esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Image Preprocessing and Shape Measurement
### Coordinate Systems' Conversion  
By the proposed algorithm, the various triangle areas within the particle images could be filled with uniformly-arranged balls successfully. However, when combining all of the triangles to form the whole virtual particle, it is necessary to calibrate the coordinate value from the local coordinate system to the global one. The relationships between the two coordinate systems are shown in Figure 4. The X- and Y-coordinate of each filled ball should be adjusted based on Equation (7).  
\[\begin{array}{l}x=x^{\prime}\times\text{cos}b+y^{\prime}\times\text{sin}b \\ y=-x^{\prime}\times\text{sin}b+y^{\prime}\times\text{cos}b\end{array} \tag{7}\]  
where \(x^{\prime}\) and \(y^{\prime}\) are the X-, Y-coordinates in the local coordinate system of the triangles, respectively; \(x\) and \(y\) are the X-, Y-coordinates in the global coordinate system of the particles, respectively; \(b\) is the angle between the bottom side of the triangle (\(S_{1}\)) and the \(X\)-axis.  
It is noted that two different coordinate systems were used in the modeling process. The local coordinate values represented the relative positions of balls in the virtual particles, while the global coordinate values represented relative positions of balls in the virtual container. When developing the single particle, the local coordinate systems were utilized to fill the irregular area with arranged balls. After that, the positions of all the virtual particles were converted to the global one so that the numerous virtual particles could be distributed within the designed area to form the whole mixtures. After the coordinates were calibrated, the virtual particles could be rebuilt as shown in Figure 5. As shown, the inner area of the virtual particles was filled with balls gradually after each triangle area was processed based on the proposed algorithm. However, although the irregular shapes of the particles were divided into several triangles, the side of \(S_{3}\) (Figure 3) could only match the actual shape contour similarly. The shape modeling was not precise enough. Therefore, further improvements were made by modeling the shape contour firstly and then filling the inner area of the shape contour with balls by the former algorithm, as shown in Figure 5. The shape contour in Figure 1b was processed by a user-defined routine to read and store the pixel locations of the images. The black pixels were distinguished. Then, a threshold value of 0.005 was selected to filter the redundant black pixels such that the adjacent black pixels within a distance of 0.005 could be ignored and the others retained. By importing the locations of the retained pixels into PFC2D, the balls could be generated in the shape contour, as shown in Figure 5. Then, the whole particle was filled with balls by triangle area filling in the end. Based on the scanning images and with the help of the proposed algorithm, aggregate particles varying in size and shape were reconstructed as shown in Figure 6.  
Figure 4: Conversions between local and global coordinate systems.  
Figure 5: Procedure of the virtual particles’ modeling.
# Empowering Tourism Accessibility: A Digital Revolution in Pieria, Greece
## Results and Discussion
### Long-Term Impact and Potential for Expansion  
The impact of the Olympus Riviera Digital Platform on the region of Pieria has the potential to be profound, particularly if the platform is expanded to include other high-traffic tourist areas within the region. The long-term vision for this initiative includes continuous improvements in infrastructure and services to meet the evolving needs of all tourists. As more areas in Pieria are audited and included in the platform, the region's reputation as an accessible and inclusive destination will continue to grow.  
In addition to the direct economic and social benefits, the platform also enhances the overall brand and reputation of the Pieria region, which is at the top of the Pieria regional authority's agenda. By positioning Pieria as a leader in inclusive tourism, the initiative sets a benchmark for other destinations to follow. This pioneering approach attracts positive media attention and recognition from organizations that advocate for disability rights and inclusive tourism. As a result, Pieria may become a model for other regions seeking to improve their accessibility infrastructure and attract a broader range of visitors.  
The initiative to promote Pieria as an Accessible Tourist Destination aligns with global trends towards inclusive and sustainable tourism. As more travelers prioritize destinations that offer accessible facilities and services, Pieria's commitment to accessibility positions it as a forward-thinking and progressive destination. This can enhance the region's appeal to tourists who are seeking not only a beautiful location but also a destination that aligns with their values of inclusivity and sustainability. Moreover, the platform could serve as a blueprint for other regions looking to develop similar tools to promote accessible tourism. The success of the platform demonstrates that investing in accessibility can yield significant benefits for a region. As such, other tourist destinations may look to replicate this model, contributing to a global movement towards more inclusive and sustainable tourism practices.
# Algorithm for Base Action Set Generation Focusing on Undiscovered Sensor Values
## 4 Experimental Results
### Discussion  
We found that our proposed algorithm, which features action extraction and combination mechanisms of contributed action fragments, can find more undiscovered sensor values than a random action generation algorithm that is equal to the initial state of the proposed algorithm. Also, appropriate extraction of action fragments in Algorithm A was observed from the results of Figure 10. Four actions showing strong responses were repeatedly used and maintained in Figure 10, and the sensor cover rate of Algorithm A was the highest in Figure 7. Therefore, undiscovered sensor values were found by using those four extracted actions repeatedly, i.e., they were not discarded.  
Thus, the action discarding mechanism is effective for removing action fragments that do not contribute to finding undiscovered sensor values and helps stabilize the performance. Algorithm A with the smaller action generation random rate \(r_{r}\) had a better sensor cover rate in Figure 11. This result demonstrates that undiscovered sensor values could be found effectively by using the action generation method that combines extracted action fragments in Algorithm A. However, sensor cover rates were almost the same when \(r_{r}\leq 0.3\) and its effectiveness in finding undiscovered sensor values peaked around \(r_{r}=0.3\). Thus, \(r_{r}\) should be around 0.3 when we use this algorithm. The robot found  
Figure 11: Sensor cover rate of various \(r_{r}\) values.  
Figure 12: Motion vector heat map of various \(r_{r}\) values.  
Figure 13: Motion vector heat map of various time intervals.  
undiscovered sensor values effectively, meaning the extracted actions the robot used repeatedly changed the external environment capably and generated new situations. Four types of actions--\"Go forward\", \"Go backward\", \"Turn right 90 degrees\", and \"Turn left 90 degrees\"--were extracted and used frequently as the base actions of a differential wheeled robot in this experiment. These base actions differ depending on changes in the environment and the robot body. Base actions can easily be set by hand if both the environment and robot body are simple--like they were in this experiment. However, this would not be possible for a complicated robot body and/or a constantly changing environment. In such cases, the algorithm can obtain a base action set to change the external environment capably.  
Finally, from the results of Figure 13, generated actions depending on action fragment extraction length \(l_{u}\) and biased distribution of robot action could be observed when \(t^{\prime}=l_{u}\). This means that \(l_{u}\) should be larger if longer action is needed. Thus, if the robot needs actions of various time scales, \(l_{u}\) should be changed in accordance with the situation. This issue will be addressed in future work.
# A Review of Foot-Terrain Interaction Mechanics for Heavy-Duty Legged Robots
## 4 Further Research
### Configuration Research of Biomimetic Supporting Feet  
#### 4.1.1 Application of Bionic Technology in Supporting Feet Design  
The common supporting feet of heavy-duty legged robots are feet with passive adaptive joints. Although there are currently cylindrical, spherical, rectangular, and other configurations, there is still a lack of feet that can be used for heavy-duty legged robots in the vast majority of scenarios. In further research, the feet design of the heavy-duty legged robots can adopt biomimetic technology. Biomimetic technology can help expand the application fields of heavy-duty legged robots. The feet of large legged animals in nature can be adopted, as shown in Figure 37. The characteristics and biomimetic design elements of the large legged animals' feet can be shown in Table 7.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Feet of Large Legged Animals** & **Walking Mode** & **Characteristics** & **Design Elements** \\ \hline \multirow{4}{*}{Ostrich feet} & \multirow{2}{*}{Digitigrade} & The didactyl foot structure of ostriches comprises only the 3 rd and 4 th toes. The 3 rd toe has a larger contact area with the terrain than the 4 th toe. & (1) & A special arch is installed on the 3 rd toe. During walking or jogging, the 4 th toe of an ostrich functions as an auxiliary element for load distribution, but it does not make contact with the terrain when the ostrich is running at high speeds. \\ \cline{3-3}  & & & & \\ \multirow{4}{*}{Camel feet} & \multirow{2}{*}{Plantigrade} & When camel feet walk in the sand, they come into contact with the terrain with a thick finger pillow (subcutaneous layer), which can play an elastic buffering effect and have less impact on the sand. & (1) & The imitation camel walking can quickly expand the grounding area after landing on foot. The foot forms a concave grounding shape. \\ \cline{3-3}  & & & & \\ \multirow{4}{*}{Horse feet} & \multirow{2}{*}{Unguligrade} & A horse’s hoo usually has a curved shape, similar to an inverted U-shaped shape. The weight of a horse is mainly concentrated on the hoo wall, not the bottom of the hoo. The bottom of a horse’s hoo is usually flat or slightly raised. & (2) & The biomichetic horse hooves are usually curved in shape to provide stability. \\ \cline{3-3}  & & & & \\ \multirow{4}{*}{Elephant feet} & \multirow{2}{*}{Semiplantigrade} & There is a thick fat foot pad beneath the root bone and metatarsal bone of an elephant’s foot. & (1) & The foot configuration is cylindrical. \\ \cline{3-3}  & & & & \\ \cline{3-3}  & & & & \\ \cline{3-3}  & & & & \\ \cline{3-3}  & & & & \\ \end{tabular}
\end{table}
Table 7: Characteristics and biomimetic design elements of large legged animals’ feet.  
Figure 37: Large legged animals and their feet: (**a**) running ostrich; (**b**) camel walking in desert; (**c**) running horse; and (**d**) walking elephant.
# Detection of Denial of Service Attack in Cloud Based Kubernetes Using eBPF
## 5 Results and Discussion
### Memory Overhead  
Memory also plays an important role in the use of microservices and has several impacts on it. To determine the memory utilization on the nodes of the Kubernetes cluster, i.e., the master node and the worker node, we have used the SAR tool to record the average 10-second stats. To demonstrate that the proposed solution does not create any type of overhead, we have measured it on the master node and the worker node.  
1. Memory overhead on the Master Node  
We have executed our proposed solution on the master node of the Kubernetes cluster and it shows in Figure 6 that our solution does not create any type of overhead on the memory of the master node. The difference between the recorded values of the programs running with the eBPF or without it is between 0.
## 1 Introduction  
The use of ultra-light, hardened, and difficult-to-cut materials such as Inconel and titanium alloys is rapidly increasing, especially in high-tech flagship industries such as aviation, space, and automobiles. Inconel, a representative super-heat-resistant alloy, has extremely desirable features, such as high strength, tenacity, high-temperature durability, and corrosion resistance [1; 2; 3]. These mechanical properties, however, reduce the machinability and thus, cause problems such as economic loss due to the wear and breakage of tools, resulting from the heat generated during drilling work [4; 5; 6; 7; 8; 9]. To minimize the heat generation during drilling, a large amount of coolant is supplied. However, this leads to the deterioration of the work environment, environmental pollution, and physical damage to the workers, owing to splashing and leakage of the coolant. To address this, minimum-quantity lubrication (MQL)--a method of supplying an eco-friendly cutting oil that maximizes cooling and lubrication while minimizing the cutting oil quantity--has attracted considerable attention, and several efforts have been made to introduce the practical use of this technology [10; 11; 12; 13; 14; 15; 16]. In the MQL method, however, because the oil mist is sprayed with high-pressure air and is thus easily scattered, it is impossible to efficiently supply the cutting oil to the cutting point. In addition, the fact that the oil is easily decomposed by the heat generated during drilling work renders this method less effective than conventional wet processing.  
To overcome these limitations, this study focused on developing an eco-friendly polyhedral oligomeric silsesquioxane (POSS)-containing MQL oil that is more resistant to the heat generated in drilling work, and exhibits a stronger adhesive force toward materials, compared with conventional MQL-specific oil. This was achieved by adding POSS--an organic-inorganic hybrid nanomaterial--to existing MQL-specific oil. For this purpose, the optimal concentration of the POSS ester compound was determined via adhesion experiments in which three kinds of POSS ester compounds were designed and added to the MQL-specific oil at varying concentrations. The resulting cutting oil was used in MQL drilling work on Inconel 601, and the effects were compared with those of normal MQL-specific oil, to evaluate the effectiveness of the POSS-containing MQL oil.
# Active Contours in the Complex Domain for Salient Object Detection
## 4 Experiment Analysis and Results
### Comparison with Recent Models without Active Contour  
The proposed method is qualitatively and quantitatively compared with state-of-the-art algorithms, including Normalized cut-based saliency detection by adaptive multi-level region merging published in Transactions on Image Processing simply abbreviated (TIP) [56], Visual saliency by extended quantum cuts (EQCUT) [57], Context-aware saliency detection (CA) [58], Saliency detection using maximum symmetric surround abbreviated on the name of Achanta (AC), [59] and Saliency filters: Contrast based filtering for salient region detection (SF) [60]. The proposed method is evaluated on part of the MSRA 1000 dataset. The aim of our complex domain algorithm is to boost the salient region that is based on the active contour. Figure 10 shows the comparison results, and Figure 11 shows the precision and recall curves. In Figure 12, we present the curves of true positives vs. false positives. The curves show that the proposed algorithm surpasses the others, except TIP. The Table 2 shows average running time for each algorithm image (image per second) on MSRA-1000 dataset.  
Figure 8: Comparison of our algorithm with three methods on the basis of the number of iterations.  
Figure 9: Comparison of our algorithm with three state-of-the-art methods on the basis of time in seconds.  
We summarize the precision-recall curves and report the F-measure, defined by  
\[F_{\alpha}=\frac{(1+\alpha).P.R}{\alpha.P+R} \tag{25}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **TIP** & **EQCUT** & **CA** & **AC** & **SF** & **Ours** \\ \hline Time (seconds) & 2.6 & 0.852 & 64.313 & 20.13 & 0.458 & 0.519 \\ Code & MATLAB & MATLAB & MATLAB & C++ & C++ & MATLAB \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average Runtime (Images Per Second) for Five State-of-the-art Methods and Our Method.  
Figure 10: Qualitative comparison of the salience results of our model with those of five state-of-the-art models. (**a**) Original images; (**b**) ground truth; (**c**) results of our proposed algorithm ACCD-SOD; (**d**) results of the Transactions on Image Processing (TIP) method; (**e**) results of Visual saliency by extended quantum cuts (EQCUT); (**f**) results of the Context-aware saliency detection (CA) model; (**g**) results of the Achanta (AC) model; and, (**h**) results of the Saliency filters: Contrast based filtering for salient region detection (SF) model.  
The parameter \(\alpha\) is the tradeoff in precision \(P\) and recall \(R\). The recall and prediction are calculated by \(\text{Recall}=\frac{\left|A\cap B\right|}{\left|B\right|}\) and \(\text{Precision}=\frac{\left|A\cap B\right|}{\left|A\right|}\), where \(A\) is the detected object and \(B\) is the ground truth.
## 4 Equivalent Circuit and Design Analysis
The resonance frequency at 11.5 GHz is triggered by the components of L6 and C6. Controlling the values of these components can change the resonance frequencies.
# Recursive Feature Elimination for Improving Learning Points on Hand-Sign Recognition
## 4 Results and Discussion
### Discussion
#### 4.2.2 The Gap between the Validation Accuracy for Dataset 3 and the Other Datasets  
From the models' accuracy shown in Table 3, we can see that the model trained using Dataset 2 has the highest validation accuracy, while trained using Dataset 3 has the lowest validation accuracy. The gap between the two models is quite huge.  
From the investigation, we found out that, in some cases, Mediapipe failed to detect the hand landmark position correctly when the images are augmented. Figure 10(a) shows a good detection result using Mediapipe, while Figure 10(b) shows a distorted landmark detection as an effect of image rotation.  
These data with incorrect information will become noise when used to train the model and may cause the accuracy to become lower. As already mentioned, Datasets 1 and 3 only provide hand-sign images and the feature extraction process is performed using Mediapipe. Dataset 2 provides a CSV file containing the hand landmark information as an output from Mediapipe, which is used in this study. This makes the data in Dataset 2 more stable and robust. This can explain why the accuracy of the models trained using Dataset 2 is better than for those trained using other datasets.  
Figure 10: Hand landmarks selected using RFE. Eliminated landmarks are not shown in the figure. (a) 15 features, (b) 10 features.  
Figure 11: Detection and extraction of hand landmarks using Mediapipe. (a) Good result, (b) distorted result.  
While it has not been investigated deeply yet, there are some suspicions on why the accuracy of the model trained using Dataset 3 has a very huge gap compared to Dataset 1, which also only provides hand images. This could be attributed to the huge number of images available in Dataset 3 compared to Dataset 1. These images are still being augmented for mirroring and also rotation, making the number of images in Dataset 3 even larger. This may cause Dataset 3 to have more images with wrongly detected hand landmark positions, as shown in Figure 11b. This means more noise is coming to the model during training that reduces the models' accuracy.
# Quantifying Nutrient Content in the Leaves of Cowpea Using Remote Sensing
## 3 Results and Discussion
### Model Calibration
#### 3.3.2 Band Ratio  
Based on the highest 2D-correlation coefficients (Figure 7), two wavelengths were selected to develop the estimation models. The wavelengths used to carry out the band ratio, as well as the calibration equations and R\({}^{2}\) values, are shown in Table 3.  
It can be seen that most of the models used to predict elements using one band or the two-band ratio selected wavelengths located in the red-edge and near-infrared bands. According to [72], selecting wavelengths in these two regions improves the accuracy of the estimate. Selecting wavelengths around 680 nm for phosphorus and potassium is related to the participation of these nutrients in the processes of producing energy for photosynthesis. According to [PERSON] [73], strong absorption of the chlorophyll pigment occurs around 680 nm.  
Another region with strong chlorophyll absorption is close to 740 nm [73]. This wavelength was selected for calcium due to the contribution of the nutrient to an increase in leaf chlorophyll.  
The wavelengths selected to obtain the models for estimating the P, K and Ca content are located in the near-infrared (NIR) region. Furthermore, although it is more suitable for detecting organic compounds, the NIR is also able to extract information about the inorganic fraction of leaf samples, such as Ca, K and other micronutrients [74].  
\begin{table}
\begin{tabular}{c c c c} \hline
**Nutrient** & **Wavelength (nm)** & **Equation** & **R\({}^{2}\)** \\ \hline P & 684 & y = \(-\)89.297x + 8.158 & 0.62 \\ K & 684 & y = \(-\)915.51x + 74.546 & 0.63 \\ Ca & 720 & y = 208.12x \(-\) 41.659 & 0.67 \\ \hline \end{tabular}
\end{table}
Table 2: Models for estimating leaf nutrient content using a single spectral band.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Nutrient** & **Band Ratio (nm)** & **Equation** & **R\({}^{2}\)** \\ \hline P & 826/750 & P = 34.63x \(-\) 32.35 & 0.66 \\ K & 744/816 & K = \(-\)256.09x + 265.54 & 0.70 \\ Ca & 720/844 & Ca = 89.675x \(-\) 33.934 & 0.63 \\ Zn & 1252/1148 & Zn = 6818.2x \(-\) 6643 & 0.66 \\ \hline \end{tabular}
\end{table}
Table 3: Models for estimating leaf nutrient content using spectral band ratios.