esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessment of Mechanical Properties of Corroded Prestressing Strands
## 2 Investigation of Corrosion in External Tendons
### Corrosion Phenomena in the External Tendon  
Figure 6 shows the profile of the voids in ducts of external tendons and the number of exposed and corroded strands counted by visual inspection for Bridges A and B. Most of the corroded strands were found in the voids, while strands in the grout in sound condition did not show any corrosion. This means that voids in ducts have a strong relationship with corrosion. The voids not filled by grout were mostly located in upper parts near air vents or anchored at high locations. A possible reason for this is that poor quality of grout causing bleeding water. It is known that bleeding water can occur due to a high w/c ratio and wicking effect. This creates voids after the bleeding water has evaporated. Another possible reason is improper grout injection process. At the time the bridges were built, there would have been no perception that air could be trapped inside due to poor regulation of grout injection. Therefore, the air vent for deflating the air was not properly positioned and was not used properly.  
Grout samples from the corroded strands in Bridges A and B were analyzed for chemical content (chloride and sulfate), and the section loss due to the corrosion was measured using Equations (1)-(3) and Figure 5. Strands in areas with no grout were not considered. Figure 7a shows the relationship between the section loss and the chloride content percentage with respect to mass of concrete measured by KS F 2713 from Korean Industrial Standards (KS) [17], and Figure 7b shows the relationship between the section loss and the sulfate content measured by KS L 5120 [18]. This demonstrates that the higher the chloride content, the more severe the corrosion, while the sulfate content had a relatively smaller effect on these bridges.  
Figure 4: Three idealized pit configurations of corroded wires: (a) Type 1: hemispherical pit, (b) Type 2: concave pit, and (c) Type 3: planar pit.  
Figure 5: Measured pitting depths of a corroded strand.  
In short, voids inside the ducts and chloride content of grouts are major factor influencing corrosion. Therefore, appropriate duct connections and proper grouting sufficient to rule out voids and protect the strands against chlorine ions would be an appropriate way to prevent corrosion.  
Figure 6: 3D model of tendon profile, state of grout, and corrosion of Bridges A and B.  
Figure 7: Relationship between chemical state of grout and section loss of strands due to corrosion: (**a**) chloride content to section loss, (**b**) sulfate content to section loss.
# Nonlinear Distortion Cancellation using Predistorter in MIMO-GFDM Systems
## 4 Conclusions  
This study investigated and analyzed the effect of HPA nonlinear distortion GFDM MIMO on the shape of the spectrum, the PAPR, and the BER in the AWGN channel. This research has derived the closed-form signal-to-noise ratio (SNR) in a nonlinear MIMO-GFDM system scenario. Furthermore, it applied predistorter techniques to mitigate distortion effects of the HPA nonlinear MIMO GFDM on the sender side.  
The effectiveness of the proposed predistorter was illustrated by the simulation results which showed the performance comparison of MIMO GFDM with the HPA without predistorter. The results showed that application of predistorter at MIMO-GFDM can reduce the effects of HPA nonlinear distortion. The system with predistorter was shown to have better spectrum parameters, good PAPR, and lower BER values. The predistorter compensated for serious nonlinear distortion caused by the HPA memory which operated in the saturation region. It is clear that the transmitter processing method reduces the effect of nonlinear distortion.  
This study only overcame nonlinear distortions in MIMO-GFDM system transmitters. In MIMO systems, interference is not only due to nonlinear distortion, but also to crosstalk, as a result of the interference between different signal paths. Therefore, a compensation technique for the joint effect of nonlinear distortion and crosstalk in MIMO-GFDM systems should be considered in future studies. In the next work, Alamouti space-time coding will be added to the MIMO-GFDM system.  
Conceptualization, A.E.J., W., and T.S.; methodology, A.E.J., W., and T.S.; software, A.E.J. and T.S.; validation, A.E.J. and W.; formal analysis, A.E.J., W., T.S., and E.; investigation, A.E.J., W., T.S., and E.; resources, A.E.J.; data curation, A.E.J.; writing--original draft preparation, A.E.J.; writing--review and editing, A.E.J., W., T.S., and E.; visualization, A.E.J., W., T.S., and E.; supervision, W., T.S., and E.; project administration, A.E.J., W., T.S., and E.; funding acquisition, A.E.J., W., T.S., and E.  
This research was funded by the Ministry of Research, Technology, and Higher Education throughthe scholarship program BUDI-DN by the LPDP of the Ministry of Finance of the Republic of Indonesia to [PERSON].  
The authors acknowledge the support for this research from LPPM of Institut Teknologi Sepuluh Nopember Surabaya, Indonesia.  
The authors declare no conflicts of interest.  
Figure 16: BER vs. IBO GFDM with and without predistorter.
## 4 Experiment and Results
### Feasibility of MPPT for Small-Diameter Thick-Walled Pipes  
In order to verify the feasibility of the MPPT method for determining the axial cracks in the internal walls of steel pipes, a set of experiments was conducted on the axial cracks of small-diameter steel pipes. A tile-type differential probe was used for the test, as shown in Figure 13a, and the probe lift-off was 0.03 mm. In the experiment, the magnetization current was 17 A, the voltage of the excitation coil was 3 V, and the frequency was 40 kHz.  
Figure 13b shows the experimentally derived signals of the axial cracks in the steel pipes of different wall thicknesses. The results show that obvious detection signals can be obtained for defects at a depth of 1.5 mm in the internal walls of the three types of pipes, but the signal amplitude of the steel pipe with the smallest wall thickness is the largest.
# A Survey of Low-Cost 3D Laser Scanning Technology
## 4 Problems of a Moving 2D LiDAR
### Other Problems
#### 4.3.1 Problem 3.1  
For Categories 1 to 6: The Density Distribution of the 3D Point Cloud Built by a Moving 2D LiDAR  
In Problem 2.1, we discussed the method of avoiding the missed detection of obstacles by changing the distribution of the points in the 3D point cloud. For a 3D point cloud collected by a moving 2D LiDAR, the solutions on how to make its density distribution more reasonable are of research value. In [48], the density distributions of the 3D point clouds collected by a rotating 2D LiDAR and a pitching 2D LiDAR are analyzed, and a conclusion is drawn that the sampling points collected by the laser beam perpendicular to the rotation axis is the sparsest, while the sampling points collected by the laser beam parallel to the rotation axis is the densest. A similar conclusion also has been drawn in [49]. Therefore, when a rotating 2D LiDAR or a pitching 2D LiDAR is used, the attitude of the rotation axis of the 2D LiDAR should be adjusted, so that we can make the denser area of the 3D point cloud covers the more important scanning objects.  
In view of the above-mentioned uneven distribution of the density of 3D point cloud, an obliquely rotating 2D LiDAR has been built in [50] to ensure that the prototype scan the surrounding environment with a more constant density. The density distribution of the collected 3D point cloud can be adjusted by changing the installation tilt angle and the angular velocity of the rotation of the 2D LiDAR, so that the density distribution of the collected 3D point cloud can be more even. In addition, an obliquely rotating 2D LiDAR also has been used in [51; 52; 53]. The 3D point cloud collected by an obliquely rotating 2D LiDAR is grid-like; this kind of distribution can effectively avoid the missed detection of obstacles, especially when the 3D point cloud is sparse [53]. From this point of view, the grid-like distribution of the 3D point cloud is more reasonable.  
[PERSON] and [PERSON] et al. [11; 15] studied the density distribution of 3D point cloud collected by a 3D LiDAR mounted a rotating mechanism. The 16-line 3D LiDAR Velodyne VLP-16 (Puck) is rotated by the rotating mechanism to form a prototype with full field of view. In these literatures, the uniformity of its scanning is optimized.
## 7 Conclusion  
### Summary of Key Findings  
The exploration and implementation of the Dynamic Feedback-Driven Learning Optimization Framework (DFDLOF) have yielded significant insights into the potential of machine learning in personalizing educational pathways. Key findings from this study include:  
Enhanced personalization: DFDLOF has demonstrated a remarkable ability to customize learning experiences, effectively addressing individual learner needs, preferences, and learning styles. This personalization has led to increased learner engagement and improved educational outcomes.  
Dynamic adaptability: One of the most notable achievements of DFDLOF is its dynamic adaptability. The framework has shown proficiency in adjusting learning content and difficulty in real time based on continuous learner feedback and performance data.  
Data-driven insights: The application of DFDLOF has emphasized the value of data-driven insights in education. The framework's ability to analyze extensive learner data has provided educators with a deeper understanding of learning behaviors, enabling more informed instructional decisions.  
Real-time feedback: providing immediate, personalized feedback to learners has been a crucial component of DFDLOF, contributing significantly to learners' understanding and retention of material.  
Scalability and accessibility: case studies, particularly with platforms like Khan Academy and Coursera, have demonstrated DFDLOF's scalability and potential for widespread application, transcending traditional geographical and socio-economic educational barriers.
# Optimization of Stop Plan for Skip-Stop Operation on Suburban Railway Line
## 4 Algorithm Design  
The proposed model includes many decision variables, such as \(f_{1},f_{2}\) and \(x_{ij}\). The two-objective optimization problem described by the optimization model is transformed using the normalization method to a single-objective optimization problem, which can be solved using an algorithm based on genetic algorithm (GA) with elite strategy, the procedure of algorithm is provided below.  
Step 1: Generate the initial population.  
Generate the random initial population of 40 chromosomes. One chromosome has 19 genes, the corresponding gene is a binary variable equal to 1 or 0. Furthermore, one chromosome can be divided into three segments, coding for express train stop plan \(x_{ij}\) (\(2\leq j\leq 10\)), local train departure frequency \(f_{1}\) and express train departure frequency \(f_{2}\), respectively. The binary encoding of the chromosome is shown in Figure 3.  
Step 2: Calculate the fitness.  
Generate the timetable according to the stop plan and frequencies of express and local train which are decoded by the chromosome. Then the overtaking condition is judged according to the safe time interval and the schedule is adjusted when overtaking occurs. If the stop plan and departure frequencies corresponding to the chromosome do not satisfy the constraints, this chromosome is discarded. If it satisfies the constraints, calculate the value of the objective function under the adjusted schedule and the fitness value of each chromosome by using the rank-based fitness method.  
Figure 3: The coding illustration of a chromosome.  
Step 3: Selection, crossover and mutation.  
Select the chromosomes which have higher fitness to form the new subgroups and then generate a new population by using the crossover and mutation methods.  
Step 4: Termination criteria.  
If it reaches the maximum iteration times, stop the calculation and output the optimal result, otherwise, return to Step 2.  
The flowchart of the algorithm is shown in Figure 4.
## 3 Results
### Operating Conditions of the CO\({}_{2}\) Cycle with Mechanical Subcooling  
As mentioned, the operating parameters to be tuned to obtain the best performing conditions of the CO\({}_{2}\) cycle with mechanical subcooling are the pressure at the gas-cooler (P\({}_{\text{high}}\)) and the degree of subcooling provided by the auxiliary system (SUB). To illustrate the behavior of this cycle, the dependence of the overall COP, Equation (9), versus the environment temperature and the subcooling degree for an evaporating level of 0 \({}^{\circ}\)C is presented in Figure 5. Data of Figure 5 are evaluated for the optimum gas-cooler pressures. For environment temperatures below 25 \({}^{\circ}\)C the best results are in subcritical operation and for warmer temperatures in transcritical. As it can be observed, for any environment temperature, an optimum degree of subcooling exists, maximizing the overall COP. Furthermore, it is observed that the subcooling degree increases when going to warmer temperatures.  
Figure 5: COP dependence on the subcooling degree of MS cycle. (T\({}_{\text{o}}\) = 0 \({}^{\circ}\)C).  
Maximum COP for the considered range and the corresponding optimum subcooling degrees are detailed in Figures 6 and 7, respectively, for all the considered range. As it can be observed in Figure 6, the transition between subcritical to transcritical operation occurs, from a theoretical energy point of view, at an environment temperature of 25.3 \(\pm\) 0.2 \({}^{\circ}\)C. Since this temperature is commonly reached in any location, the plant must be designed to be able to operate in subcritical conditions when possible, since forcing it to operate in transcritical would result in reductions of COP. That means that the first CO\({}_{2}\) heat exchanger must be sized as condenser, but it must be ready to operate also as gas-cooler. The trend is the same as in pure CO\({}_{2}\) transcritical systems, as it can be observed in the work presented by [PERSON] et al. [23]. Another important aspect is that the presence of the optimum subcooling degree disappears when temperature difference between T\({}_{\text{C}}\) and T\({}_{\text{H}}\) is high. It can be observed for the operation at \(-5\)\({}^{\circ}\)C and below. It will be mentioned later, but the reason is that at a high temperature lift the MS cycle is overcome by the cascade solution.  
Figure 6: COP of the MS cycle at optimum conditions.  
Figure 7: Optimum subcooling degrees of the MS cycle.  
Finally, the ratio between the power consumption of the auxiliary cycle (R1234 yf) and the main compressor (CO\({}_{2}\)), Equation (10), are represented in percentage for the optimum operating conditions in Figure 8. For the considered range, the needed power consumption of the auxiliary compressor ranges from 4% at an evaporation of 5 \({}^{\circ}\)C and environment temperature of 15 \({}^{\circ}\)C to 21% approximately for 5 \({}^{\circ}\)C at 40 \({}^{\circ}\)C. The most important observation is that sizing the auxiliary compressor for high environment temperatures will cover the operation in transcritical and subcritical without problems.
# Three-Dimensional Localization of Underwater Nodes Using Airborne Visible Light Beams
## 4 The 3D-LUNA Localization Framework
### Mitigating Measurement Errors  
The scenarios described above do not consider errors that can occur during intensity measurement or mobility that can subsequently affect the 3D contour size and position, which in turn can result in non-overlapping contours or disjointed contour clusters. To tackle these issues, we first take a 3D contour corresponding to one VLC transmission, \(\text{Cont}_{1}\), and consider the intersection of \(\text{Cont}_{1}\) with every other contour, \(\text{Cont}_{k}\,|\,\text{k}\
eq 1\), and then calculate the centroid of such an intersection (which will be a 2D polygon). We repeat this for every pair of contours and calculate all centroids in the same way. If a 3D contour is standalone and does not have any intersection with other contours, we calculate its centroid itself. This method ensures increased weight for the shapes that intersect with each other since those intersections will be counted twice. In addition, 3D-LUNA assumes the centroid of all the obtained centroid points to be the estimated position of the underwater node. The steps are summarized in Algorithm 1.  
```
\(\text{S}=\{\text{S}_{1},\text{S}_{2},\dots,\text{S}_{n}\}\), where each \(\text{S}_{i}\) represents a 3D shape. \(\text{C}(\text{S}_{i})\) represents the centroid of shape \(\text{S}_{i}\). \(\text{I}\left(\text{S}_{i},\text{S}_{j}\right)\) represents the intersection of shapes \(\text{S}_{i}\) and \(\text{S}_{j}\). \(\text{C}\left(\text{I}\left(\text{S}_{i},\text{S}_{j}\right)\right)\) represents the centroid of the intersection of shapes \(\text{S}_{i}\) and \(\text{S}_{j}\). 1. Initialize a set of centroids: \[\text{C}=\emptyset\] 2. For each \(\text{S}_{i}\in\text{S}\), \[\text{SetC}_{i}=\text{C}(\text{S}_{i})\] 3. Check for intersections between shapes: \[\text{For each pair}\left(\text{S}_{i},\text{S}_{j}\right)\text{ where i}\
eq\text{j}\text{:}\] 4. Calculate the final centroid: \[\text{C}_{\text{final}}\,=\,\frac{1}{|\text{C}|}\sum\limits_{\text{C}_{i}\in \text{C}}\,\text{C}_{i}\]
```  
**Algorithm 1**Localization for error scenarios  
The step-by-step process for handling overlapping beam contours and computing a refined localization point based on received VLC signals is illustrated in Figure 7. This flowchart captures the core innovation of our framework, namely the use of centroid-based geometric reasoning without requiring depth sensors or surface anchors, enabling accurate and infrastructure-free underwater localization
# The Applications of MALDI-TOF MS in the Diagnosis of Microbiological Food Contamination
## 2 The Principles of MALDI-TOF MS
### Sample Preparation  
The effectiveness of MALDI-TOF MS is highly dependent on the quality of sample preparation. For microbiological applications, a single colony from a culture plate is typically transferred to a MALDI target plate. In the direct transfer method, the sample is overlaid with a matrix solution--most commonly \(\alpha\)-cyano-4-hydroxycinnamic acid (HCCA)--which assists in the desorption and ionization of cellular components. For more robust or encapsulated microorganisms (e.g., Gram-positive bacteria, yeasts), a formic acid-based extraction step is often included to enhance protein solubilization and promote the release of ribosomal proteins, which are the primary targets for identification due to their high abundance and species-specific profiles [41; 42; 43; 44; 45; 46].
# A Multi-Objective Evolutionary Algorithm Model for Product Form Design Based on Improved SPEA2
## 2 Methods
### Improved Strength Pareto Evolutionary Algorithm 2 (ISPEA2)
(3) The MO modifies the results of the crossover and mutation operations with a certain modification probability according to the following equation:  
\[f_{rj}^{t+1}=f_{rj}^{t}+mc_{r}\times\left|f_{rj}^{t+1}-f_{rj}^{t}\right|(j=1,2,3) \tag{17}\]  
\[mc_{r}=\frac{m_{P}(|m_{P}|+rn_{2})}{rn2\times|m_{r}|} \tag{18}\]  
Figure 3:0where \(f_{ij}^{t+1}\) represents the descendant individuals obtained after the crossover and mutation operations, where \(f_{i1}^{t+1}\) is \(g_{ij}^{t+1}\), \(f_{2i}^{t+1}\) is \(k_{ij}^{t+1}\) and \(f_{i3}^{t+1}\) is \(h_{ij}^{t+1}\); \(i\) is a set of all PC numbers; \(i^{*}\) corresponds to the PC numbers with consistent correlation coefficient between PC scores and IEMSs of target adjectives; \(mc_{i^{*}}\) is the modification coefficient of the \(i^{*}\)th PC number; \(m_{2}\) is a random number in the interval [4,5]; \(\lambda_{rz}\) is the correlation coefficient of the \(z\)th target adjective of the \(i^{*}\)th PC number, and it is considered that there is a correlation between two variables when \(\lambda_{i^{*}z}\in[-1,-0.3]\cup[0.3,1]\)[32]; \(Z\) is the number of target adjectives. By replacing the gene corresponding to the position of \(i^{*}\) of \(f_{ij}^{t+1}\) with \(f_{rj}^{t+1}\), the modification operation can be completed. Each operator acts on the algorithm as follows. Firstly, the value range of \(\alpha\) and \(\beta\) in ISPEA2 is not limited to [0,1] in SPEA2, this ensures that the search space of the ICO in ISPEA2 covers all the neighborhoods of \(g_{i}^{t}\) and \(k_{i}^{t}\). Therefore, ICO can make the probability of searching for more different offspring higher and the diversity of ISPEA2 better. Secondly, the IAMO ensures that the mutation rate changes dynamically with the increase of the number of iterations. In the initial stage of the iteration, the global optimal solutions are searched by a large mutation rate, and at the end of the iteration, local optimal solutions are fully searched by a small mutation rate. This improves the convergence of ISPEA2. Finally, the MO helps to achieve the goal of continuously improving the scores of target adjectives in iterations. Since the value range of \(\lambda_{rz}\) and \(m_{2}\) determines the reasonable range of \(mc_{i^{*}}\) as the modification coefficient, which ensures that no matter positive and negative correlation, individual genes consisting of PC scores of the position of the \(i^{*}\)th PC number can be evolved towards the direction of increasing the scores of target adjectives with the pre-set modification probability in each iteration. Therefore, the introduction of MO can significantly improve the convergence of the algorithm. By reasonably presetting the modification probability, it can effectively maintain a good balance between the convergence and diversity of SPEA2. Step 7: Increase the generation number \(t\) by 1 and go to Step 2.
## 3 Results  
### Eye Tracking Experimental Data  
#### 3.1.1 Analysis of Eye Tracking Experimental Data  
The experimental records of each subject were evaluated. If the collection rate of eye movements was 80% low, they were retested. The collection rates of some of the records are shown in Figure 6. The number of gaze points, the variance of the duration of each gaze point, the total gaze time, and the coordinates of all gaze points in each record were derived. For ease of processing, all the above data are treated in Tables 2 and 3.  
Figure 5: Experimental process record. Participants are wearing Tobii Pro Glasses 2 and looking at a grayscale image of a roadblock on the display.  
Figure 6: The collection rates of some of the records. They should be listed as (**a**) Solution 10 Product hotspot map, red represents the longest gaze, followed by yellow, the shortest is green; (**b**) Plot of gaze points in scheme 10. The number and length of gaze reflect the importance and interest of the part of the area being tested.  
The outputs of data visualization include a hotspot map and an eye-track map. The hotspot map uses different colors to present the experimenter's attention area or to show the visual dwell time of the experimenter in a certain area, which can better understand the general pattern of their behavior. The gaze duration of the subjects in the hotspot map indicates that the red color has the longest time, the yellow color is the second longest time, the green color is the shortest time, and the absence of a color-covered area indicates that the experimenter has not paid attention to the area. The number of gaze points (a gaze point can only be regarded as a single gaze if it lasts for more than 100 ms) and the duration of gaze reflects the importance and interest in the part of the area under test.