esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 False Data Injection Attacks on RL-Based Charging Coordination
### False Data Injection Attacks
#### 3.1.5 Attack 5: Irregular Strategic SoC Under-Reporting  
To make this attack stealthy, it under-reports the SoC values only during key periods when the consumer is likely to need more energy, while reporting correct SoC values in other times. By strategically lowering their reported SoC during high-demand times, such as evenings or peak usage hours, the malicious consumer ensures they receive more energy without raising suspicion. This irregular malicious behavior is designed to confuse the RL agent by creating subtle, irregular patterns in the SoC readings, while ultimately benefiting the attacker as shown in Equation (13).  
\[f_{5}(SoC_{i}^{(t)})=\begin{cases}SoC_{i}^{(t)}\times(1-\beta)&\text{if high-demand period},\\ SoC_{i}^{(t)}&\text{otherwise}\end{cases} \tag{13}\]  
where:* \(\beta\) is the reduction factor applied during high-demand periods, where \(0.1\leq\beta\leq 0.3\) depending on the severity of the attack. This attack differs from Attack 2, which randomly varies the reduction factor throughout the day and affects the SoC value at every time step. In contrast, Attack 5 strategically under-reports the SoC during peak demand periods to maximize energy allocation when it is most beneficial for the consumer. When the parameter \(\beta\) is between 0.1 and 0.3, the attackers under-report their SoC by 10% to 30%. This attack is particularly concerning because, for the most part, the attacker reports their actual SoC. Table 1 summarizes the functions of the five attacks discussed in this subsection.
# Retinex-Based Relighting for Night Photography
## 3 Proposed Method
### Lighting Points Sampling  
We select \(N\) light source points based on \(\mathbf{S}\) as a 2D probability distribution, where the normalized saliency map is \(\mathbf{S}_{\mathbf{p}}\mapsto[0,1]^{1}\). In addition, the samples must have a greater distance from each other, called blue noise sampling [30]. This paper introduces an efficient sampling method from \(\mathbf{S}\) with dithering and blue noise sampling. First, we use dithering \(\mathbf{S}\) to extract the candidates of samples. Dithering is a blue noise sampling method [54; 55]; however, it cannot control the number of samples. To change the number of samples, intensity remapping for dithering has been proposed [56]. Ideally, in dithering, the number of samples is the summation of the probability; \(N=\sum\mathbf{S}\). Thus, a remapping function for \(\mathbf{S}\) can control the number of samples. [PERSON] et al. [56] used the clipping linear function \(U(h,v)=\min(hx,1)\). Given the desired number of samples \(N_{d}\), the input \(\mathbf{S}\) is remapped and dithered to obtain the appropriate samples by setting the most suitable \(v\) that satisfies equation \(N_{d}=\sum U(\mathbf{S},v)\). The detailed information is shown in [56]. Note that the number of sampling points \(N_{d}\) is not the final number. We regard the dithering samples as candidates' points because dithering does not have randomness, i.e., fixed samples. Therefore, we select the final sampling points from the dithered samples, i.e., oversampled dithering samples. An example of the dithering output is shown in Figure 4(a). After performing remapped dithering, the oversampled points \(N_{d}\) are randomly resampled based on blue noise sampling to reduce the number of the last samples \(N_{l}\). This task is equivalent to selecting light sources from \(N_{d}\). In the selection process, the distance between the points should be as far apart as possible to illuminate a wide area. Figure 4: Restored image with multiplying \(\mathbf{R}\) and \(\mathbf{L}\). (a) \(\mathbf{R}\) is Figure 2(b), and the output is the same as Figure 2 grayscale. (b) \(\mathbf{R}\) is Figure 2(c) and the output is not recovered. The resampling is performed according to Figure 6 and Algorithm 1. First, we select a point randomly at lines 5 and 6 in Algorithm 1. Next, we find a point that has a distance within the range \(r_{l}\) and maximum distance \(\gamma r_{l}\) from the already sampled points while maximizing the remaining points that can be sampled as much as possible, which is shown in line 7 to line 23. Then, we iterate the sampling process. If the condition is not met, we retry the process by reducing \(r_{l}\) to make it easier to meet the condition that is represented from lines 24 to 26. After processing, we can select the light source point set of \(\mathbb{A}\) from the dithering point set \(\mathbb{D}\), where the number of samples is \(N_{l}=|\mathbb{D}|\). Figure 5 shows examples of the proposed sampling method. All points are sampled at a certain distance and at approximately equal intervals.
## 2 Schematic Description of Unit Cell and Simulation  
The parametric description of the proposed unit cell demonstrated in Figure 1a is a new arrangement of two square ring resonators with double-epsilon-shaped metal strips. Figure 1b illustrates the perspective view of the unit cell while Figure 1c shows the simulation setup.  
A square-shaped dielectric Rogers RT6002 material with a thickness (t) of 1.524 mm was used as substrate. The dimensions of the substrate were 10 \(\times\) 10 mm\({}^{2}\) with a dielectric constant of 2.94 and a loss tangent of 0.0012. The dimensions of the outer and inner SRR were 4.6 \(\times\) 4.6 mm\({}^{2}\) and 3.5 \(\times\) 3.5 mm\({}^{2}\), respectively. Copper (annealed) of conductivity is 5.96 \(\times\) 10\({}^{7}\) Sm\({}^{-1}\) was utilised for all resonators of the top and bottom layers. Split gaps (G) of the each SRR was 0.20 mm, whereas the thickness of the copper strips were 0.035 mm. The split gap between the two epsilon-shaped resonators was also maintained at 0.20 mm. All metal strips were of same width (b) 0.50 mm except the epsilon-shaped bar in the centre of the unit cell. The outer radius (r\({}_{1}\)) and the inner radius (r\({}_{2}\)) of both the epsilon-shaped metals are 2.5 mm and 1.9 mm, respectively. The width (c) of the epsilon-shaped metal was 0.60 mm, whereas the length of each middle arm of the epsilon shape (e) was 1.50 mm with a thickness (d) of 0.50 mm. Table 1 summarises all the parametric magnitudes and the dimensions.  
Figure 1: (a) Unit cell, (b) perspective view of unit cell and (c) simulation set up.  
To achieve the expected results from the proposed unit cell an electromagnetic wave is incident to the Z-axis while perfect electric condition (PEC) and perfect magnetic condition (PEM) along the X-axis and Y-axis, consecutively.
###### Abstract  
In the process of scratch defect detection in textured materials, there are often problems of low efficiency in traditional manual detection, large errors in machine vision, and difficulty in distinguishing defective scratches from the background texture. In order to solve these problems, we developed an enhanced scratch defect detection system for textured materials based on optimized photometric stereo vision and FFT-Gabor filtering. We designed and optimized a novel hemispherical image acquisition device that allows for selective lighting angles. This device integrates images captured under multiple light sources to obtain richer surface gradient information for textured materials, overcoming issues caused by high reflections or dark shadows under a single light source angle. At the same time, for the textured material, scratches and a textured background are difficult to distinguish; therefore, we introduced a Gabor filter-based convolution kernel, leveraging the fast Fourier transform (FFT), to perform convolution operations and spatial domain phase subtraction. This process effectively enhances the defect information while suppressing the textured background. The effectiveness and superiority of the proposed method were validated through material applicability experiments and comparative method evaluations using a variety of textured material samples. The results demonstrated a stable scratch capture success rate of 100% and a recognition detection success rate of 98.43% \(\pm\) 1.0%.  
fast Fourier transform; photometric stereo; scratch defect detection; image enhancement +
Footnote †: journal: Applied sciences  
Article Enhanced Scratch Detection for Textured Materials Based on Optimized Photometric Stereo Vision and Fast Fourier Transform-Gabor Filtering [PERSON] et al. (2021); [PERSON] et al. (2021); [PERSON] 1,2 and [PERSON] 1,2,*
## 1 Introduction
In this way, not only the required computational resources for both CFD and CSD solvers can be largely saved, but the information exchange (interface mesh deformation) and loads transfer (pressure from fluid domain and displacement on the solid surfaces) representing the FSI effect can be applied in an efficiency and stable way. In recent years, coupling simulation based on the partitioned method has been widely applied in the FSI problems of pump and other turbomachineries, either with the strong two-way coupling strategy or the one-way sequential coupling strategy, and most of the application cases achieved satisfactory prediction results in the deformation, oscillation, and orbit curves of the impeller [16, 17, 18, 19, 20]. However, present reported numerical methods are not able to meet the requirements for the prediction of rotor vibration in centrifugal pump. The main reason is that the solving of CSD in current work is built on the general dynamic equation, thus neither the rotary inertia nor gyroscopic effects can be considered, and the stiffness and damping characteristic of bearing has been ignored. In such a case, the dynamics strain and stress distribution of the impeller can be obtained for use of strength checking and safety evaluation, but the motion features about the shaft system cannot be acquired with acceptable accuracy. In fact, the flow-induced vibration behaviors of the shaft system contain a lot of useful information, and they can be easily measured in a cost-effective manner [21]. Therefore, the monitoring and analyzing of shaft vibration data has become the mainstream approach for the safety assessment and fault diagnosis of fluid machinery [22,23]. Based on the previous work by [PERSON] et al. [1,24,25], this research presents a new one-way coupling simulation approach for the flow-induced vibration of rotor assembly in centrifugal pump, with special attention on the association features of unsteady internal flow and shaft vibration. Compared with former works, the first and foremost improvement of this work is the application of rotordynamics in the CSD, where the bearing damping matrix and gyroscopic matrix have been added to the general dynamic equation. With this advantage, the above new approach is able to provide a wealth shaft oscillation information including vibration velocity and shaft centerline orbit, with both the unsteady hydraulic force on the impeller and the rotor dynamic properties in consideration. In this article, an industrial split-case double suction centrifugal pump was chosen for study, and the unsteady flow as well as rotor assembly vibration behaviors under different operation conditions were investigated. The remaining of this article was organized as follows. In Section 2, the mathematical model and its overall frame are presented. Section 3 describes the computation details and experiment setup for a research case. Sections 4 and 5 are the analysis and discussion for the numerical and experimental results, followed by the conclusions in Section 6.
## 4 Experiments and Results
### Results of the HMT Gesture Recognition
#### 4.2.3 Results of the HMT Gesture Recognition
The confusion matrices of the recognition results under the two conditions are shown in Figure 10. In addition, the precision, recall, and F-measure of the 10 gestures' classification are calculated based on the confusion matrix to further verify the performance of the gesture recognition, which are shown in Figure 11. The mean value of the F-measure under the sitting condition is up to 0.984; under the standing condition, it is 0.963. The higher F-measure value in sitting conditions indicates its better classification performance than that in standing conditions. The overall recognition results of sitting conditions and standing conditions based on LOSO cross-validation are clearly shown from the confusion matrix in Figure 10. Among the confusing gestures, we analyzed the reasons for some having a high proportion of varying classification types. Gesture 7 was classified inaccurately as gesture 4 with the proportion of 4.67% under the sitting condition and 3.11% under the standing condition, because of the missing horizontal motion detection. The missing detection was caused by the incorrect end of a gesture due to the unexpected pauses during a single gesture period. For similar reasons to those mentioned above, gesture 8 was classified inaccurately as gesture 4 with the proportion of 4.67%, and gesture 10 was recognized incorrectly as gesture 2 with the proportion of 2.67%. There is another situation: gesture 8 was classified wrongly as gesture 2 with the proportion of 2.67%, which was caused by the unobvious longitudinal motion in gesture 8. For example, the subjects immediately started the showcased gestures with the uncompleted motion of the fist bobbing, resulting in the insufficient longitudinal motion. Figure 11: (a) The precision, recall, and F-measure of gesture classification in sitting conditions; (b) The precision, recall, and F-measure of gesture classification in standing conditions. Figure 10: (a) Confusion matrix of gesture recognition in sitting conditions; (b) Confusion matrix of gesture recognition in standing conditions.
# Development of Alfa Fiber-Based Mortar with Improved Thermo-Mechanical Properties
## 2 Materials and Methods  
### Material Selection  
The new composite material manufacturing consists of mixing cement, sand and alfa fiber (AF) with drinking tap water. The AF plant used to reinforce mortar was extracted from the Oujda region in the northeast of Morocco (Figure 1a). It was prepared in different steps. It was first prepared by submerging it in a salt water (35 g-L\({}^{-1}\)) [23] at ambient temperature (about 30 \({}^{\circ}\)C) for 7 days and was washed with water jet. The goal of this soaking process was to remove sand and dust existing on the stem surface and to facilitate fiber separation. Subsequently, the plant stems were dried under the sun for one week. These low cost and environmental operations will eliminate most of the moisture in alfa stems and consequently make the grinding operation more efficient. The dried alfa stems were crushed (Figure 1b), separated, and cut into short fibers with a diameter between 0.7 and 1 mm, and a length between 1.5 and 2.5 cm (Figure 1c). These lengths were selected since [24] demonstrated that optimal mechanical performance was obtained at this length range.  
This natural treatment of AF will enhance the fibers adhesion with the cementitious matrix.  
An alfa fiber diffractogram obtained from an X-ray diffractometry (XRD) test is shown in Figure 2. One can clearly notice that the spectrum contains two main peaks. The first one, wide and multiple, is obtained at Bragg angles of 15\({}^{\circ}\) and 16.54\({}^{\circ}\) corresponding to crystallographic planes of (1-10) and (110), respectively. The second one appears at 22.50\({}^{\circ}\) which is associated to a crystallographic plane of (200). These two peaks are characteristic of the presence of cellulose I. The cellulose component is considered as a bio-binder that can bond efficiently the fibers to mortar.  
The Portland (CPJ 35- CEM II 22.5) cement is used in this study, and its technical characteristics are in agreement with the Morocco norm (NM 10.01.004., 2003) [25]. The sand used is extracted from  
Figure 1: Steps of alfa fiber (AF) preparation: (**a**) alfa plant, (**b**) crushed alfa stem after drying process and (**c**) used AF.  
Figure 2: Alfa fiber X-ray diffractogram.  
Nafiss River (Loudaya, Morocco) with a maximum size of 2 mm and water mass content of 2.15%. The sand equivalent test indicates that the average value of its neatness is 87% (NF EN 933-8, 1999) [26]. Its chemical constitution is reported in Table 1.
## 3 Results and Discussion
### Displacement Responses  
According to Figure 10, the displacement time history at the center of the wall was obtained for different thicknesses. It can be seen that the closer the explosives to the wall, the greater the displacement. With an explosive charge weight of 7 kg, for all wall thicknesses, the displacement was more significant than that for the other charge weights. For example, Models 4 and 6 had higher displacements than Models 1 and 3, respectively. According to the results, it can be observed that at a stand-off distance of 2 m (such as in Model-1), the wall models practically failed and had a larger displacement compared to the other models. Therefore, it can be concluded that at short stand-off distances, AAC-based masonry walls do not have enough resistance against blast loading, and a retrofitting scheme is required.
## 5 Conclusions and Future Works  
This paper studies dynamic beam hopping time slots allocation under the effect of time-varying rain attenuation. To address this issue, rain attenuation time series based on Dirac and log-normal distribution is first provided. Then, generally considering uneven traffic demand, changeable communication link conditions, and different spectrum efficiency, the dynamic allocation method by employing the genetic algorithm is proposed. It can obtain both the quantity and arrangement of time slots allocated for each beam. Numeric simulation results show that, compared with conventional methods, the proposed algorithm can dynamically adjust time slots allocation to meet the spatial heterogeneity traffic requirements of each beam under the effect of time-varying rain attenuation and effectively improve system performance.  
In future work, we will try to obtain the actual measurement data of satellite communication rain attenuation, although it may be difficult to acquire.  
Conceptualization, C.Z. and G.Z.; methodology, C.Z. and J.Y.; software, J.Y. and Y.Z.; validation, C.Z., G.Z. and Z.L.; formal analysis, C.Z.; investigation, Z.L.; resources, C.Z.; data curation, C.Z.; writing--original draft preparation, C.Z. and Y.Z.; writing--review and editing, C.Z. and G.Z.; visualization, C.Z.; supervision, G.Z.; project administration, G.Z.; funding acquisition, C.Z., Z.L. and G.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Fundamental Research Funds of CETC (No. 2020069).  
**Acknowledgments:** The authors acknowledge the anonymous reviewers for their very helpful and useful comments to improve this paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 8: Traffic matching ratio.
# Enhancing Indoor Localization with Room-to-Room Transition Time: A Multi-Dataset Study
## 3 Material and Method
### Online (Learning-and-Test) Phase
#### 3.2.10 Ensemble Bagged Trees  
Ensemble Bagged Trees [57] is an Ensemble method that combines many Decision Trees to form a powerful classification or Regression model. Using the Bagging (Bootstrap Aggregating) technique, random samples are generated from the dataset and separate trees are trained on each sample. Then, the predictions of these trees are averaged or modulated to make the final prediction. This method compensates for the errors of a single tree, increases generalization ability, and reduces overfitting. Ensemble Bagged Trees are known for their robustness and ability to learn complex relationships. Bagging reduces variance by training each tree on different samples, while reducing bias by aggregating the trees [58]. For this reason, the model ensures more reliable and stable forecasts.
# Enhancing Energy Efficiency in IoT-NDN via Parameter Optimization
## 1 Introduction
The development of such a framework not only streamlines the simulation process but also holds the potential to accelerate progress in NDN network optimization, ultimately benefiting the broader IoT ecosystem. The developed framework simplifies parameter configuration within networks and serves as a foundational tool for conducting network experiments. The contributions can be summarized as follows:  
1. _Simple Parameter Definition for Diverse Experiments_: The framework allows a declarative definition of parameters, rather than an imperative approach, as would be necessary when using plain ndnSIM: Instead of having to configure all the necessary networking objects manually, users can simply define a set of desired parameter values. This facilitates the conduction of a wide range of IoT-NDN-related experiments considerably. 2. _Identification of Optimal Parameter Combinations_: It enables the identification of the most energy-efficient network parameter combinations using the hyperparameter optimization algorithm grid search. 3. _Automated Visualization of Results_: The framework automates the visualization of experiment results, enhancing the clarity and comprehension of outcomes. 4. _Integration of Caching Schemes and Strategies_: It incorporates new caching strategies and schemes into the framework and allows for straightforward extensions to accommodate additional strategies and schemes in the future. 5. _Automatic Graph Generation_: The framework streamlines data representation by automatically generating graphs directly from network parameters like the topology or graph size. The remainder of this paper is organized into the following sections: Section 2 introduces related work on fundamental concepts, IoT-NDN and ndnSIM. Moving on, Section 3 outlines the essentials of the NDN protocol, including an exploration of its various packet types and data structures. In Section 4, we explore the architecture of the framework proposed in this work, providing insights into the key parameters that can be configured using this framework. Moving on to Section 5, we introduce the reader to ndnSIM, the chosen simulator for implementing the previously discussed parameters. Here, we outline the framework's structure and provide a detailed account of how we implemented crucial parameters within the simulator. Section 6 provides a detailed explanation of our approach to analyzing network-related parameters for IoT-NDN. The primary objective is to identify optimal parameter combinations for minimizing energy consumption. Finally, in Section 7, we conclude the paper by summarizing our key findings and engaging in a discussion regarding energy efficiency in IoT-NDN through parameter optimization and possible future applications.
## 4 Results and Discussion
### Impact of Feature Extraction on Results  
In the previous section, the model was trained using the original dataset. However, due to the presence of redundant features and noisy data, the training time was prolonged, and the accuracy was suboptimal. To improve the model's performance, this section explored the use of PCA to preprocess the dataset, enhancing its quality and, in turn, improving the model's prediction performance.  
PCA is a dimensionality reduction technique that transforms the original data into a new feature space through linear transformation to extract the main features. The first 28 principal components were selected by PCA, retaining 95% of the total variance of the data, which constituted a subset of the new features, as shown in Figure 8. The experiment compared the performance of the proposed model before and after PCA processing, in terms of test accuracy, precision, recall, F1-Score, and testing time. The results show that after PCA processing, the model's precision, recall, and F1-Score improved by 6.08%, 1%, and 4.02%, respectively, while the testing time was reduced by 15.5%. However, the overall model accuracy decreased by 1%, as PCA caused some loss of information during dimensionality reduction, as shown in Table 6.  
Figure 7: Loss plots for the four ablation experiments.