esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Design and Results
### Findings and Analysis from the Experiments
#### 4.3.4 P-R Curve  
The precision-recall (P-R) curve is a widely adopted approach for evaluating the performance of classification models, particularly when dealing with imbalanced datasets [39]. PR represents \"_Precision_\" and \"_Recall_\", two essential metrics in classification evaluation. Precision is the proportion of true positive samples among the samples predicted as positive by the classifier. In other words, it measures the accuracy of positive predictions made by the model. Recall, on the other hand, is the proportion of true positive samples among all the actual positive samples. It gauges the model's ability to capture all positive instances correctly, without missing any. The P-R curve offers valuable insights into how well a classification model performs, especially in scenarios where class distribution is imbalanced. The calculation formulas are as follows:  
\[Precision=\frac{TP}{TP+FP} \tag{13}\]  
\[Recall=\frac{FP}{FP+TN} \tag{14}\]  
In this context, _TP_ represents the count of true positives, which signifies the instances correctly predicted as the positive class. _FP_ refers to the count of false positives, representing the instances incorrectly predicted as the positive class. FN represents the count of false negatives, indicating the instances that were incorrectly predicted as the negative class. Firstly, the classification model is used to predict the samples in the test set, and the probabilities or confidences of each sample are calculated based on the predictions. Secondly, the samples in the test set are sorted based on their probabilities or confidences. Starting from the sample with the highest probability or confidence, each sample is added to the positive set one by one, and the precision and recall are calculated at each step. Finally, the precision and recall values are plotted on a coordinate system to form the PR curve.  
Average precision (_AP_) is a performance metric utilized to assess information retrieval systems, object detection, classification models, and various other tasks. It takes values between 0 and 1, where higher values correspond to superior model performance. A higher _AP_ means that the model can maintain a higher precision at different recall levels.  
\[AP=\sum_{n}{(R_{n}-R_{n-1})P_{n}} \tag{15}\]  
Figure 13: Predicted incorrect samples. ((a) Normal Operation; (b) Fluid Pound; (c) Gas Interference; (e) Delayed Closing of Traveling Valve).  
We have generated the PR curves for each class, as illustrated in Figure 14. Observing the graph, it becomes evident that the curves for each class exhibit a well-balanced nature, with average precision values exceeding 0.98. This observation indicates that the model performs exceptionally well in terms of detecting incorrect classes and overall model performance.
# Real-Time State Evaluation System of Antenna Structures in Radio Telescopes Based on a Digital Twin
## 6 Example Analysis and Results
### Operating Result
#### 6.2.2 The Training Results of the Surrogate Model  
Figure 15 is the mechanical state cloud diagram of the primary reflector of the experimental prototype, where Figure 15 represents the wind pressure cloud diagram at a 90\({}^{\circ}\) elevation angle, a 90\({}^{\circ}\) wind direction, and a 15 m/s wind speed. Figure 15,\(c\) represent the total deformation cloud diagram of the coupling field at the elevation angles of 90\({}^{\circ}\) and 5\({}^{\circ}\) under the above wind field, respectively. From the results, it can be seen that the panel is greatly affected by the wind and is the main wind-bearing part; from the total deformation cloud diagram, it can be seen that the overall deformation of the panel is linearly correlated with the angle of elevation.  
According to the real-time simulation field construction method described in Section 3, an RF regression model is established and its prediction accuracy is verified. Two random conditions are selected, namely, working condition 1: 60\({}^{\circ}\) elevation, 5 m/s wind speed, 90\({}^{\circ}\) wind direction; condition 2: 15\({}^{\circ}\) elevation, 10m/s wind speed, 0\({}^{\circ}\) wind direction. The \(RMSE\) and \(R^{2}\) between the predicted value and the simulated value are compared.  
Table 2 shows the evaluation data under two test conditions; Figure 16 shows the response relationship between the actual value and the predicted value under the first working condition. Figure 16 shows the comparison diagram of the prediction effect of 100 consecutive node-deformation values. Through analysis and comparison, it can be seen that, compared with the finite element analysis results, the \(RMSE\) of the prediction results of the RF model is at a low level, and the average \(R^{2}\) is greater than 0.97, indicating that the random forest regression model has a more accurate prediction effect and has certain reliability.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Working Condition** & \(RMSE\) **(mm)** & \(R^{2}\) \\ \hline working condition 1 & 1.20 \(\times\) 10\({}^{-4}\) & 0.982 \\ working condition 2 & 1.47 \(\times\) 10\({}^{-4}\) & 0.977 \\ \hline \end{tabular}
\end{table}
Table 2: Accuracy evaluation of the surrogate model.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Structure** & **Before Simplification** & **After Simplification** & **Simplification Rate** \\ \hline Panel & 27,322 & 14,976 & 45.2\% \\ Back-up structure & 41,786 & 22,464 & 46.3\% \\ Alidade & 51,522 & 24,224 & 53.0\% \\ Subsurface & 12,872 & 6296 & 51.1\% \\ Elevation gear & 21,010 & 11,792 & 43.9\% \\ \hline \end{tabular}
\end{table}
Table 1: Comparison of the mesh-simplification effect.  
Figure 15: The wind pressure cloud diagram and total deformation cloud diagram of the primary reflector of the experimental prototype.
## 7 Future Work  
For practical engineering applications, it is possible to measure the spatial coordinates of three feature points in \"real time\" using the two visual measurement methods [33; 34]. The mirror facet alignment can as such be monitored and guide the mirror facet alignment to improve installation accuracy and efficiency of the mirror unit in a solar dish concentrator. This will involve:  
(1) Real-time measurement of the spatial coordinates of three feature points on the mirror unit by using the two visual measurement methods, where the position parameters of two cameras are known in advance.  
(2) The facet parameters of the mirror unit, adjustment values of the each support bolts, and error distance \(L\) after mirror alignment (theoretically calculated using Equation (18)) will be determined in real-time by the mirror facet calculation method (Section 3.2) and mirror facet alignment method (Section 5). These results will be displayed in real-time on the electronic screen.  
(3) Alignment of the mirror facet by an operator will be performed according to the bolt adjustment values displayed on the screen. In this case, the real-time display of the facet parameters could reduce the position error of the vertex by allowing the operator to make adjustments to the initial facet alignment of the mirror unit. This will improve installation accuracy of the mirror unit and ensure good working efficiency.
The SFE gives information about the sum of intermolecular forces between different molecules at the surface. The wetting is favorable if the substrate has higher surface energy than the surface tension of the liquid equipped with low interfacial surface free energy. The surface quality of RBCs is influenced by filler particle characteristics (type, hardness, size, morphology), filler loading, quality of silane coupling agent, resin matrix content and formulation, and degree of conversion [18; 19]. At a certain finishing and polishing procedure on a given RBC surface, the hardness differences between the cured resin matrix and inorganic filler and hardness differences between the filler and abrasive particles determine the formed surface topography [18]. The filler particle size does not affect the sandblasting procedure. To evaluate the surface quality, the profilometry measurement is a frequently used method that gives information about the surface roughness and quantitative data for surface irregularities. The scanning electron microscopy (SEM) analysis of the surface of the composite reveals further information about shape and morphology that can be undetectable for the stylus tip of the profilometer. The surface free energy is a physical property of the surface of the material that determines how the adhesive makes intimate contact and provides a bond. In certain clinical situations like direct composite restoration repair or luting indirect overlays, veneers, or crowns, the wetting ability of RBCs by dental adhesives has an important role in achieving strong and durable adhesion between the old and new resin-based composite. In this study, the surface quality of one nanohybrid and two bulk-fill RBCs was analyzed with profilometry, SEM, and SFE measurements by measuring the apparent contact angle of water and diiodomethane. Dental adhesives are complex systems and play an important role in the adhesion mechanism of composite repair. The apparent contact angle of five different dental adhesives (one solvent-free, one-step self-etch, and three universal 10-MDP containing) was measured on polished and sandblasted RBC surfaces. The basis of the selection of RBCs and dental adhesives was our previous study, in which these materials were analyzed and used [20; 21; 22] in composite repair. The aim of this study was to investigate the SFE of RBCs regarding surface roughness and wetting ability of dental adhesives.
## 1 Introduction
Thereafter and according to the proposed RGL model, the SNR of the end-to-end relaying channel and a closed-form expression for the outage probability performance of the CC-SIR-RS scheme with constant power-splitting ratio are derived. * A fuzzy logic-based power-splitting scheme for an EH relay with SIR strategy is proposed. This solution takes into account the fading coefficients of the source-relay and relay-destination links and the distance between source and relay nodes as input parameters of the fuzzy logic system to deliver an appropriate power-splitting ratio, leading to a quasi-optimal SNR of the equivalent end-to-end link. * A comparative study on the outage probability performance of the CC-HIR-RS and CC-SIR-RS schemes is carried out, and Monte Carlo simulations are presented to demonstrate the validity of the analytical results. The rest of this paper is organized as follows. Section 2 describes the transmission model. In Section 3, the CC-HIR-RS schemes with constant and optimal power-splitting ratios are presented, and closed-form expressions on the corresponding outage performances are derived. The CC-SIR-RS scheme is presented in Section 4, where the modeling for the soft estimated symbols and a closed-form expression on the outage performance are presented. A subsection is dedicated for the fuzzy logic-based power-splitting approach. In Section 5, Monte Carlo simulation results are presented and evaluated. Section 6 provides a conclusion.
## 2 Related Work  
Most of the early studies on VLC focused on increasing communication rate, signal-to-noise ratio, etc. A barcode-based VLC system between smart devices was proposed in Reference [4], which showed high level of security. Unfortunately, the error rate increased due to the \"blooming\" effect of image sensors caused by uneven light output. In Reference [5], the authors found a way to avoid the \"blooming\" effect. However, the effective transmission distance was too short (about 4 cm), and when the distance was large, the data packet could be partially lost. There are a lot of similar works, such as References [6; 7; 8].  
These works have all been in terms of signal modulation and demodulation, and sought improvement in signal encoding and decoding to transmit a large amount of data. In our previous work [3], for the first time, we pushed VLC from \"encoding + decoding\" to \"encoding + recognition.\" This shows a more powerful practicality than traditional VLC, but this method needs complicated image processing and feature selection, which is not universal. What is more, the system only has 1035 unique LED-IDs under the same light intensity, which greatly restricts \"encoding + recognition\" VLC wider application, and recognition rate is also limited by the accuracy of feature extraction.
# Enhancing Robotic-Assisted Lower Limb Rehabilitation Using Augmented Reality and Serious Gaming
## 4 Proposed Methodology for the Lower Limb Rehabilitation Treatment
### Identification of Targeted Movements in Correlation with Game Modes  
The game modes are specifically designed to focus on movements that support the therapeutic objectives.  
**Football Mode**  
The goal of this mode is to replicate the essential movements required to kick a ball. These movements include the following:  
* Lifting the Foot: Engages the quadriceps and hip flexors.
* Extending the Knee: Increases knee joint stability and stimulates the quadriceps.
* Flexing the ankle: Requires ankle joint flexibility and activates the calf muscles.  
**Color mode**  
This mode involves executing combined motions to touch various colored circles, focusing on the following:  
* Hip Movements: Engages the hip flexors and extensors.
* Knee Movements: Activates the quadriceps and hamstrings.
* Ankle Movements: Involves dorsiflexion and plantarflexion.
# Recent Advances on Surface Modification of Halloysite Nanotubes for Multifunctional Applications
## 3 Application of Surface Modification of HNTs
### As the Adsorbent  
As research pointed out that HNTs are natural occurring hollow tubes, within 10-150 nm diameter, 500-1500 nm length, HNT shave large specific surface area, and a high aspect ratio [51]. Hydroxyl groups mainly exist in the internal surface of the HNTs which are convenient for graft some organics. HNTs have extensive applications for separated and absorbed various metal ions in industrial use due to these special properties [52]. [PERSON] et al. [53] used two-step methods to modify HNTs with APTES and murexide (Mu). The results indicated that HNTs-Mu was absorbed ten-fold higher than original HNTs for Pb (II) at a pH of 1. The phenomena showed that the HNTs-Mu provided available sites for anionic metal complexes. Meanwhile, the HNTs were modified with 2-methoxyyloxyethyl phosphorylcholine (MPC) to adsorbed BSA with the method of phase inversion [54]. The modified HNTs of absorption capacity increased 87% as compared with the pure membrane.  
As is well known, Zearalenone has a strong toxicity damage to the reproductive system. It is necessary to remove the toxicant for the development of animals. The feeder adopts the modified HNTs to adsorb Zearalenone at the sow reproduction and piglet growth stage [55]. The HNTs were modified with stearyldimethylbenzylammonium chloride (SKC). The results demonstrated that functionalized HNTs conspicuously reduced the damage as compared with the Zearalenone-treated one in the aspects of colostrum and milk (\(p\) < 0.05). The modified HNTs possessed superior adsorption property when compared to the unmodified ones for Zearalenone in vivo [56]. The results summarized that the modified HNTs have obviously improved composite ability with Zearalenone than the HNTs in the gastrointestinal tract.
## 2 Materials and Methods
### Synthesis of Cellulose Air Filter and Development of ZIF-8 Cellulose Air Filter (ZCAF)  
The ZIF-8 cellulose air filter (ZCAF) was synthesized using bleached softwood kraft pulp, chosen for its high surface area, porosity, and inherent hydrophobic properties, which are ideal for CO\({}_{2}\) capture applications. Initially, 5 g of bleached softwood kraft pulp was dispersed in 100 mL of deionized (DI) water and stirred to achieve a homogeneous pulp suspension. A solution containing 0.78 g of zinc nitrate hexahydrate dissolved in 6 mL of DI water was then added to the pulp mixture and stirred moderately for 2 min to facilitate the initial coordination of zinc ions with the cellulose fibers.  
Subsequently, a separate solution of 15.8 g of 2-methylimidazole dissolved in 30 mL of DI water was gradually introduced into the reaction mixture under continuous stirring for an additional 2 min. This step facilitated the in situ formation and deposition of ZIF-8 crystals on the cellulose fibers. The resulting milky mixture was dried at 70 \({}^{\circ}\)C for 24 h to ensure proper integration of ZIF-8 into the cellulose matrix.  
The dried ZCAF samples were filtered using a Buchner funnel, thoroughly washed with ethanol and DI water to remove any unreacted species, and then poured into a Petri dish. The samples were freeze-solidified at \(-\)56 \({}^{\circ}\)C for 3 h to enhance the mechanical stability and structural integrity of the air filter. A schematic of the synthesis process is shown in Figure 2. Table 2 presents the ZCAF samples synthesized with varying pulp-to-powder ratios, demonstrating the tunability of the filter's properties based on ZIF-8 loading.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Serial Number** & **Samples** & **Pulp:Powder** \\ \hline
1. & ZCAF1 & 60:40 \\
2. & ZCAF2 & 50:50 \\
3. & ZCAF3 & 40:60 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Synthesized ZCAF samples with varying pulp-to-powder ratio.  
Figure 2: Synthesis of ZCAF using bleached softwood kraft pulp.
# Calibration of Finn Model and UBSCAN Model for Simplified Liquefaction Analysis Procedures
## 2 Constitutive Models  
### Finn Model  
[PERSON] et al. [14] noted that the irrecoverable volume strain (causing the fluid pressure change) and the cyclic shear strain amplitude of sand are related, and this relationship is independent of the confining stress. In addition, the accumulation rate of the volumetric strain decreases as the accumulated volumetric strain increases. An incremental shear volume coupling equation for sand under simple shear loading was proposed as follows:  
\[d\varepsilon_{v}=C_{1}(\gamma-C_{2}\varepsilon_{v})+(C_{3}\varepsilon_{v}{}^{2 }/(\gamma+C_{4}\varepsilon_{v})), \tag{1}\]  
where \(d\varepsilon_{v}\) is the volumetric strain increment per cycle of shear strain, \(\varepsilon_{v}\) is the accumulated volumetric strain, \(\gamma\) is the amplitude of shear strain of the stress cycle in the calculation, and \(C_{1}\) to \(C_{4}\) are model constants related to the relative density of sand.  
[PERSON] [15] proposed an alternative form of \(d\varepsilon_{v}\) that has fewer model constants and is simpler than the original form [14]:  
\[d\varepsilon_{v}/\gamma=C_{1}(\exp(-C_{2}\varepsilon_{v}/\gamma)), \tag{2}\]  
where \(C_{1}\) and \(C_{2}\) are model constants related to the relative density of sand.  
In Equation (2), \(C_{1}\) controls the amount of the volumetric strain increment, and \(C_{2}\) controls the rate of the volume strain accumulation. [PERSON] [15] provided relationships between model constants (\(C_{1}\) and \(C_{2}\)) and the SPT N-value as a reference for users.  
In FLAC [16], a built-in constitutive model, the Finn model, was coded and incorporated Equations (1) and (2) into the Mohr-Coulomb model to simulate the dynamic pore pressure generation under cyclic loading. In practice, Equation (2) [15] is mostly used because of its convenience and simplicity.
# MSPNet: Multi-Scale Strip Pooling Network for Road Extraction from Remote Sensing Images
## 5 Discussion  
The above experimental results demonstrates that our proposed MSPNet obtains new competitive performance over other state-of-the-art methods. However, the road maps extracted by all the considered methods still have some interruptions. This situation exists in some special surface environments. We show two samples as illustrated in Figure 10. The road regions are severely occluded by a large number of trees that are difficult to distinguish in the upper-left corner of Figure 10 (the first row). Figure 10 (the second row) shows some areas of farmland, and the color of the roads is very similar to the surrounding environment. Our MSPNet fails to predicate complete road maps in these challenging regions. In the future studies, some other prior information will be introduced to segment the roads in these challenging regions, such as the direction information of the roads, which may be helpfull to generate more complete results in those occluded regions.  
To fairly evaluate different methods, as shown in Table 4, we list the floating point operations per second (FLOPS) and inference time of our MSPNet and several compared methods. The comparison experiments of all methods are run on a workstation with a NVIDIA RTX 2080 Ti GPU. For fair comparison, the FLOPs and inference time are calculated based on an input size of \(1024\times 1024\). It is seen that the proposed MSPNet obtains  
Figure 10: Two examples that our MSPNet fails. (**a**) Test image. (**b**) Ground Truth. (**c**) Segmentation results of our MSPNet.  
Figure 9: (**a,b**) respectively plot the comparison methods on misclassification percentages of false positive (FP) and false negative (FN) in the results of three samples in Figure 8.  
competitive inference time, while it has a reasonable computational cost compared with other methods. The comparison results illustrate that our proposed MSPNet is suitable for road extraction tasks from remote sensing images.  
During the training process, we apply data augmentation for improving the generalization of the proposed model. We also perform comparative experiments to show the contribution of data augmentation, as shown in Table 5. We find that it gives an improvement of 0.38 in terms of IoU and 0.44 in F1-score. The results demonstrate that data augmentation is a useful strategy to improve performance.
# Combating Web Tracking: Analyzing Web Tracking Technologies for User Privacy
## 2 Tracking on the Web
### Web Tracking Methods
#### 2.1.6 Canvas Fingerprinting  
Canvas fingerprinting is a sophisticated tracking technique that utilizes the HTML5 canvas element to generate a unique identifier for a user's device and browser. This method involves rendering specific graphics within the browser and analyzing the resulting image pixel by pixel. The image is then converted into a hash value, which serves as a unique identifier for the user [27]. Unlike cookies, canvas fingerprinting does not rely on storing data on the user's device. Instead, it leverages the browser's rendering capabilities to create an identifier based on the device's unique characteristics. Additionally, unlike cookie-based tracking, canvas fingerprinting does not require any user action, such as accepting cookies. It operates discrerely in the background when a webpage loads, enabling a more passive and subtle form of tracking. As a result, canvas fingerprinting raises significant ethical concerns, as it functions without explicit user consent, unlike cookies. Despite the privacy issues associated with its persistent nature, certain browsers and tools--such as specialized browsers and extensions--are increasingly being employed as countermeasures to mitigate this form of tracking.