esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Mathematical Modeling
### Suboptimal Control in Nonlinear Dynamics
#### 4.1.1 Body-Fixed Hovering Around (16) Psyche  
We now present the results from simulations for body-fixed hovering around the relative equilibrium points for the asteroid (16) Psyche shown in Table 1 and Figure 2. For this case we used \(C_{20}=-0.0320\), \(C_{22}=0.0069\)[76; 78] and, from Equation (2), the reference synchronous radius is \(r_{0}\sim\)215 km, well outside the \(\sim\)140 km-radius Brillouin circumference enveloping Psyche's equator.  
The evolution of the uncontrolled and controlled planar trajectories for a spacecraft in the vicinity of the stable equilibrium point \((x,\ y_{\rm s})=(0,\,213,\!712.06)\) in the \(Oxy\) effective potential (3) for (16) Psyche are shown in Figure 7 (left). The two hovering maneuvers shown in the left picture were performed using (constant) control parameters \(Q(X)=10^{-3}\,diag(1,\,1,\,4,\,1)\) and \(R(X)=10^{5}\,diag(4,\,1,\,4,\,1)\), for the lower trajectory (dotted curve), meaning that errors in the \(x\) position coordinate are penalized 4 times less than errors in the \(y\) coordinate, while control efforts are equally penalized within the position variables. For the upper trajectory (dashed), we used \(Q(X)=10^{-4}\,diag(3,\,1,\,3,\,1)\) and \(R(X)=10^{9}\,diag(1,\,1,\,1,\,1)\), meaning a \(10\times\) lower penalty for overall state errors and \(10^{4}\times\) higher penalty for control effort. The corresponding (dashed, dotted) \(U_{py}\) control  
Figure 6: Left: Discount factor \(\alpha=10^{-6}\) used to ensure stability in calculations.  
efforts for these cases are shown in the picture at right. From these results, one can see the great flexibility the SDRE approach affords mission designers in trading control effort and convergence of the controlled trajectory, ranging from a faster (\(\sim\)0.5 h) and almost direct trajectory (dashed) to a slower convergent (\(\sim\)1 h) one (dotted) with \(\sim\)20 times lower cost (area delimited by the dashed and dotted curves at right).  
In Figure 8, we present the trajectories and control-effort results for hovering around the unstable equilibrium point \((x,y_{x})=(221,891.42,\ 0)\) (red star), by this time adopting control strategies with different loads for the control-penalty matrices \(R\) and \(Q\) for two different maneuvers. In this case, we used \(Q(X)=10^{-5}\,diag(4,\ 1,\ 4,\ 1)\) and \(R(X)=10^{9}\,diag(1,\ 1,\ 1,\ 1)\) for the lightly controlled trajectory (dashed curve) while only \(2\times\) smaller \(Q(X)=10^{-5}\,diag(2,\ 1,\ 2,\ 1)\) and \(1000\times\) smaller control-penalty index \(R(X)=10^{6}\,diag(1,\ 1,\ 1,\ 1)\) for the tightly controlled trajectory (dotted curve).
## 2 Distortion and Perceptual Measures
### Distortion Measures  
In the conventional additive distortion setting, the distortion measure is defined as the average of a per-letter distance.  
**Definition 1**.: _Given a per-letter distortion measure \(\delta:\mathcal{X}\times\mathcal{X}\mapsto[0,d_{\text{max}}]\) with \(d_{\text{max}}<\infty\), the (average) distortion between two sequences \(x^{n}\) and \(y^{n}\) is defined as_  
\[\Delta(x^{n},y^{n}):=\frac{1}{n}\sum_{i=1}^{n}\delta(x_{i},y_{i}).\]  
_Here, we assume that the distortion is upper-bounded by \(d_{\text{max}}\)._  
This bounded distortion enables the derivation of a bound relating the distortion measure to the perceptual measure, as shown in Equation (11), which will be introduced below.
## 3 Simulation Results and Analysis
### Crack Initiation Stress of Rock-like Samples  
In PFC2D, for an intact sample, the crack initiation stress of an intact sample is difficult to be determined by the number of micro-cracks due to the discrete crack distribution. However, for the samples with defects, the crack initiation usually starts from the existing defects, and the crack initiation stress is defined as the corresponding stress value when the number of micro-cracks reaches about 10% of the total number of cracks under the peak stress of the sample [35]. According to the laboratory test results and numerical simulation results, the variation of crack initiation stress under different flaw inclination angles is shown in Figure 5.  
It can be seen that the results of numerical simulation are in good agreement with the results of the uniaxial compression laboratory test performed by [PERSON] et al. [22], and the crack initiation stress increases with the increase of flaw inclination angle.  
Figure 3: Axial stress–strain curves of sample models with different flaw inclination angles.  
Figure 6 is a snapshot of different numerical models under crack initiation stress. For the convenience of comparison, the snapshot of the intact sample model was taken when the number of micro-cracks reaches about 10% of the total number of cracks under the peak stress, as shown in Figure 6a. In Figure 6b, if the flaw inclination angle \(\beta=15^{\circ}\), the micro-cracks are all concentrated near the flaw tip. When \(\beta\) increases from 30\({}^{\circ}\) to 60\({}^{\circ}\), part of the micro-cracks is generated at the flaw tip, while the other part is dispersed in the model, and the degree of dispersion increases with the increase of flaw inclination angle, as shown in Figure 6c-e. When the flaw inclination angle \(\beta=75^{\circ}\), only a few micro-cracks appear at the flaw tip, and most of them disperse in the sample model, which is similar to the micro-cracks in the intact sample model, as shown in Figure 6a,f. It shows that with the increase of flaw inclination angle, the crack concentration of the model decreases.
## 4 Results and Discussion
### Discussion
These three elements synergized with each other to construct a lightweight design with a unique feature extraction capability, which allowed the SL-CNN model to significantly reduce the model parameters while maintaining high-precision image classification, effectively improving its memory efficiency and training speed. In summary, increasing the network depth, parallel structures, or using structures such as residuals is not fully applicable for desert grassland fine-grained feature classification, and the model structure should be optimized and adjusted appropriately. The 2D-CNN and SL-CNN differed only in the convolution method. Therefore, the model classification accuracy was similar. However, the SL-CNN model's training time, generated model, and the total number of parameters for the model building run accounted for 65.88%, 29.21%, and 26.41% of the memory of the 2D-CNN, respectively. This indicates that an improved depth-separable convolution is necessary for the convolutional approach. In addition, we explored the differences between this model and other desertification-grassland feature classification models. In this study, the latest deep learning network models DIS-O [39], LGFEN [41], and GDIF-3D-CNN [50] for hyperspectral grassland feature recognition were selected for a comparative study. To ensure experimental reliability, the structure and parameters of the selected models were the same as those in the original study. The experimental results are listed in Table 6. As shown in the table, all the models achieved more accurate results for the classification task. Although the SL-CNN model was not time efficient, it showed the highest accuracy in classification and consistency testing. This indicated that the SL-CNN model had an appropriate model complexity, could more effectively capture features, and had a stronger generalization ability and robustness. The DIS-O model had the lowest classification accuracy, mainly because of its relatively simple model structure, resulting in the extraction of grassland features. The DIS-O model had the lowest classification accuracy, mainly because of its relatively simple model structure, leading to an insufficient ability to extract grassland features. The DIS-O model was originally designed for a small number of classified species, and increasing the number of classified categories would lead to underfitting of the model and make its capacity insufficient. By replacing 2D convolution with 3D convolution, GDIF-3D-CNN improved the performance compared to the DIS-O model, indicating that 3D convolution helped to extract higher-level features. However, without further structural design, it still faces the problem of insufficient feature extraction capabilities. In contrast, the classification accuracy of the LGFEN model was slightly lower than that of the SL-CNN model, which indicates that the addition of the CBAM attention mechanism helped to improve the recognition and classification ability of desert grassland features and further enhanced the robustness of the model based on a separately designed feature extraction module.
## 4 Simulation of NDN Transport in V2X Scenarios
### A Toolchain for NDN Transport Simulation  
In case of NDN, the most elaborated simulation framework is ndnSIM [56] which is based on ns-3 [57]. It utilizes the Network Forwarding Daemon (NFD) and the ndn-cxx library which allow for realistic experimentation (from a software point of view). Ns-3 supports different ways to introduce mobility into the simulation. However, the available mobility models target VANET research and are mostly restricted to a playground (bounding box). For example, ns-3 includes the Gauss-Markov mobility model, random direction, random walk, random way point and many more. However, in V2X use cases (near) realistic mobility of vehicles is required. One of the most elaborated tools allowing the simulation of traffic flows is Simulation of Urban Mobility (SUMO) [58]. It supports map data import from OpenStreetMap and is widely used in VANET and V2X research as well. It furthermore allows to generate mobility traces for ns-3. Besides this _offline_ feature it also can be directly paired with the network level simulation via a TCP/IP interface called Traffic Control Interface (TraCi). Please note, that we do not discuss mobility models such as random walk or random direction because these models do limit the expressiveness and goal of a V2X simulation.
# Inverse Halftoning Methods Based on Deep Learning and Their Evaluation Metrics: A Review
## 4 Quality Evaluations of Inverse Halftone Images
### Subjective Evaluation  
Subjective evaluation is the most commonly used and direct method, which is to judge and evaluate images by observers according to their experience or specified evaluation scale [40]. The restored continuous-tone image is evaluated by considering the following factors: halftone dot noise removal, texture detail reproduction, smooth area processing, and chromatic aberration. As shown in Figure 17, the continuous-tone images restored by the IH-UNet [17] and PRL method [20] are better in dot removal than others, whereas the images processed by the PRL method perform better in the smooth region. In terms of texture details, the image processed by ESRGAN [25] is the clearest, whereas the images processed by PRL is the closest to the original continuous-tone image. In the aspect of color reproducing, the image generated by the PRL method is the most similar to the original image, followed by IH-UNet and ESRGAN.  
Figure 17: The images of butterfly, Lena, and peppers (from up to bottom) evaluated by the subjective evaluation, in which the regions marked with a green box are enlarged for detail observation. (**a**) Halftone images, (**b**) continuous-tone images, (**c**) restored images by the PRL method, (**d**) restored images by the IH-UNet method, and (**e**) restored images by the ESRGAN method.
# Surfactant-Modified Construction Waste Bricks for the Removal of Diclofenac from Aqueous Solutions
## 3 Results and Discussion
### Adsorption Isotherms  
The isotherms of the adsorption of DCF onto the PRBPs and CCBPs are presented in Figure 9. The results show that the adsorption capacity of DCF increases as the equilibrium concentration increases for both adsorbents. However, the CCBPs exhibit a significantly higher adsorption capacity than the PRBPs at all equilibrium concentrations, highlighting the considerable impact of CTAB modification on the removal of DCF from aqueous solutions.  
Figure 8: Effect of ionic strength on the adsorption of DCF onto PRBPs and CCBPs (experimental conditions: [DCF]\({}_{0}\) = 20 mg/L, dosage = 2 g/L, T = 298 K, pH = 4.0 \(\pm\) 0.1).  
Figure 9: Adsorption isotherms of DCF onto PRBPs and CCBPs (experimental conditions: dosage = 2 g/L, T = 298 K, pH = 4.0 \(\pm\) 0.1).  
To investigated the isotherms of DCF adsorption onto waste bricks further, the Langmuir isotherm model and Freundlich isotherm model were used to fit the data and are expressed as follows [38]:  
\[Q_{e}=Q\;bC_{e}/(1+bC_{e}) \tag{3}\]  
\[q_{e}=K_{F}C_{e}^{(\frac{1}{n})} \tag{4}\]  
where \(Ce\) (mg/g) represents the maximum adsorption amount and \(b\) (L/mg) represent adsorption equilibrium constant, \(n\) represents the Freundlich constant, and \(K_{F}\) (mg/g (L/mg)\({}^{1/n}\)) represents the adsorption performance, which indicates the affinity of the sorbent and the possibility of adsorption.  
The isotherms of the adsorption of DCF onto the CCBPs and PRBPs were analyzed using the Langmuir and Freundlich models, and the parameters obtained are shown in Table 4. The Langmuir model provided a better fit to the experimental data than the Freundlich model, with R\({}^{2}\) values greater than 0.97 for both adsorbents. This indicates that the adsorption of DCF onto the waste bricks occurs through monolayer molecular adsorption rather than multilayer or heterogeneous adsorption. [39]. Compared with the \(q_{max}\) of the PRBPs (0.19 mg/g), the CCBPs had a higher capacity for the adsorption of DCF (33 mg/g), which was mainly determined by the physical and chemical properties of the CCBPs. In addition, the values of 1/n obtained from the Freundlich model for both the CCBPs and PRBPs were found to be less than 1.0, indicating that the adsorption of DCF onto the two adsorbents is favorable. These findings further support the potential application of CCBPs as an effective adsorbent for the removal of micropollutants from aqueous solutions.
# Validation and Parametric Study of a Soil-Structure Interaction Nonlinear Simplified Model
## 1 Introduction
This model can better reflect the stiffness, damping and nonlinear properties of the soil, but the determination of the model parameters is relatively complex. [PERSON] et al. (2019) transformed the single pile of the Penzien model into a multi-pile model and added a free field system outside the structural system to improve the applicability of the model. [PERSON] et al. (2019) improved the superstructure from a multi-mass system to a beam element system, allowing for the quantitative analysis of the superstructure with the improved Penzien model. [PERSON] et al. (2020) proposed an improved Penzien model that takes into account the dynamic nonlinearity between the soil and the superstructure, and used an ANSYS numerical simulation to verify the soil nonlinearity in pseudo-dynamic substructure tests. However, further validation is required before this model can be widely applied to a wider range of conditions. In this paper, the improved Penzien model was validated using MATLAB and ANSYS software to construct structural models that take into account soil-structure interaction, using a real building as an illustrative example. On this basis, the effects of the parameters of the components in the simplified model were investigated. This parametric study included the free field system, site soil properties, equivalent soil area, superstructure dimensions and number of stories, and seismic waves.
## 3 Materials and Methods
### Inference with BRB Using ER Approach
#### 3.2.2. Calculation of Activation Weight of Each Belief Rule  
To quantify the overall impact of each belief rule in the inference process, an activation weight of each belief rule is calculated based on the weight associated with each rule and individual matching degrees of referential terms generated from then input transformation as described by (1). However, a belief rule may consists of more than one antecedent attributes linked with logical connectives AND or OR under conjunctive and disjunctive assumption respectively. Therefore, it is necessary to find out the combined matching degree of a rule considering all the antecedent attributes of the rule. The combined matching degree of a belief rule measures the degree to which the input vector matches the antecedent part of that rule and is computed integrating all the individual matching degrees associated  
Figure 1. Working process of BRBES.  
with the rule. To do so, individual matching degrees of all the antecedent attributes in the \(k\)th rule need to be integrated using an aggregation function. Proper care should be taken in selection of the aggregation function. While \"AND\" connective is used to form the rule, the combined matching degree \(\alpha_{k}\) for the \(k\)th rule can be calculated using the following simple weighted multiplicative aggregation function:  
\[\alpha_{k}=\prod_{i=1}^{M}(\alpha_{i}^{k})^{\delta_{i}} \tag{5}\]  
Otherwise, if the \"OR\" connective is used for all the antecedents in a rule, then the combined matching degree \(\alpha_{k}\) for the \(k\)th rule is calculated using the following weighted sum aggregation function:  
\[\alpha_{k}=\sum_{i=1}^{M}(\alpha_{i}^{k})^{\delta_{i}} \tag{6}\]  
where \(\delta_{i}=\frac{\delta_{i}}{\underset{i=1,\dots,M}{max}(\delta_{i})}\) is the normalized weight of the \(i\)th antecedent attribute with \(0\leq\delta_{i}\leq 1\). The contribution of an antecedent attribute towards \(\alpha_{k}\) is positively related to the weight of the attribute. In other words, an important attribute play greater role in determining \(\alpha_{k}\)[33]. Finally, the activation weight \(\omega_{k}\) of the \(k\)th rule is generated by weighting and normalizing the combined matching degree \(\alpha_{k}\) given by (7) as  
\[\omega_{k}=\frac{\theta_{k}\alpha_{k}}{\sum_{l=1}^{L}\theta_{l}\alpha_{l}} \tag{7}\]  
where \(\theta_{k}\) represents the relative weight of the \(k\)th rule. Note that \(0\leq\omega_{k}\leq 1\) (\(k=1,\dots,L\)) and \(\sum_{l=1}^{L}\omega_{l}=1\). When the activation weight of a rule becomes zero, then the rule is not activated and it has no impact on the inference process [34].
# Breaking Alert Fatigue: AI-Assisted SIEM Framework for Effective Incident Response
## 3 Proposed SIEM Framework  
In this section, we present the proposed SIEM framework, which is designed to address alert fatigue in security operations. The proposed framework combines AI and data visualization techniques to streamline the incident handling process. As shown in Figure 1, the framework comprises four key modules.  
* The alert generation module collects alerts from multiple IDS sources.
* The feature processing module standardizes diverse log formats and encodes relevant information as numerical vectors for uniform representation.
* The ML module employs supervised learning algorithms on labeled alerts to develop a prediction model to detect critical alerts.
* The investigation module assesses the performance of the prediction models and enables rapid incident investigation through data visualization and alert correlation, which summarize alerts into high-priority events.
# A Survey of Routing Protocols in WBAN for Healthcare Applications
## 4 Prospects for WBAN and Suggestions for Routing Design  
In the future, the development of WBAN will be explosive. With the rapid development of Internet of Things, artificial intelligence, and the coming 5G era, WBAN will play a decisive role in human society. Everyone is looking forward to the application of WBAN as soon as possible, but there are still many problems to be considered at present [36; 37]. The limited energy of the nodes is an important factor hindering the development of WBAN, thus, energy efficiency must be considered. We cannot only rely on energy harvesting technology, but should also explore the energy efficiency method from the source. In the future, the energy efficiency routing design of WBAN can, not only be improved and optimized, but should also be bold to innovate new methods and new ideas. The design of energy efficiency routing can reference the following ideas.
# Analysis of the Current State of Automation of Hazard Detection Processes in BIM in Slovakia
## 3 Results
### Questions Related to the Importance of the Topic and Its Future (Hazard Detection)  
At the end of the questionnaire, the respondents answered questions concerning the significance and future of the topic (Figure 5). The vast majority, 93.33% (Figure 5a), answered that people who are responsible for HS on the construction site should use BIM to detect hazards. At the same time, they thought that increasing the efficiency of hazard detection would directly influence the increasing use of BIM by those responsible (Figure 5b). It is the demanding and quantitative manual work that discourages companies from these activities, most of which do not even perform them, and they deal with the hazards only when they occur on the construction site.  
However, respondents are aware of the risks of such actions and up to 80% of them (Figure 5c) thought that if these processes were made more efficient and carried out, it would directly increase the level of safety on the construction site.  
As mentioned at the conclusion of the Introduction, there is a lack of commercially available solutions (e.g., plug-ins) or stand-alone software that would allow companies to use the software they currently use in their work for HS issues. At the same time, all respondents stated that they would use such a tool or software, and 73.33% (Figure 5d) would be willing to pay for it.