esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method
### Haar Cascade Classification
#### 3.2.2 AdaBoost (Adaptive Boosting)  
AdaBoost was further used to choose the useful Haar-like features and train the classifier. AdaBoost is a machine learning technique that creates a strong classifier by combining multiple weak classifiers. In our system, we employed various masks of Haar-like features paired with thresholds as individual weak classifiers. Subsequently, AdaBoost selected the useful weak classifiers and combined them to form a strong classifier. Finally, we adopted the Haar cascade classifier approach to integrate the strong classifiers into a multi-stage classifier. An advantage of this approach was that each stage underwent threshold-based detection, ensuring recall and reducing false-positive rates. The approach is illustrated in Figure 8.
## Materials and Methods
### IR Thermography  
During thermal monitoring, an Optris PI 640 IR camera (Optris GmbH, Berlin, Germany) captured the thermal history during the printing process with a resolution of 640 \(\times\) 480 pixels, at 32 Hz. The measured temperature range was set from 0 \({}^{\circ}\)C to 250 \({}^{\circ}\)C, with a sensitivity of 75 mK. During printing, the infrared camera resided on the build plate. To shield the IR camera from direct contact with the build plate, the camera was placed on a piece of expanded polystyrene cut to fit the camera, once the brim was printed. Since the internal temperature of the camera cannot exceed 45 \({}^{\circ}\)C, the recording time was limited. Hence, the camera could capture the full print as well the cooling down up to several minutes after the print, but not until the printer signals that the print could be removed. The intensity of infrared light with wavelengths between 7.5 and 13 \(\upmu\)m was measured and directly converted to temperature by the Optris PIX Connect software, which was used to analyze the temperature variations in the printed samples. For every wall printed at the conditions displayed in Table 2, the two regions of interest were the middle of Layers 10 and 40. To extract temperature profiles from the recorded IR videos, measure areas of 2 \(\times\) 2 pixels were defined at the regions of interest. The emissivity at the measure areas was set to 0.92, which gave good agreement between the measured and set values of the build plate temperature. The time-temperature data at each measure area could be directly extracted and used as input for the FSC device. Previous literature has suggested a correction for the reflected radiation of the nozzle, thereby also circumventing the need to provide a value for emissivity [36]. However, the software accompanying the IR camera used in this research directly converts infrared intensity to temperature and no direct access to measured intensities is provided by the manufacturer. Therefore, no correction was carried out, and the data were used as provided by the software. It should be noted that the IR camera performs an automatic calibration to prevent excessive thermal drift every 7 s, which results in data loss during approximately 1 s.
# AI, Optimization, and Human Values: Mapping the Intellectual Landscape of Industry 4.0 to 5.0
## 4 Theoretical Perspectives
### Comparative Analysis Between Industry 4.0 and Industry 5.0  
Together, the aforementioned technologies represent a shift from automation-driven innovation toward a human-centered, resilient, and ethically grounded industrial paradigm. They enable real-time adaptability, strategic optimization, and values-based design, all of which are hallmarks of Industry 5.0. Likewise, Industry 4.0 and Industry 5.0 represent two consecutive phases in industrial and technological evolution, yet they differ fundamentally in their priorities and philosophical orientation. Understanding their relationship and complementary roles is essential to shaping sustainable and human-centered future development trends.  
While Industry 4.0 focus upon digitalization, automation, and real-time data, it integrates cyber-physical systems, the Internet of Things (IoT), artificial intelligence (AI), cloud computing, and robotics into manufacturing and industrial operations/efficiency, Industry 5.0 further relies on human-centric innovation. It extends beyond technological optimization to prioritize human well-being, societal values, and environmental sustainability, whilst emphasizing collaboration between humans and machines rather than replacement, aiming to reintegrate the human into advanced manufacturing in a sustainable way (Table 5).  
The main identified trends might be as follows:  
First, while Industry 4.0 streamlined operations, Industry 5.0 asks why and who these optimizations serve, pushing organizations to align with broader ethical and societal goals; it is ensuing a movement away from full automation toward augmented intelligence and collaborative robotics that enhance human capabilities rather than replace them.  
Likewise, Industry 5.0 promotes sustainability through circular economy models, building upon the data and efficiency gains of Industry 4.0 to minimize waste and carbon footprints; in other words, AI and machine learning become more embedded and the focus shifts from just \"smart systems\" to responsible AI, including fairness, transparency, and accountability, paving the way to frameworks that align technological progress with labor rights, digital ethics, and environmental policy.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dimension** & **Industry 4.0** & **Industry 5.0** & **Complementarity** \\ \hline Objective & Efficiency, automation & Human-centric, sustainable innovation & 5.0 builds upon 4.0 \\ \hline Technology & IoT, AI, CPS, Big Data & CoBots, AI with ethics, sustainable tech & Shared infrastructure \\ \hline Human role & Limited or supervisory & Collaborative and creative & Rebalanced integration \\ \hline Value focus & Productivity and cost & Societal well-being and & \\ layer added & reduction & inclusivity & Ethical layer added \\ \hline Sustainability & Optional or secondary & Integral and prioritized & Expands Focus \\ \hline \hline \end{tabular}
\end{table}
Table 5: Relationship between Industry 4.0 and 5.0.
## 4 Proposed Data Collection Algorithms
### Grid and Probabilistic Neighborhood-Based Data Collection Algorithm with Layered-Scan(GPN-LSCAN)  
For 3D UASNs without prior deployment knowledge, we propose the GPN-LSCAN algorithm. This algorithm partitions the network into grids, which allows the AUV to visit the central location of each grid for data collection. Generally, our GPN-LSCAN contains three phases. In the first phase, the network is partitioned into several same size grids according to the parameter \(p\) and the characteristics of the underwater acoustic network. Next, a traveling trajectory with layered-scan is planned for the AUV to traverse all the grids. In the last phase, the AUV travels along the specified path and schedules a TDMA-based multiple access control protocol to collect data from the whole network. We will illustrate the three phases in detail in the following parts.
###### Abstract  
Probabilistic shaping (PS) is a powerful tool that can realize a flexible data rate and high spectrum efficiency. However, the performance of PS is closely related to the quadrature amplitude modulation (QAM) order, source entropy (SE), normalized generalized mutual information (NGMI), and achievable information rate (AIR). In this paper, we investigated the relationship between PS QAM order, SE, NGMI, and AIR performance for the first time in the visible light communication (VLC) system. Under a specific signal-to-noise ratio (SNR), the PS QAM order and SE in this paper can realize a high AIR at a preset NGMI threshold with the lowest computation complexity, which is just 0.1 bit smaller than the highest AIR. Simulation and experimental results show that the NGMI fluctuates between only \(\pm\)0.005 of the preset NGMI threshold, which proves that the scheme proposed in this paper is feasible.  
probabilistic shaping; visible light communication; achievable information rate; normalized generalized mutual information +
Footnote †: journal: Applied Sciences
# CoAR: Congestion-Aware Routing Protocol for Low Power and Lossy Networks for IoT Applications
## 5 Performance Evaluation
### Comparative Analysis
The reason behind the better performance of CoAR is, it because it reacts more precisely to congestion than other protocols. Figure 5: Packet receiving ratio (PRR) on the root: (**a**) Data transmission over time, PRR with respect to the different time intervals; and (**b**) PRR with respect to the varying traffic load. Figure 6: Average end-to-end delay per packet over time. **Throughput. ** It is defined as the total amount of data bytes received at the root node in unit time. Figure 8 shows the throughput of the protocols. The result shows that even though the throughput of all protocols increases with the traffic load, the variation is noticed under high traffic loads. CoAR, and ECRM achieve higher throughput with increasing traffic load compared to RPL. This reveals that both resource control algorithms can work effectively in the network with a higher load. On the other hand, RPL lowers the throughput after traffic load exceeds 360 packets per minute. The reason is that RPL does not utilize the network resource control strategy to alleviate the congestion. CoAR achieves the highest throughput at a high traffic load of 480 packets per minute. In contrast, ECRM achieves the lower throughput for the same data generation rate. The figure also depicts that when the traffic load is 480 packets per minute, the throughput of the network achieved by CoAR is 8.87% greater than ECRM. **Energy consumption. ** The energy consumption due to the transmission and reception of per successfully delivered packet from source to the destination is considered. The energy consumption of nodes in the network is shown in Figure 9. We note that the energy consumption of CoAR in the network is significantly lower than the others. Because CoAR loses fewer packets at the buffer and minimizes the number of retransmissions. On the other hand, ECRM and RPL dissipate energy by transmitting and receiving packets which are lost due to the buffer overflow. Figure 8: Throughput with respect to the traffic load. Figure 7: Packet loss with respect to the traffic load.
# Principles and Characteristics of Different EDM Processes in Machining Tool and Die Steels
## 4 Research in EDM of Tool Steel
### Modeling and Simulation of EDM Process
[140] developed a comprehensive mathematical model based on RSM to study the influence of machining parameters, including peak current and pulse-on duration, on SR, average white layer thickness, and surface crack density of M2 die steel machined by the EDM process. The author reported that the minimum SR was achieved at lower peak current and pulse-on duration, i.e., at 2 A and 20 us, while the average white layer thickness could be minimized by keeping the peak  
Figure 13: Variation of volume of material removed per discharge as the operation progresses [106]. current as low as possible (preferably in the range of 2-5 A) and by keeping pulse-on duration in the range of 163 to 510 \(\upmu\)s. They also reported that the medium value of peak current, along with the minimum possible pulse-on time, could minimize surface crack density. Finally, it was found that the optimal combinations to achieve minimum surface roughness, white layer thickness, and surface crack density were 2 A/20 \(\upmu\)s, 2 A/20 \(\upmu\)s, and 9 A/20 \(\upmu\)s, respectively. An axisymmetric 2D thermal model for powder mixed EDM was also developed by [PERSON] et al. [114]. To achieve different objective functions, many studies were conducted using models and simulation methods, such as one investigation [103] which reported the development of a piezoelectric servo system to improve the gap control in dry EDM. In another investigation [203], an optimization system to generate the optimal process parameters of EDM was proposed. [PERSON] et al. [175] developed a simulation method for wire EDM. Additionally, [PERSON] et al. [201] developed a model to represent the thermal stress of the EDM process, and [PERSON] et al. [109] developed a semi-empirical model for the surface finish of machined workpieces in EDM. Furthermore, [PERSON] et al. [110] established a semi-empirical model of MRR in the EDM process. [PERSON] and [PERSON] [113] developed a wire EDM process model to study the characteristics of machined surfaces. [PERSON] and [PERSON] [207] introduced a statistical model to study the variation of time-lag durations in the EDM process.
## 3 Results
### Application Case  
In this section, we apply the full workflow followed to prepare a real patient of NB tumour taken from the PRIMAGE platform to be used as input for a Finite Element simulation. The patient presented a tumoral mass surrounding the mesenteric artery (Figure 5A) that was diagnosed via magnetic resonance imaging (MRI). Additionally, the cellularity and vascularization maps for the tumour growth models subsequently developed were provided via diffusion-weighted (DWI) and dynamic-contrast-enhanced (DCE) MRI, respectively.  
The tumour segmentation was firstly retrieved from the platform in a common medical image format, in this particular case, an NIfTI (.iii) file with 50 slices. Specifically, the size of the image volume was 512 \(\times\) 512 \(\times\) 50 voxels with a pixel size of 0.49 \(\times\) 0.49 mm and a slice thickness of 5.5 mm. It is worth noting that the tumour was contained within 17 slices out of the 50, further restricting the geometric information available. The segmented stack was automatically processed to transform the slices into a cloud of points defining the surface of the tumour, using 10 intermediate positions to interpolate between slices. This was enough to obtain smooth transitions and preserve fine details, such as the interior vases when obtaining the surface mesh (Figure 5B). It is worth noting that the reconstruction of the surface via 3D Slicer in this particular case was suboptimal using the default values (see Figure 5A, Appendix B Figure A1), as opposed to _im2 mesh_ which generated a smoother surface. The three-dimensional mesh was subsequently computed (Figure 5C), and the cellularity and perfusion maps interpolated to its elements (Figure 5D), closing the process and readying the necessary files to be further processed by any FE software.  
Figure 4: Effects of downsampling on the human pelvis example: (**A**) a visual comparison of the reconstruction using all the slices (top, orange) versus a downsampling of 10% (30/300 slices used) (bottom right, pink) and a downsampling of 5% (15/300 slices used) (bottom left, blue). The dotted white square marks the region where we extract the detail for (**B–D**) after flipping the view for better visualization; (**B**) reconstruction of the cococyx without downsampling; (**C**) downsampling of 10%; and (**D**) 5%, respectively. Arrows on (**C**,**D**) highlight the loss of features with respect to (**B**) due to downsampling.
## 2 Materials and Methods  
The proposed method interprets the target structure by labeling the components of space targets. First, a new component decomposition method is used to decompose space targets into components, with electromagnetic calculations performed for both the individual components and the overall target. Simulation echo datasets are obtained, then three representative polarimetric target decomposition methods (Huynen decomposition [39], Cloude-Pottier decomposition [40], and Krogager decomposition [41]) are selected to construct new polarimetric feature vectors through feature selection. After this, the scattering centers of the target are extracted and support vector machines are employed for scattering center identification and visualization, ultimately achieving structural identification of the target. The basic process is illustrated as shown in Figure 2.
# Design and Development of a Climbing Robot for Wind Turbine Maintenance
## 2 Materials and Methods
### Force Analysis  
A force analysis model was crafted to determine the adhering forces needed in the wind turbine climbing robot and the moments applied in the robot under the static condition. And the robot wheel is assumed to make contact with the tower surface by the gravitational force without consuming energy as illustrated in Figure 5.  
Figure 5a shows that the forces of the climbing robot are the normal force (F\({}_{\text{n}}\)), which is the support force on the wheels and perpendicular to the contact surface; the tension force (F\({}_{\text{t}}\)), which is the force from the steel rope to attain the ideal traction force; the gravitational force (F\({}_{\text{g}}\)), which is the gravitational force of the robot; and the frictional force (F\({}_{\text{f}}\)), which is the force between the rubber wheels and the tower surface. These are the necessary forces for the climbing robot to adhere on the tower surface. Figure 5b illustrate the lateral view  
Figure 4: Climbing robot dimensions: (**a**) side view; and (**b**) top view. Figure 5: (**a**) Forces of the climbing robot; and (**b**) forces in lateral view. of the robot force analysis. It also assumes that there is no slip with respect to the circular truncated cone tower. Practically, no robot can keep a perfect horizontal position because of structure deformation [17]. Because there are two stepper motors installed in one of its body frames in the robot, this will cause the uneven distribution of robot mass that leads to the robot center of mass not coinciding with the other body frame structure. Thus, the climbing robot can hang itself on the tower under static conditions without consuming any energy and resist free falling to the ground. Equations (1) and (2) come from the resultant forces in the \(y\) direction under the equilibrium state of the forces applied to the robot based on Coulomb's force of friction, where the normal forces provide the frictional force by multiplying the friction coefficient together with the inclination angle.
## 2 Materials and Methods
### HPLC Analysis of Lutein  
According to the method of [PERSON] et al. [15] and [PERSON] et al. [16], with slight modifications, the level of lutein in marigold powder and noodles was quantitatively analyzed using high-performance liquid chromatography (HPLC, Ultimate 3000, Dionex, CA, USA) with a UV detector and a C30 column (CT99S05-2546 WT, YMC Co. Ltd., Kyoto, Japan). For analyzing the level of lutein in the noodles, the noodles were freeze-dried and ground to pass through a 100-mesh sieve. The marigold or the noodle powder (0.1 g) was treated with 25 mL of 70% ethanol, and butylated-hydroxy-toluene (10 mg) was added to avoid the oxidation of the lutein. The sample was agitated at 50 \({}^{\circ}\)C for 4 h and centrifuged at 4500\(\times\) g (4 \({}^{\circ}\)C, 15 min). After the supernatant was collected, the residue was re-treated with 70% ethanol one more time. All the ethanolic extracts were combined and evaporated using a nitrogen evaporator (Organomation, Berlin, MA, USA) at 40 \({}^{\circ}\)C. The concentrated extracts were eluted with the use of two mobile phases that consisted of methanol/MTBE/water (81:15:4, \(v/v/v\)) (solvent A) and methanol/MTBE (9:91, \(v/v\)) (solvent B). After being dissolved in 2 mL of solvent A, the samples were filtered with 0.45 um syringe filters (Pall, New York, NY, USA). The linear gradient started from 100% solvent A and decreased to 50% within 45 min, followed by a second linear gradient to 100% solvent B within 15 min. The column temperature was set at 25 \({}^{\circ}\)C and the flow rate was 1.0 mL/min. External neat solvent calibration was performed by diluting suitable volumes of lutein standard (90.0%, Phytolab, Dutendorfer, Germany), and the calibration curve was shown to have a regression equation as follows:  
\[\text{y}=1.6972\text{x}+0.3104\text{ (R2}=0.9999).\]
## 5 Case Study
### Analysis of Optimization Scheduling Indicators for Different Types of Pumped Storage
#### 5.3.3 Analysis of APRD and TPOV under Different Pumped Storage Configuration Schemes  
Figure 10 illustrates the thermal power average peak regulation depth (_APRD_) and the thermal power output volatility (_TPOV_) under different pumped storage configuration schemes. From schemes 2 to 8, both indicators show a downward trend. Among them, both the indicators of scheme 8 are the smallest, and both the indicators of scheme 2 are the largest. Combined with Figure 8, this is because as the flexibility of the pumped storage plants increases, the standard deviation of the net load curve gradually decreases after the upper-level optimization. Consequently, during the lower-level optimization, the adjustment of thermal power becomes smoother. The data reveal that from schemes 2 to 5, the _APRD_ for thermal power drops from 32.07 MW\(\cdot\)h\({}^{-1}\) to 29.61 MW\(\cdot\)h\({}^{-1}\), and its _TPOV_ decreases from 130.62 MW to 125.95 MW. Further, during the transition from schemes 5 to 8, the _APRD_ of thermal power drops to 23.77 MW\(\cdot\)h\({}^{-1}\), and _TPOV_ drops to 114.98 MW. This demonstrates that the T-PSUs are the most effective in enhancing the smoothness of thermal power output.
## 2 Materials and Methods
### Electromyography  
A Telemyo 2400T G2 device (Noraxon, Scottsdale, AZ, USA) compatible with the MyoResearch XP 1.07 Master Edition software was used to record the electrical activity of seven muscles bilaterally: lumbar erector spinae (ES), rectus abdominis (RA), vastus medialis (VM), long head of biceps femoris (BF), lateral head of gastrocnemius (LGAS), tibialis anterior (TA), and fibularis longus (FIB) with the ground electrode placed over the right posterior superior iliac spine. The rationale for choosing these muscles for analysis was as follows: ES and RA muscles are trunk stabilizers, helping dancers to keep their pelvis in neutral position. VM and BF stabilize the knee during demi plie. VM is a medial stabilizer of the patella, preventing its lateral dislocation. BF, besides flexing the knee, also laterally rotates the lower leg in first classical ballet position. The LGAS, TA, and FIB muscles mainly act on the ankle joints. LGAS is a strong ankle plantar flexor during demi pointe releve and also externally rotates the lower leg in first classical ballet position. TA and FIB act antagonistically to stabilize the feet. TA is a foot supinator while FIB is a foot pronator. Figure 1 illustrates the electrode placement. The study subject was prepared for the test following the SENIAM recommendations [16]. First, the skin prepared for the electrode placement was shaved and cleaned with an abrasive and disinfecting agent. Next, a pair of Ag/AgCl surface electrodes (SORIMEX, Torun, Poland, 1 cm in diameter) was attached in the bipolar configuration along the longitudinal axis of the examined muscles. The distance between the electrodes was 2 cm. The correctness of the attachments was verified through the observation of a raw EMG signal during the testing of the muscles. The EMG signal was sampled at a frequency of 1000 Hz and filtered with a hardware bandpass filter (bandwidth: 10-500 Hz).