esa-sceva/satcom-chunk-collection huggingface dataset:
# Defect Recognition in Ballastless Track Structures Based on Distributed Acoustic Sensors
## 5 XGBoost-Based Defect Recognition Approaches for Ballastless Tracks
### Results and Discussion  
The proposed method was compared to three popular classifiers in the field of pattern recognition, such as random forest (RF), gradient boosting decision tree (GBDT) and support vector machine (SVM), that were also trained with the samples in Figure 7. The results of each classifier are shown in Table 4. As can be seen, XGBoost has an average accuracy of 89.34%, which is better than the accuracy of 83.07% for RF, 82.97% for GBDT and 85.56% for SVM.  
Although XGBoost has a high classification accuracy, its accuracy of track defect identification is still less than 90%. One reason for this may be that feature engineering limits the improvement of accuracy to some extent. Moreover, there are currently no stable parameter-tuning methods for some algorithms, and the best parameters are often selected through exhaustive methods, which adds uncertainty to model optimization.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Classifier** & **Label** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{8}{*}{
\begin{tabular}{} \end{tabular} } & 0 & 0.8772 & 0.8152 & 0.8451 \\  & 1 & 0.7368 & 0.7456 & 0.7412 \\  & 2 & 0.8365 & 0.8365 & 0.8365 \\  & 3 & 0.8606 & 0.8404 & 0.8504 \\  & 4 & 0.7198 & 0.7661 & 0.7422 \\  & 5 & 0.9583 & 0.9877 & 0.9728 \\  & 0 & 0.8508 & 0.8370 & 0.8438 \\  & 1 & 0.7321 & 0.7278 & 0.7300 \\ GBDT & 2 & 0.8713 & 0.8462 & 0.8585 \\ Accuracy = 0.8297 & 3 & 0.8679 & 0.8638 & 0.8659 \\  & 4 & 0.7126 & 0.7251 & 0.7188 \\  & 5 & 0.9524 & 0.9816 & 0.9668 \\  & 0 & 0.8636 & 0.9293 & 0.8953 \\  & 1 & 0.8485 & 0.8284 & 0.8383 \\ XGBoost & 2 & 0.9500 & 0.9135 & 0.9314 \\ Accuracy = 0.8934 & 3 & 0.9238 & 0.9108 & 0.9173 \\  & 4 & 0.8225 & 0.8129 & 0.8176 \\  & 5 & 0.9753 & 0.9693 & 0.9723 \\  & 0 & 0.8595 & 0.8641 & 0.8618 \\  & 1 & 0.7938 & 0.7515 & 0.7720 \\ SVM & 2 & 0.8738 & 0.8654 & 0.8696 \\ Accuracy = 0.8556 & 3 & 0.8952 & 0.8826 & 0.8889 \\  & 4 & 0.7459 & 0.7895 & 0.7670 \\  & 5 & 0.9697 & 0.9816 & 0.9756 \\ \hline \end{tabular}
\end{table}
Table 4: Performance of four classifiers.  
\begin{table}
\begin{tabular}{c c c} \hline
**Name** & **Formula** & **Meaning** \\ \hline Accuracy & \((TP+TN)/(TP+FP+TN+FN)\) & It simply measures how often the classifier makes the correct prediction. \\ Precision & \(TP/(TP+FP)\) & It is a measure of correctness that is achieved in true prediction. \\  & & It is a measure of actual observations which are predicted correctly, i.e., how many \\ Recall & \(TP/(TP+FN)\) & It is a measure of actual observations which are predicted correctly, i.e., how many \\  & & It is a number between 0 and 1 and is the \\ F1-score & \((2\times TP)/(2\times TP+FP+FN)\) &
\begin{tabular}{c} \(\text{hadronic mean of precision and recall}\). \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 3: Standard performance metrics of the classifier.
# Multi-Band Polarization Imaging in a Harsh Sea Fog Environment
###### Abstract  
Researchers in many nations are focusing more on the growth and usage of the marine field, and it is apparent that study on the marine field will be the future development trend. The present study adopts the idea of polarization imaging based on liquid crystal phase retarder as a solution to the drawbacks of conventional industrial camera imaging clarity. Various optical thicknesses are employed to characterize the sea fog concentration; an outside optical imaging equipment is constructed for sea fog imaging research; and pictures comprising polarization characteristics may be determined through image processing. Using multi-band as factors, the benefit of polarization imaging in a sea fog environment is assessed objectively using contrast, information entropy, degree of polarization, and other evaluation indices. The results demonstrate that the quality of the polarization image is superior to that of the intensity image and that the outline of the target is more pronounced in the polarization image. Additionally, the polarization imaging effect is better in the 670 nm band, and the polarization contrast is increased by 1.9%. The contrast trend of the polarization picture is impacted by the time period, but it is roughly equivalent to that of the intensity image. This gives a solid platform for target surveys and civic operations under conditions of dense marine fog.  
sea fog; image processing; multi-band polarization imaging; intensity images +
Footnote †: journal: Applied Sciences
# Meander Line Super-Wideband Radiator for Fifth-Generation (5G) Vehicles
## 3 Antenna Design
### Design of 5G FR2 Meander Line Antenna
The width of the meander line affects the antenna's capacitance and inductance, while the length only affects the line's delay. This technique helps to increase the antenna's bandwidth to super-wideband by introducing multiple resonances and reducing the mismatch between the antenna and its feedline. The Ant4 structure is super-wideband and covers 5G micrometer, radar, satellite, and millimeter (mm-wave) applications and high peak gain compared to Ant 2 and Ant 3, as displayed in Figure 5b.
## 5 Performance Evaluation
### Discussion
#### 5.3.2 Computational Complexity  
Our proposed method performs the lightpath establishment procedure every time a new lightpath-setup request arrives, as described in Section 4.2.2. Here, we discuss the computational complexity of the lightpath establishment procedure. In the procedure, it is necessary to examine all possible combinations of routing paths, cores in the links of those paths, and FSs of the cores in order to establish a new lightpath. Let \(N\) denote the maximum number of hops of the routing paths. Because the number of candidate routing paths is \(K\), the number of cores in each link is \(|\mathcal{C}|\), and the number of available FSs is \(|\mathcal{F}|\), the computational complexity is estimated to be \(O(KN|\mathcal{C}||\mathcal{F}|)\).  
This complexity may pose challenges for real-time decision-making in optical control planes or hardware implementations. To reduce complexity, a restricted application of the proposed method, e.g., limiting the number of candidate routes, could be considered. In addition, we expect that the use of Software-Defined Networking (SDN) environments is effective. SDN enables flexible network management through centralized control of the control plane. Recently, SDN-based optical network architectures [29] have been introduced. Applying SDN-based optical network architectures is expected to facilitate a more flexible implementation of the proposed method.
# AI, Optimization, and Human Values: Mapping the Intellectual Landscape of Industry 4.0 to 5.0
## 4 Theoretical Perspectives
### Key Technologies Enabling the Evolution from Industry 4.0 to 5.0
#### 4.5.1 Artificial Intelligence  
AI continues to serve as the cognitive core of modern industry. In Industry 5.0, AI applications are shifting from pure automation to collaborative and creative augmentation, empowering human decision-making and ethical oversight.  
Generative AI and Explainable AI (XAI)  
Generative AI models (e.g., GPT-based systems, diffusion networks) contribute to design, engineering, and content generation by enabling machines to co-create with humans in real time [53; 54; 55]. These tools support ideation, rapid prototyping, and personalization.  
Meanwhile, Explainable AI (XAI) addresses the black-box nature of complex algorithms by providing interpretable outputs. [PERSON] and [PERSON] [56] emphasized that XAI tools enhance transparency, allowing end-users to trust and understand how decisions are made--an essential element in ethical, human-centered environments [15; 49].  
Machine Learning (ML)  
ML forms the analytical engine behind many Industry 5.0 systems. Its predictive and adaptive capabilities are applied in areas such as predictive maintenance, energy optimization, and demand forecasting [21; 28]. Moreover, ML enables responsive systems that adjust in real-time, supporting both operational efficiency and user personalization.  
Cyber-Physical Systems (CPSs) and Digital Twins  
CPSs and digital twins represent the physical-digital interface essential for smart, adaptive environments. CPSs integrate sensors, actuators, and control systems to create intelligent, context-aware infrastructures [12]. Digital twins extend these capabilities by simulating physical assets in real time for enhanced diagnostics, scenario testing, and predictive planning [57; 58]. When paired with AI, they significantly improve resilience and system intelligence.
## 10 Soft Decision Cognitive Radio and Hybrid Overlay/Underlay Cognitive Radio Waveform Design  
### Overview  
Here, we present a novel soft decision cognitive radio paradigm to combine the benefits of underlay CR and overlay CR to maximize the channel capacity and spectrum efficiency. Specifically, the soft decision CR will detect not only if one spectrum block is used or unused as in current spectral sensing, but detect if it is _underused_. Moreover, the instantaneous interference tolerance level of all underused bands will be determined by weighted spectrum estimate (WSE). Based on the instantaneous interference tolerance level, we employ a soft decision spectrally modulated spectrally encoded (SMSE) framework to design hybrid overlay/underlay waveform to distribute transmission power over the entire bandwidth including both unused bands and underused bands to maximize the channel capacity. Current overlay CR and underlay CR can be viewed as two extreme cases of the general soft decision cognitive radio paradigm.  
Figure 44: Illustration of three cases in the heuristic decision rule.
## 5 Experimentation, Results, and Analysis
### Significance of Dynamic Context Modeling
#### 5.5.1 Handling Complex Relationships  
Images often contain objects, scenes, and activities that have complex interrelationships. A simple dog in a picture could be'sitting', 'running', 'playing', or even 'guarding a house'. The action of the dog can be inferred only when the context\(-\)other objects in the image or the background\(-\)is considered. Dynamic context modeling can automatically tune its attention mechanisms to focus on these significant parts of an image that define such relationships, thereby generating more accurate and meaningful captions.
# Convergence and Stability of Explicit and Implicit Schemes in the Simulation of the Heat Equation
## 4 Explicit Methods: Centered, Forward and Backward in Time Differences
### Direct Finite Difference of First Order
#### 4.1.4 Difference Centered Forward in Time  
Approximates the derivative of time in Equation (1) with a direct difference  
\[\frac{\partial\phi}{\partial t}\bigg{|}_{tm+1,\ \ x_{i}}=\frac{\phi_{i}^{n+1}- \phi_{i}^{n}}{\Delta t}+\mathrm{O}(\Delta x) \tag{13}\]  
Implementation of the forward scheme (Figure 4) requires solving a system of equations at each time step. In addition to the complication of developing the code, the computational effort per time step for the backward scheme is greater than the computational effort per time step of the forward difference scheme. The forward scheme has a great advantage over the centered scheme: it is unconditionally stable (for solutions to the heat equation). Therefore, the forward scheme produces a robust computational model, even with the choice of \(\Delta t\) and \(\Delta x\). This advantage, however, is not always appropriate and the centered scheme is still useful for some problems.  
\[\frac{\varnothing(x_{i},t_{n+1})-\varnothing(x_{i},t_{n})}{\Delta t}=d(x_{i},t_ {m}) \tag{14}\]  
Figure 4: Finite forward difference (FTSC). Unknown values are indicated with a black circle and known values with white circles.
# Popular Biochar as an Alternative Substrate for Curly Endive Cultivated in a Soilless System
## 3 Results  
### Physicochemical Traits of Growing Substrates  
Data on physicochemical characteristics of the nine growing substrates are presented in Table 2.  
The highest pH was identified in the growing substrate with 100% biochar produced via a pyrolysis temperature of 700 \({}^{\circ}\)C, followed by the growing substrate containing 100% biochar produced via a pyrolysis temperature of 450 \({}^{\circ}\)C. The lowest pH value was observed in the substrate with 100% peat. Overall, pH increased (from 5.8 to 9.1) with the increase of biochar content and pyrolysis temperature. A similar behavior was observed for EC, K content and particle density.  
ANOVA for N content showed a significant effect of the treatments. The growing substrate with 100% peat had the highest N content, followed by that with 20% biochar produced via a pyrolysis temperature of 450 \({}^{\circ}\)C, which, in turn, showed values higher than the growing substrate with the same peat/biochar ratio but produced via a biochar pyrolysis temperature of 700 \({}^{\circ}\)C. The combination of biochar at 100% and pyrolysis temperature of 700 \({}^{\circ}\)C had the lowest N content.  
The growing substrate with 100% peat had the highest P content. Increasing biochar concentration resulted in a P decrement, whereas pyrolysis temperature did not affect P content in the growing substrates tested. Data collected on Ca, Mg and Na supported the trend established for the P content.  
The combination of biochar at 20% and pyrolysis temperature of 700 \({}^{\circ}\)C had the highest total porosity, whereas, the combinations of biochar at 40% and pyrolysis temperature of 450 \({}^{\circ}\)C and that of biochar at 70% and pyrolysis temperature of 450 \({}^{\circ}\)C showed the lowest values.  
Growing substrate with 100% biochar associated with pyrolysis temperature of 450 or 700 \({}^{\circ}\)C had the highest values in terms of bulk density. Decreasing biochar content resulted in a bulk density increase. On the other hand, pyrolysis temperature did not significantly affect the bulk density of the growing substrates tested.
## 3 Spectral Scattering Imaging
### Analysis of Scattering Images
#### 3.2.1 Extraction of Scattering Profiles  
The raw scattering images usually contain some pixels of unusually high or low intensities, compared to their adjacent pixels, due to the presence of abnormal or defective tissue spots at the surface of fruit [53]. These pixels appear as dark or bright spots in the images, which should be  
Figure 8: (**a**) Typical multispectral and (**b**) hyperspectral scattering images from apple fruit.  
Figure 7: Schematic of a laser-based multispectral imaging prototype for real-time scattering measurement.  
Figure 9: Procedures for analyzing multispectral and hyperspectral scattering images.  
removed in the pre-processing step. A simple spatial filter, including pixel binning, can be used for the purpose. Besides, removal of random noise, residing in both multispectral and hyperspectral scattering images, needs to be done by smoothing or applying a frequency-domain filter.  
A multispectral scattering image can be reduced to a 1-D spatial scattering profile, or scattering profile for brevity, by using a radial averaging technique [24]. Since the multispectral scattering image, as shown in Figure 10a, is basically circular and symmetric with respect to the incident point, it can be divided into a series of concentric annuli of equal width, with an assumption that the pixel intensities are approximately constant within each annulus. To divide the scattering area, the incident center needs to be identified beforehand, which is often defined as the pixel position corresponding to the maximum intensity [24]. Then, the radial averaging is performed by averaging all the pixels within each concentric circular band, thus leading to a scattering profile as shown in Figure 10b. The averaging procedure not only simplifies the data analysis, but also greatly improves the SNR of the data.  
For a hyperspectral scattering image, as shown in Figure 8b, extracting the scattering profile is rather straightforward, since each horizontal line from the image represents a scattering profile at a specific wavelength. Moreover, considering the symmetric feature of the obtained scattering profile, as shown in Figure 10b, one can average the scattering profiles from the left and right sides with respect to the incident center, thus reducing the data by half and further enhancing the SNR.
## 3 The Development of Detection Technology
### Advances in Electrochemical Method  
The optimization of the electrochemical method lies mainly in the optimization and improvement of electrodes, the improvement of detection efficiency, and the development of automatic detection equipment. Many scholars have studied sensitive film materials on electrodes, such as silver thiocyanate, tripod fluorides, graphene-based selective film electrodes, metal oxide sensors, etc. [50; 51; 52; 53; 54]. [PERSON] et al. [55] disclosed a photochemical method for the detection of Cl\({}^{-}\) concentration in water by using a three-electrode system, which was mainly constructed by a bismuth-based compound thin-film working electrode, auxiliary electrode, and reference electrode. The bismuth-based electrode material generates bismuth hypochlorite in liquid and forms a heterostructure with the raw material. The formation of the heterostructure could promote the separation of carriers and improve the photoelectric response of the material. The change in the photoelectric signal had a good linear relationship with the Cl\({}^{-}\) concentration in the range of 0.1\(\sim\)10 mol/L. [PERSON] et al. [56] used ZnPc (Tos)\({}_{8}\) to prepare electrode sensor coating for continuous monitoring of Cl\({}^{-}\) concentration in water, but this sensor had the highest selectivity to sulfate, and the detection was interfered with by bicarb, bromide, and sulfide. Generally, the concentration of bromide and sulfide in water was low, which was not enough to cause strong interference with the chloride test. Bicarbonate needed to be pretreated before testing. The concentration of Cl\({}^{-}\) in the method was between 2.018 \(\times\) 10\({}^{-5}\) mol/L and 2.514 \(\times\) 10\({}^{-3}\) mol/L, and the detection limit was 50 \(\upmu\)mol/L. The precision of this method was slightly lower than the titration method, but its simplicity was much higher than the titration method.
# Development of a Combined Orchard Harvesting Robot Navigation System
## 2 Materials and Methods
### Overall Design of the System
#### 2.1.2 Platform Construction of the Master-Slave Navigation in an Orchard  
The hardware structure of the orchard master-slave robot cooperative navigation system is shown in Figure 3. The crawler robot that pulls the transport trailer is used as the master robot (the overall vehicle length after pulling the trailer is 2.05 m), and the crawler robot that performs picking work is used as the slave robot. The orchard master-slave robot cooperative navigation system consists of a positioning unit, a central control unit, a bottom control unit for the master robot, a positioning unit, a central control unit, and a bottom control unit for the slave robot.  
Figure 3: Hardware structure of the orchard master-slave robot cooperative navigation system.  
Figure 2: Overall framework of the orchard master-slave navigation system.  
The central control unit of the master robot in a master-slave cooperative navigation system utilizes a UNO-2484G computer with a Core i7 CPU processor, 8 GB of memory, USB data transfer support, a wireless network card, and support for 802.11 AC wireless protocol. The underlying control unit of the master robot includes a DBL6050-2E DC brushless motor controller and two 48 V brushless DC motors. The DC motors inside them carry incremental encoders. The mobile station in the positioning unit of the master robot has a Shanghai Sinan M600 and communicates with a Sinan T300 type base station at low frequency (461.050 MHz). This RTK-GNSS (M600) positioning accuracy is at the centimeter-level, with a horizontal accuracy of \(\pm\)1 cm and a heading angle accuracy of 0.57\({}^{\circ}\). The LIDAR in the positioning unit adopts Velodyne VLP-16 line LIDAR, which is set above the center point of the robots, where the horizontal scanning range of the LIDAR is 0-180\({}^{\circ}\), the vertical scanning range \(\pm\)15\({}^{\circ}\), the measurement distance 0.4 m to 100 m, ad and the resolution 0.1\({}^{\circ}\).  
The system software of the combined orchard harvesting robot runs on a Linux system, and the system software of each robot is composed of three modules: an information interaction layer, an information processing layer, and an execution layer. The software system structure is shown in Figure 4. The information interaction layer is based on the Linux system model for data interaction between the picking robot and the transport robot. The information processing layer is based on the robot operating system (ROS) navigation program for the picking and transport robot group, and the execution layer is based on the Linux system mobile platform control. According to the speed information output from the information processing layer to the underlying servo controller, the autonomous walking of the picking and transporting robot is realized.
# Evaluation of Glottal Inverse Filtering Techniques on
## 3 Experiments
### Error Measures  
For the assessment of the compared GIF techniques, we used a set of well-known temporal and frequency-domain performance measures computed at the pulse level. A total of 28.193 (14.097 for female and for 14.096 male, respectively) pulses were considered. These measures allowed us to evaluate the estimated signals of glottal flow in comparison with their respective ground-truth counterparts from a global perspective but also for each subset of speech features (e.g., phonation types, fundamental frequencies, or vowel type).  
First, the relative RMS distance (in %) between normalized and ground-truth glottal signals was obtained for every considered period of the signal according to the CGIs [30]. Moreover, the following four error measures related to voice quality were also calculated for both the estimated and ground-truth glottal signals: (i) average normalized amplitude quotient (NAQ, in %), defined as the relative duration of the glottal closing phase, which has been used in the estimation of voice quality variation along the breathy-to-pressed continuum [31; 37]; (ii) H1H2 (in dB), a measure widely used to characterize voice quality [17] and defined as the difference between the amplitudes of the first and second harmonics of the glottal flow spectrum; (iii) the harmonic richness factor (HRF, in dB), which is defined as the ratio of the sum of the amplitudes at the harmonics in the glottal waveform to the amplitude of the component at the fundamental frequency and which has shown good correlation with the phonation types [31; 38]; and (iv) spectral tilt (ST, in dB/decade), which describes the spectral slope of the GS using a simple linear regression of the harmonic amplitudes below 5 kHz [25] and has also been found to correlate well with phonation type [38] as well as vocal effort [39].  
Following other similar studies, such as [25; 31], the NAQ, H1H2, HRF and ST measures computed at the pulse level for both the glottal flow output of a given GIF technique and the corresponding reference signal were used to obtain a certain error measure; this was, specifically, the absolute error (in dB) for H1H2, HRF, and ST, while it was the absolute relative error (in %) for NAQ. The obtained results were analysed globally and also per vowel, phonation type, and \(F_{0}\) value. In all cases, the results for both the male and female vocal tracts were also compared in order to analyse their influence on the set of selected GIF methods.