic repulsion that can increase the nanofluids' stability.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Time**} & \multicolumn{5}{c}{**Concentration (Wt\%)**} \\ \cline{2-5}  & **0.1** & **0.2** & **0.3** & **0.4** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Visual inspection of the prepared samples.  
Figure 13: Electrical double-layer formation around the MXene nanoparticle.
# Current-Signal-Based Fault Diagnosis of Railway Point Machines Using Machine Learning
## 3 Results
### Determining the Optimal Model  
Hyperparameter tuning using six machine learning algorithms (LR, kNN, SVM, DT, RF, and ANN) was conducted in this study to determine the parameters of each machine learning method that yielded the best validation accuracy. The machine learning parameters that were investigated in grid search 5-fold cross-validation were as follows: the parameter in the LR algorithm was the C value (range 1-10); the parameter in the kNN algorithm was the number of neighbors (range 1-10); the parameters in the SVM algorithm were the C value (range 1-10), gamma value (range 0.1-1), and kernel type (linear or rbf); the parameters in the DT algorithm were the maximum depth of the tree (range 1-5) and the criterion function (gini or entropy); the parameters in the RF algorithm were the number of trees in the forest (range 1-5) and the criterion function (gini or entropy); and the parameters in the ANN were the number of hidden layers, neurons, and epochs, as well as the batch size. Because of the dataset's shorter dimension, the proposed ANN technique employed only one hidden layer using the feedforward propagation technique. Furthermore, the input layer contained 10 nodes, the hidden layer contained 7 nodes, and the output layer contained 3 nodes. The ANN model employed a batch size of 32 values, which is the optimum size for faster convergence compared to a large batch size [4]. To find the optimal number of epochs, we monitored the validation loss every 30 epochs using the early stopping method. The optimal number of epochs found in this study was 1414. Table 3 presents the grid search 5-fold cross-validation results, including the training and validation accuracy values explained in Section 2.3. Table 3 also describes the best parameters in each ML model.  
The six ML models achieved more than 80% validation accuracy. The kNN algorithm yielded the lowest validation accuracy, whereas the DT and RF algorithms exhibited the best performance with 100% accuracy. These parameter values were implemented to train the best models for each ML technique and were tested using the confusion matrix approach.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Feature** & \(p\)**-Value** & **Relevance** \\ \hline Length & \(6.48\times 10^{-74}\) & True \\ Sum values & \(2.08\times 10^{-63}\) & True \\ Mean & \(8.47\times 10^{-59}\) & True \\ Median & \(3.11\times 10^{-58}\) & True \\ Root mean square & \(1.04\times 10^{-56}\) & True \\ Kurtosis & \(3.07\times 10^{-42}\) & True \\ Skewness & \(4.44\times 10^{-42}\) & True \\ Minimum & \(1.14\times 10^{-39}\) & True \\ Standard deviation & \(8.53\times 10^{-8}\) & True \\ Variance & \(8.53\times 10^{-8}\) & True \\ Maximum & \(6.40\times 10^{-1}\) & False \\ \hline \hline \end{tabular}
\end{table}
Table 2: The selection of time-domain features.
## 3 Results
### User Experiments
#### 3.2.1 Methodology  
The user experiments follow a structured methodology to ensure consistent and reliable data collection through user surveys. The experiment is divided into two phases: pre-demo and post-demo:  
1. Pre-demo: The patients are introduced to the experimental setup and informed about the robot and its technical features, but not about the specific tasks and implemen  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(y_{m}\) **(m)** & \(x_{m}\) **(m)** & \(dx_{m}\) **(m)** & \(t_{e}\) **(s)** \\ \hline \(0.01\pm 0.00\) & \(0.89\pm 0.02\) & \(0\pm 0\) & \(39.5\pm 1.9\) \\ \(0.06\pm 0.01\) & \(0.74\pm 0.01\) & \(0.09\pm 0.01\) & \(51.5\pm 0.8\) \\ \(0.01\pm 0.01\) & \(1.02\pm 0.02\) & \(-0.08\pm 0.01\) & \(46.6\pm 1.5\) \\ \hline \(0.28\pm 0.00\) & \(0.85\pm 0.01\) & \(0\pm 0\) & \(43.1\pm 1.6\) \\ \(0.36\pm 0.03\) & \(0.75\pm 0.03\) & \(0.06\pm 0.02\) & \(59.5\pm 6.4\) \\ \(0.30\pm 0.01\) & \(0.97\pm 0.1\) & \(-0.08\pm 0.01\) & \(50.4\pm 1.5\) \\ \hline \(-0.23\pm 0.00\) & \(0.83\pm 0.01\) & \(0\pm 0\) & \(46.9\pm 0.8\) \\ \(-0.25\pm 0.03\) & \(0.76\pm 0.01\) & \(0.07\pm 0.01\) & \(55.8\pm 2.9\) \\ \(-0.22\pm 0.02\) & \(0.92\pm 0.02\) & \(-0.06\pm 0.01\) & \(55.9\pm 5.5\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Execution results of the pick-from-the-table routine. \(x_{m}\) and \(y_{m}\) are the mean initial coordinates with respect to the robot, \(dx_{m}\) is the robot’s travel distance to match the gripping distance, and \(t_{e}\) is the execution time.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Origin** & **Destination** & \(t_{m}\) **(s)** & \(\sigma\) **(s)** \\ \hline table & user & 106.73 & 3.72 \\ shelf & user & 121.52 & 2.88 \\ user & table & 91.36 & 2.26 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Execution results of the routine of delivering the container. The _origin_ is the POI where the container is located, the _destination_ is where to deliver the container, \(t_{e}\) is the mean execution time, and \(\sigma\) is the standard deviation of the execution time.  
tations it can perform, e.g., \"the robot has a camera\". They are then asked about the potential for assistance.
2. Post-demo: The patients act as users of the robot's assistance and are asked to command the desired assistance routines from their own mobile phones. They are then surveyed about their satisfaction, the usefulness of the robot, the attractiveness of the user interface, and their future needs and suggestions for improvements.
## 2 Preliminaries
### Best-Worst Method  
The best-worst Method (BWM) was improved based on AHP by [PERSON] (2015) in 2015, changing the pairwise comparison from AHP into the comparison between the remaining criteria and the best-worst criteria. BWM has been used to rank criteria by some scholars. [PERSON] et al. [PERSON] et al. (2016) presented a supplier selection life cycle approach, which integrated qualitative, quantitative, traditional business, and environmental criteria using BWM. [PERSON] et al. [PERSON] et al. (2017) used BWM to rank the criteria of green innovation for supplier selection. In addition, [PERSON] et al. (2018) applied BWM in order to determine the weights and priority of the identified criteria.  
Firstly, the best important and least important criteria are identified by the DMs. Then, the preference of the best and the worst criterion over all the other criteria is determined using a number between 1 and 9. Let \(A_{B}\) (\(A_{W}\)) be the sets of comparison values between the best (least) important criteria and the other criteria. The vectors would be \(A_{B}=(a_{B1},a_{B2},\cdots,a_{Bn})\) and \(A_{W}=(a_{1W},a_{2W},\cdots,a_{nW})\). It is clear that \(a_{BB}=a_{\rm WW}=1\). The calculating function is as follows:  
\[a_{ij}=a_{ik}*a_{kj},\text{ for all }k\
eq i,j, \tag{2}\]  
\[a_{ji}=\frac{1}{a_{ij}}. \tag{3}\]  
Finally, according to the vectors, we can get the best-others and the worst-others comparative matrices. The optimal and worst comparison matrices are standardized by Equation (4).  
\[\overline{A}=\sqrt{A_{B}*A_{W}} \tag{4}\]  
In this paper, this method was employed in order to calculate the priority weighting of the areas. The advantages of the BWM are the following:  
1. Pairwise comparison needs to do \(n^{2}\) comparisons, and the BWM only needs \(2n\) comparisons, which greatly simplifies the weighting steps.
2. By reducing the amount of data, the results are consistent and reliable.
## 4 Results
The heatmap visualizes the quantum channel error rate as the detector noise increases (from 5 Hz to 50 Hz) under various atmospheric conditions: clear day, cloudy, foggy, and night. The data reveal that foggy conditions exacerbate error rates due to scattering effects, whereas night-time operation minimizes errors owing to lower background interference. Darker regions indicate higher error rates, emphasizing the system’s sensitivity to both hardware noise and environmental variability. system's resilience to anomalous interference (e.g., unauthorized signal attempts), thereby improving reliability metrics by 20%.
# Verdet Constant of Magneto-Active Materials Developed for High-Power Faraday Devices
## 2 Characterization of the Verdet Constant as a Function of Wavelength and Temperature
### NIR-MIR Region (\(\lambda>\) 1100 nm)
This can be done either by moving the element to a stronger/weaker magnetic field (if a permanent magnetic system is used) or by changing the input current to the electromagnet. The majority of materials used for the NIR wavelengths contain Tb\({}^{3+}\) ions (see the Table 2), which have strong absorption in the MIR, and, therefore, it is needed to search for materials with different compositions. The fluorides CeF\({}_{3}\), PrF\({}_{3}\) and LiREF\({}_{4}\) (RE = Dy, Ho or Er) [59] have several transparency windows in the MIR; nevertheless, their magneto-optical characteristics in this region are unknown. The only exception is the CeF\({}_{3}\) crystal, for which the Verdet constant was reported up to 1950 nm in Ref. [46]. The CeAlO\({}_{3}\) material [151] is also transparent up to \(\sim\)2500 nm and its Verdet constant is about twice as high as the one of CeF\({}_{3}\) at the 633 and 1064 nm wavelengths. Apart from these cerium-based materials, Verdet constant investigations of (Dy\({}_{\text{x}}\)Y\({}_{0.95-\text{x}}\)La\({}_{0.05}\))\({}_{2}\)O\({}_{3}\) (with variable doping X = 0.7, 0.8 and 0.9) ceramics [129; 130], EuF\({}_{2}\) crystal [42; 159] and Dy\({}_{2}\)O\({}_{3}\) ceramics [48] were recently reported. Although further optimization of the fabrication process is needed, all of them exhibit very promising magneto-optical characteristics for their future utilization in high-power MIR FDs. All of these materials, however, have the Verdet constant in the range of \(\sim\)15 rad\(/(\text{Tm})\), which implies a requirement for a relatively strong magnetic field \(>\)2 T (for a \(\sim\)20-30 mm long MOE to achieve the desired 45-degree polarization rotation in a Faraday isolator). Although some permanent magnets with the needed peak magnetic field magnitudes were already reported [24; 26], it is still challenging to construct such magnets with the desired field distributed homogeneously over the whole MOE. Searching for other paramagnetic materials with higher magneto-optical characteristics in MIR is therefore still very topical.
# A Self-Paced Multiple Instance Learning Framework for Weakly Supervised Video Anomaly Detection
## 4 Experiments  
### Datasets and Evaluation Metrics  
We conducted the WS-VAD experiments on three widely used datasets: ShanghaiTech [26], UCF-Crime [7], and XD-Violence [27]. (1) ShanghaiTech: A medium-scale dataset derived from campus street video surveillance, encompassing 13 distinct background scenes, comprising 437 videos--330 normal and 107 abnormal. Following the standard splits established in [1] for WS-VAD, we partitioned this dataset into two subsets: a training set with 63 abnormal and 175 normal videos, and a testing set with the remaining 44 abnormal and 155 normal videos. (2) UCF-Crime: A large-scale dataset comprising 1900 videos, evenly split between 950 abnormal and 950 normal videos. The dataset encompasses 13 realistic anomalies, including abuse, arrest, arson, assault, accident, burglary, explosion, fighting, robbery, shooting, stealing, shoplifting, and vandalism. (3) XD-Violence: A dataset comprising 217 hours of audio-visual content collected from both movies and real-world scenes, containing 4754 untrimmed videos, with 3954 videos for training and 800 videos for testing.  
For ShanghaiTech and UCF-Crime, the area under the frame-level receiver operating characteristic curve (AUC) is used as the evaluation metric. For XD-Violence, the area under the precision-recall curve, referred to as frame-level average precision (AP), is used as the evaluation metric.
# Low-Cost RSAC and Adsorption Characteristics in the Removal of Copper Ions from Wastewater
## 2 Materials and Methods
### Study of Initial Concentration on Adsorption  
A series of 10, 50, 100, and 150 mg/L copper ion solutions were prepared separately. Then, a 5 g RSAC adsorbent was added into the solution series. Finally, the copper ion concentration in the supernatant of each sample was measured when the static adsorption time reached 2, 6, 12, 24, 48, and 72 h.
## 5 Results and Discussion
### Results for the 33-Node AC MG Operating in Grid-Connected Mode
#### 5.1.1 Statistical Analysis for the Objective Function \(Fo_{1}\) (Operating Costs)  
An analysis of the best solutions obtained for \(FO_{1}\) (operating costs minimization) reveals that all methods achieved reductions with respect to the base case (USD 9931.6423), with an average reduction of 56.3904%. To assess the effectiveness of each methodology, the average solutions obtained after 100 executions were also evaluated. This is illustrated in Figure 11, where PGA stands out for its effectiveness, achieving an average reduction of 63.1132%. This corresponds to average reductions of 34.1342%, 1.7604%, 0.7352%, 13.7275%, and 0.3396% compared with MC, PSO, JAYA, GNDO, and MVO, respectively.  
The standard deviation of each methodology had to be examined in order to evaluate the dispersion observed in the results. Figure 12 illustrates this analysis, where PGA achieves a standard deviation of 0.0187%, reflecting reductions of 0.6705%, 1.5359%, 1.6904%, 6.5476%, and 0.1023% when compared with MC, PSO, JAYA, GNDO, and MVO. An analysis of both the average solution and the standard deviation clearly indicated that PGA provides the best performance in terms of cost reduction.  
Figure 11: Average cost reduction in the 33-node AC MG operating in grid-connected mode.  
Figure 12: Boxplot of the solutions for \(FO_{1}\) in the 33-node AC MG in grid-connected mode.  
According to the processing times reported in Table 6 for \(FO_{1}\), JAYA exhibits the fastest performance, completing the optimization process in 225.4094 s, while GNDO is the slowest method (393.0044 s). The proposed PGA reports a processing time of 256.7073 s, ranking second among the six strategies evaluated.  
The average processing time of all methods is approximately 293.7181 s, which is relatively short considering that the optimization covers a full 24-h period of operation. Although PGA is not the most time-efficient method, it offers a favorable balance between speed and consistency, making it computationally efficient and well-suited for practical MG operation scenarios.
###### Abstract  
A series of 3D finite element (FE) analyses were performed to estimate the peak penetration resistance of spudcan foundations in sand over clay soil profiles. Elasto-perfectly plastic models following Mohr-Coulomb and Tresca failure criteria were used for sand and clay layers, respectively. The coupled Eulerian-Lagrangian (CEL) approach was used to simulate the large deformation in soil that occurs during the spudcan penetration. The performance of the numerical model was validated against centrifuge test results. A parametric study with a broad range of strength parameters for sand and clay was performed. The numerical results were used to assess the influence of sand thickness (\(H_{s}\)), the diameter of spudcan (\(D\)), friction angle of sand, and undrained shear strength of clay (\(s_{u}\)). A wide range of \(s_{u}\) was utilized to predict the resistance both of the soft and stiff clays. The calculated peak resistances are compared with a published analytical model. It is demonstrated that the model highly overestimates the peak resistance for stiff clays, most likely because it was developed specifically for soft clays and, therefore, does not account for the influence of \(s_{u}\). One of the parameters of the model is revised to account both for \(s_{u}\). Comparisons highlight that the modified model is able to capture the simulated peak penetration resistance for both soft and stiff clays.  
spudcan; peak bearing resistance; sand-clay profile; distribution factor; finite element analysis Article
# Development of Additive Fibonacci Generators with Improved Characteristics for Cybersecurity Needs
## 5 Results
### Research of Statistical Characteristics of AFG and MAFG Pseudorandom Sequences  
Research the statistical characteristics of the output pseudorandom bit sequences of AFG and MAFG for some \(p\) values were carried out with the NIST test package [29; 30; 31]. Results shows in Figures 6-9. Figure 6 presents a statistical portrait of the AFG output sequence at \(p=137\).  
As can be seen from Figure 6, the most tests valued at 0 and did not fall within the specified interval; meaning that the sequence does not meet the randomness requirements.  
Figure 8: Statistical portrait of the AFG output sequence at \(p=65,537\) and \(X(0)\).  
Figure 7: Statistical portrait of the MAFG output sequence at \(p=137\), \(X(0)=1\), \(a=b_{0\oplus}\ b_{1}\ldots\oplus b_{7}\).  
Figure 9: Statistical portrait of the MAFG output sequence at \(p=65,537\), \(X(0)=1\), \(a=b_{0\oplus}\ b_{1}\ldots\oplus b_{16}\).  
Figure 7 shows MAFG using the same initial data as AFG. The sequence also does not meet the randomness requirements, but there is a significant improvement over the AFG. In particular, most test values are above 0. So, the proposed modification demonstrates positive dynamics.  
Figure 8 shows the statistical portrait of the AFG output sequence at \(p=65\),\(537\). The tests failed and did not meet the randomness requirements.  
Figure 9 presented the statistical portrait of the MAFG output sequence using the same parameters as AFG. As can be seen, all tests are within the allowable range. It means that such sequence has high statistical characteristics and meet the randomness requirements.  
Analysis of statistical portraits (Figures 6-9) shows that, with the same parameters, the statistical characteristics of the output pseudorandom sequences of MAFG significantly predominated in the AFG. Thus, at \(p=65\),\(537\), \(X(0)=1\), and \(a=b_{0\,\oplus\,}b_{1}\,\ldots\,\oplus\,b_{16}\) (Figure 9) MAFG statistical characteristics entirely pass all NIST tests.  
The conducted research proves that the proposed Fibonacci additive generators can operate by recurrent equations, whose modules values can be arbitrary, including modules whose values are prime numbers. It distinguishes them from the known additive Fibonacci generators, whose value of the modules is equal to the power of two. That is, the class of proposed generators includes the known generators as a subclass. At the same time, the proposed generators have the best statistical characteristics and designs for hardware implementation primarily, in which will achieve their maximum speed when implementing the proposed structures in a modern element base, for example, in programmable logic integrated circuits (PLDs).
# Automatic Point-Cloud Registration for Quality Control in Building Works
## 2 Materials and Methods
### Method  
The method developed for the automatic registration follows an execution line structured in different steps (Figure 2). The laser data acquired with the robotic system (Step 1) are subjected to an automatic pre-processing of the laser data (Step 2). The pre-processing consists of a coarse noise filtering based on the analysis of the coordinate histogram, followed by a homogenization of the resolution of the resultant point clouds through voxelization [45] and a filtering process. Then, the registration strategy developed is applied (Steps 3 and 4): (i) coarse registration of each point cloud based on 3D detectors and descriptors (Step 3) [46], (ii) fine registration with iterative closest point (ICP) technique (Step 4) [47]. The result is one 3D point cloud joining all the point clouds acquired into one unique coordinate system, obtained with no user interaction. The last step of the methodology  
Figure 1: System used for data acquisition: (**a**) terrestrial laser scanner FARO 3D \(\times\) 330. (**b**) Unnamed ground vehicle guardian from Robotnik. (**c**) Hybrid system: terrestrial laser scanner (TLS) on unnamed ground vehicle  
is the quality control of the construction works focused on certain constructive elements (Step 4).