esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Initial Results
### Discussions: Towards Real-World Application and Stakeholder Alignment
#### 4.4.4 Risks and Governance Challenges in Blockchain-AI Integration  
The proposed CF-BIAI-SXT framework highlights blockchain as a promising ethical infrastructure for AI-based sextortion mitigation. However, there is the risk of over hyping its benefits; therefore, an acknowledgement of potential risks and governance challenges that may arise from such integration, particularly in high-risk, high-stakes environments, is needed.  
The first risk comes from the DAO Governance Vulnerabilities. DAOs are susceptible to governance capture or majority collusion in scenarios where voting power is token-based, as well-resourced actors might gather disproportionate influence and, thus, skew decision-making and undermine fairness. Potential mitigations for such risks are quadratic voting mechanisms, reputation-based access, or the introduction of veto rights for verified civil society nodes.  
The second risk also comes from collusion (coordinated manipulation of model updates), but this time in federated learning that relies on distributed training across organizational nodes. This risk is enhanced by the opacity of local training environments. A potential mitigation is the use of smart contract-enforced audit trails. Another important risk derives from the token-based reward mechanisms. For instance, victims may be or feel pressured to contribute data to earn tokens, or bad actors might submit synthetic data to game model updates. The token incentive distortions may lead to a gamification of pseudo-consensual sextortion, inducing data sharing. A mitigation measure may be the use of real consent tracking via data wallets. Lastly, the complexity of BIAI systems brings a significant risk, with edge-case failures, such as smart contract bugs, ZKP misconfigurations, or identity spoofing, having outsized consequences and, more importantly, carrying the potential to retraumatize users or compromise legal evidence. This risk may be mitigated by ongoing third-party audits, staged rollouts, and real-time risk monitoring.
# Experimental Study on the Strength and Durability for Slag Cement Mortar with Bentonite
## 3 Proposed Experimental Tests
To determine the effect on durability, such as the resistance to chloride compounds due to the decrease in permeability, the absorption of the chloride by the bentonite, and the variation of the properties of the hardened specimens after suffering sulfate attacks, sulfate resistance tests and the chloride-blocking tests (ASTM C1202-19 [29]) were also carried out. Similarly, specimens of three various water/binder ratios and four replacement ratios of bentonite were soaked in the solution of sodium sulfate and then dried out, alternately, for five cycles (ASTM C1202). After the sulfate resistance testing, examinations of the shape distortion and volume variation of the specimens were performed. In addition, the possible weight loss of each specimen was measured, and the increment of moisture absorption rate was also recorded. All of these data were compared to the data before the sulfate resistance test, which combined indicate the durability of the slag cement mortar against sulfate attack. A series of compressive strength tests were also carried out for the specimens subjected to sulfate corrosion. The test results were compared to the strength obtained from the specimens that were not subject to sulfate corrosion and were cured for 28 days in a standard procedure.
# Machine Learning Models for Ecofriendly Optimum Design of Reinforced Concrete Columns
## 3 Results  
In this study, predictions were made by using different ML algorithms for achieving the ecofriendly optimum design of a rectangular RC column. Bending moment and axial force features were used as inputs while reinforced rectangular column dimensions and the total area of the longitudinal reinforcement features were taken as outputs to be predicted with the MOR algorithm (Figure 9). The data used in the prediction was generated with HS algorithm as explained before. The HS algorithm was implemented to determine the optimal reinforced rectangular column dimensions and reinforcement area. Figure 8: Flowchart for the creation and evaluation of machine learning models. Figure 9: Inputs and outputs of the ML process. The input dataset consisted of 4429 rows. Regression algorithms were used to predict optimal RC rectangular column dimensions and the total area of the longitudinal reinforcement based on the model inputs. Linear regression, decision tree regression, elastic net regression, K neighbors regression, support vector regression, random forest regression, gradient boosting regression, histogram-based gradient boosting regression, voting, and stacking models were tested during the study. To ensure the validity of the models, the 10-fold cross-validation method was applied in the model training process. In the 10-fold cross-validation, the dataset is divided into 10 parts. One of these parts is used as the test and the remaining nine parts are used as the training dataset. This process is repeated 10 times. To ensure each of these parts, it is used at least once as the test dataset. The accuracy of the model is then determined by averaging the accuracy values for all folds of the test datasets. The regression metrics of the models using different learning methods are shown in Table 4. The metrics used to assess models were coefficient of determination (R), negative root mean squared error (NRMSE), negative mean absolute error (NMAE) and negative mean squared error (NMSE).
# Aging Performance and an Improved Evaluation Method for PE80 and PE100 Pipelines for Urban Gas
## 1 Introduction  
As a kind of polymer material, PE pipes inevitably age during the service process [1; 2; 3; 4]. There are three main types of aging: photo-oxidative, thermo-oxidative, and stress cracking (Table 1). For buried PE pipes, the thermo-oxidative process is the main process, which also includes stress crack expansions. The aging process depends on the material, the environment, and the working conditions [5].  
There are two main methods for research on the aging life prediction of PE pipes. Some research is focused on the properties of the PE material [2; 6; 7]. In this research, the PE specimens were required to be made into plate-like samples for aging experiments. For example, based on cyclic cracked bar (CRB) tests on four types of pipes, including PE80, PE80-MD, PE100, and PE100-RC, a life prediction method was proposed by [PERSON] et al. [8], combined with linear elastic fracture mechanics. According to thermal oxidative aging experimental results, the relationships between oxidation induction time (OIT) and mechanical properties were explored by [PERSON] et al. [9], including PE63, PE80B, PE80, and PE100 pipes. However, the aging characteristics of the pipes cannot be actually reflected according to these kinds of specimens. Another piece of research was about integral PE pipes aging under experimental conditions that were more similar to actual aging conditions. According to thermal oxygen aging tests, a life prediction method for PE80 considering the inner pressure was proposed by [PERSON] [10; 11]. Based on the thermal oxidative aging tests of PE100 pipes under cyclic pressure loading, a method for service life predicting of PE100 pipes was established by [PERSON] [12; 13]. However, research about the aging status of PE pipes with different diameters is not enough. An effective method for aging status evaluating of PE pipes has not yet been established. Based on the common thermal oxygen aging method, the experimental equipment was improved by being combined with the operating characteristics of urban gas PE pipes in this paper. Accelerated aging tests of PE pipes for various pressures, materials, and diameters were undertaken. Based on these experimental results, a prediction model for the aging status of PE pipes was proposed.
## 3 Mathematical Foundations
### Non-Uniform Dual-Rate Extended Kalman Filter (NUDREKF)
#### 3.2.2 Formulation of the NUDREKF
\begin{split} P_{k|k-1}^{T}&=\left.\frac{ \partial f_{MR}}{\partial\xi}\bigg{|}_{\widehat{\xi}_{k-1|k-1}^{T}}\cdot P_{k- 1|k-1}^{T}\cdot\left[\left.\frac{\partial f_{MR}}{\partial\xi}\right|_{ \widehat{\xi}_{k-1|k-1}^{T}}\right]^{\top}\\ &\quad+h_{MR}(\widehat{\xi}_{k-1|k-1}^{T})\cdot Q_{k-1}^{T}\cdot \left[h_{MR}(\widehat{\xi}_{k-1|k-1}^{T})\right]^{\top}\\ K_{k}^{T}&=P_{k|k-1}^{T}\cdot\left[\left.\frac{\partial g_{MR} }{\partial\xi}\right|_{\widehat{\xi}_{k-1|k-1}^{T}}\right]^{\top}\\ &\quad\cdot\left(\left.\frac{\partial g_{MR}}{\partial\xi} \right|_{\widehat{\xi}_{k-1|k-1}^{T}}\cdot P_{k|k-1}^{T}\cdot\left[\left.\frac {\partial g_{MR}}{\partial\xi}\right|_{\widehat{\xi}_{k-1|k-1}^{T}}\right]^{ \top}+R_{k}^{T}\right)^{-1}\cdot(\Delta^{y})_{k}^{T}\\ P_{k|k}^{T}&=P_{k|k-1}^{T}-K_{k}^{T}\cdot\left.\frac{ \partial g_{MR}}{\partial\xi}\bigg{|}_{\widehat{\xi}_{k-1|k-1}^{T}}\cdot P_{k| k-1}^{T}\\ \widehat{\xi}_{k|k-1}^{T}&=f_{MR}\left(\widehat{\xi}_{k-1}^{T} \right)\\ \widehat{\xi}_{k|k}^{T}&=\widehat{\xi}_{k|k-1}^{T}+K_{k}^{T} \cdot\left(z_{k}^{T}-g_{MR}\left(\widehat{\xi}_{k|k-1}^{T}\right)\right)\end{split} \] (18)where \(K_{k}^{T}\) is the Kalman filter gain, and \(Q_{k}^{T}\), \(P_{k|k}^{T}\) are covariance matrices (see more details in Appendix A).
## 4 Discussion  
In order to justify the observed superiority of our method to those compared, we conducted more experiments on Jilin-1 and Skysat datasets. We selected images by sampling at fixed intervals. This strategy can preclude the influence of subjectivity or the selection of specific images for biased evaluations. In the experiment on Jilin-1, the 70 th frame was taken as the beginning frame and the interval was fixed to 40. In the experiment on Skysat, we took the 50 th frame as the beginning frame and interval 50. The video from Skysat was longer than that from Jilin-1, so its range was set wider. In the experiments, the parameters of the model were consistent with those set in previous experiments for data from two satellites.  
The quantitative evaluation indexes of the results are shown in Figures 10 and 11, respectively. In the experiment on Jilin-1, the average P, R, and F values of test frames were 0.81, 0.92 and 0.86, respectively, which are also much better than those for GMM, IFD, and ViBe. The standard deviations of P, R and F were 0.03, 0.02 and 0.01, respectively. This indicates that our method is accurate and stable.  
In the experiment on Skysat, the average \(P\), \(R\), and \(F\) values for the test frames were 0.80, 0.81 and 0.80, respectively, which are also better than those for GMM, IFD, and ViBe. The standard deviations of \(P\), \(R\), and \(F\) were 0.02, 0.01, and 0.01, respectively. The same conclusions hold for the Jilin-1 video. Therefore, the performance of our method is highly accurate and stable.  
To demonstrate the application of satellite videos in transportation land planning and management, we conducted another experiment to estimate the speed of the vehicles. We took vehicle number 57 as an example, as shown in Figure 12. It moved 1.775 pixels along the x-axis and 1.419 pixels along the y-axis in consecutive frames. The spatial resolution of Jilin-1 is 0.91 m, and the temporal interval is 10 frames/s. We can infer that the speed of this vehicle is 16.15 m/s along the x-axis and 12.91 m/s along the y-axis. Therefore, the total speed of this vehicle is 20.68 m/s, or 74.45 km/h. The direction of movement is 38.64\({}^{\circ}\). This result is reasonable for vehicles running on highways.
# Graphene Quantum Dots Doped PVDF(TBT)/PVP(TBT) Fiber Film with Enhanced Photocatalytic Performance
## 2 Experimental Section
### Preparation of PVDF(TBT)/PVP(TBT) Nanofibrous Films  
4.0 g of polyvinylidene fluoride (PVDF) was weighed and poured into a beaker, and 10 g of \(N\),\(N\)-dimethylformamide (DMF), 10 g of acetone, and 2 mL of tetrabutyl titanate (TBT) were sequentially added to the beaker. The beaker was sealed and placed on a magnetic stirrer, and stirred in a water bath at a temperature of 40 \({}^{\circ}\)C for 1 h to obtain a homogeneous PVDF(TBT) solution. Two grams of polyvinyl pyrrolidone (PVP) was weighed into a beaker, and 13.38 g of absolute ethyl alcohol and 2 mL of TBT was sequentially added to the beaker. The beaker was sealed and placed on a magnetic stirrer for 2 h to obtain a homogeneous PVP(TBT) solution. In the preparation of the PVDF(TBT)/PVP(TBT) fiber film, as shown in Scheme 1A, the PVDF(TBT) solution was first electrospun uniformly on the surface of the roller. After 1 h, the PVDF(TBT) and PVP(TBT) solutions were uniformly electrospun on the roller as shown in Scheme 1B. The parameters of the PVDF(TBT) and PVP(TBT) solutions during electrospinningning to prepare the films are as follows: voltages of 15 kV and 18 kV, advancing speed of 25 \(\mu\)L/min and 18 \(\mu\)L/min, respectively, and the distance between the syringe and the roller was 15 cm. After electrospinningning for 12 h, the PVDF(TBT)/PVP(TBT) film was removed from the roller. The peeled film was placed in a drying oven and dried at 60 \({}^{\circ}\)C for 6 h. After lowering the temperature in the drying oven to room temperature, the PVDF(TBT)/PVP(TBT) film was taken out and cut into 2 cm \(\times\) 2 cm.
## 5 Results and Discussion  
Regarding Problem N\({}^{0}\)I, the main results obtained from the DDS for stock control are as follows: the knowledge, at any time, of how much there is in stock of every product--with the implementation of the Control Sheet and the \"Weekly Control\"; the elimination of the waste \"Overstocking\" (and opposite situation of lack of stock)--with the \"New Project\" control; and the reduction in the \"Movements\" waste--due to the coding of all the products that the company possesses. Regarding Problem N\({}^{0}\)I, comparing the values presented in Tables 6 and 7 (the Current State and Future State values), it is possible to verify that through the implementation of the Standardized Work, SS, and Kaizen tools, it is possible to obtain a complete elimination of the activities classified as NVA (pure waste) according to Equation (3), and a reduction of about 47% of the necessary waste (NNVA) according to Equation (4). This way, is possible to reduce the total Lead Time by about 26% (Equation (5)), which, consequently, allows a theoretical increase in production of approximately 33% (Equation (6)), considering that the company only repairs seats in the light motor vehicle section. \[\frac{0-1.503}{1.503}\times 100\;\approx-100\% \tag{3}\]  
\[\frac{13.022-24.423}{24.423}\times 100\approx-47\% \tag{4}\]  
\[\frac{22.042-29.801}{29.801}\times 100\;\approx-26\% \tag{5}\]  
\[\frac{28-21}{21}\times 100\approx 33\% \tag{6}\]  
It should be noted that this study refers to the most critical process of seat repairing, i.e., considering some steps that do not happen every time an upholstery is maintained and repaired, namely, the painting of the seat structure, which only happens if the customer specifies it, and the drying of the foam, which only occurs if the foam is damp. Thus, in Gemba, in most repairs, these two steps are not considered, which means that the increase in production will be higher than 33% as well as the monetary return associated with it. Also, through the implementation of the proposed Kanban, it is possible to reduce the lost time between projects, as well as streamline the operation of the shop floor. The implementation of the proposed Poka-Yoke (associated with the implementation of a fixed Quality Control) will allow the reduction of defects and the extra time associated with their rectification. It is relevant to mention that, despite the lack of literature regarding vehicle seat productive process optimization, it is similar to an upholstered furniture process optimization, and the results obtained were similar to the ones obtained in optimization studies in said industry.
# Testing the Effectiveness of Voxels for Structural Analysis
## 1 Introduction
The second approach employs multiple techniques: (i) the simplification of the triangular mesh prior to its conversion into a volumetric form, which may introduce significant discrepancies between the actual and simulated shapes [14]; (ii) a streamlined representation of shapes as discretized profiles that provide a low-resolution depiction of the structure as a whole, from which a volumetric model can be derived [15; 16; 17]; and the adaptation of the obtained 3D model into a parametric format suitable for volumetric transformation [18]. The third strategy disregards the mesh entirely, opting instead to generate a volumetric approximation of the 3D mesh from the point cloud [19], which the same authors later compare with alternative methods [2; 20]. The fourth methodology utilizes a reality-based 3D model to create HBIM models intended for finite element analysis (FEA), involving two levels of approximation: (i) the initial phase pertains to the drafting of a BIM model; (ii) the subsequent phase involves generating volumes for FEA from these BIM models. In [21], the authors explored the application of BIM to aid in site observing and administration, extracting thematic information for structural analysis, although they did not conduct a finite element analysis of the structure. Another instance includes the development of HBIM from archival data and laser scanner surveys [22; 23]. Several sequential steps are involved in the creation of volumetric models for structural analysis, each of which adds a degree of approximation. Approximations resulting from 3D surface reconstruction are used to convert an unstructured point cloud to a mesh. Retopology simplification improves surface smoothness while preserving high accuracy [24]. To approximate the shape of the object, NURBS is created by applying patches equal to the number of surface elements in the mesh. Considering all these steps, the process starts with accurate data (point cloud), leads to an inaccurate mesh, and, because the structural analysis using FEA adds another approximation, the results will be far from reality.
# Electroencephalogram Emotion Recognition via AUC Maximization
## 5 Numerical Experiments
### Results and Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Logistic Regression**} & **Linear SVM** & **Numerical** \\ \cline{3-5}  & & **Train/Test** & **Train/Test** & **Train/Test** \\ \hline \multirow{4}{*}{**Accuracy (\%)**} & Set 1 & 66.74/66.63 & **67.44**/**67.25** & 59.08/58.81 \\  & Set 2 & 67.27/67.34 & **68.05**/ **67.74** & 60.10/59.35 \\  & Set 3 & 70.88/70.34 & **72.81**/ **72.24** & 65.41/64.93 \\  & Set 4 & 74.05/72.45 & **75.78**/ **74.25** & 69.94/68.53 \\ \hline \multirow{4}{*}{**Precision (\%)**} & Set 1 & 51.33/53.19 & **55.45**/**56.47** & 42.56/42.64 \\  & Set 2 & 56.10/54.30 & **59.41**/**55.63** & 43.82/42.74 \\  & Set 3 & 63.00/61.19 & **66.18**/**64.79** & 48.96/48.33 \\  & Set 4 & 67.14/64.57 & **69.56**/**67.41** & 53.26/52.37 \\ \hline \multirow{4}{*}{**Recall (\%)**} & Set 1 & 10.58/10.59 & 13.41/13.48 & **63.91**/**63.37** \\  & Set 2 & 11.63/11.27 & 15.34/14.59 & **66.60**/**63.37** \\  & Set 3 & 31.99/30.62 & 38.71/37.03 & **73.35**/**71.99** \\  & Set 4 & 43.73/41.60 & 48.92/46.64 & **81.87**/**79.69** \\ \hline \multirow{4}{*}{**F1 score**} & Set 1 & 0.175/0.177 & 0.216/0.218 & **0.511**/**0.510** \\  & Set 2 & 0.193/0.187 & 0.244/0.231 & **0.529**/**0.517** \\  & Set 3 & 0.424/0.408 & 0.488/0.471 & **0.587**/**0.578** \\  & Set 4 & 0.529/0.506 & 0.574/0.551 & **0.645**/**0.632** \\ \hline \multirow{4}{*}{**AUC**} & Set 1 & 0.632/0.632 & 0.642/0.644 & **0.645**/**0.644** \\  & Set 2 & 0.652/0.639 & 0.661/0.647 & **0.664**/**0.651** \\  & Set 3 & 0.719/0.709 & 0.743/0.732 & **0.744**/**0.734** \\  & Set 4 & 0.782/0.763 & 0.807/0.787 & **0.812**/**0.791** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison across models on train/test sets, with recall, F1-score, and AUC as key metrics in handling the imbalanced dataset. Figure 5: Confusion matrices for **(a)** Logistic Regression, **(b)** Linear SVM, and **(c)** Numerical Optimization. The precision scores are 0.646, 0.674, and 0.524, and the recall scores are 0.416, 0.466, and 0.797 for **(a)**, **(b)**, and **(c)**, respectively.
# Turbulent Drag Reduction Characteristics of Bionic Nonsmooth Surfaces with Jets
## 3 Results and Discussion
### Analysis of Nonsmooth Surface with a Jet  
To solve the drastic increase in the pressure drag of the nonsmooth surface, a new active surface based on a bionic nonsmooth surface with a jet was proposed. The bionic inspiration came from sharks. Previous studies have shown that the jetting mechanism of shark gills has a remarkable drag reduction effect; therefore, the shark gill was adopted as the bionic prototype. Accordingly, the jet hole was set  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Surface Type**} & **Total Drag** & \multirow{2}{*}{\(R_{D}\) **(\%)**} & **Pressure Drag** & **Skin Friction** & \multirow{2}{*}{\(R_{f}\) **(\%)**} \\  & **(N)** & & **(N)** & **(N)** & \\ \hline Smooth surface & 1.312 & - & 0.000 & 1.312 & - \\ Nonsmooth surface & 1.897 & \(-\)45.50\% & 1.108 & 0.789 & 39.86\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Drag reduction ratio of the nonsmooth surface.  
Figure 10: Pressure contour of the nonsmooth unit cell.  
on the leeward side of the nonsmooth unit cell, wherein the jet hole was oriented in the direction of the incoming flow. When the fluid flowed through the active surface, the jet hole injected low-speed fluid.  
Figure 11 shows a schematic diagram of the active surface unit. The jet hole was set in the front of the nonsmooth unit cell, whereas the shape of the unit cell remained unchanged. The drag reduction ratios (\(R_{D}\)) of the active surface under different jet velocities are shown in Figure 12.  
The drag reduction effects were different under different jet velocities; however, the total drag on the nonsmooth surface with a jet was lower than that on the nonsmooth surface (\(R_{D}=-45.5\%\)). When the jet velocity was between 7 m/s and 17 m/s, the active surface significantly achieved drag reduction effects, and the drag reduction ratio reached a maximum at 19.35% when the jet velocity was set to 11 m/s.  
The simulation results also show that the active surface had significant effects on the skin friction reduction. The drag reduction effect of the active surface was better than that of the nonsmooth surface, and the skin friction reduction ratio increased with increasing jet velocity (Figure 13).  
Figure 11: Schematic diagram of the active surface unit cell.  
Figure 12: Drag reduction ratio of the active surface.  
Figure 13: Skin friction reduction ratio of the active surface.  
When the skin friction decreased, the relative increase in pressure drag between the smooth surface to nonsmooth surface decreased. The pressure drag of the active surface was smaller than that of the nonsmooth surface (dotted box in Table 3). However, the active surface could not eliminate the net increase in pressure drag.  
The drag changes in the outlet section (surface B) are shown in Table 4. The active surface influenced both the area near the nonsmooth unit cell and the distant areas behind the active surface. The drag reduction ratio in this area also increased with increasing jet velocity.