esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Collection
The third row shows holes classified as \"Red\", indicating that they were made with a significantly worn drill bit, resulting in rough and irregular holes with noticeable defects. These examples are used for research analysis  
Figure 3: Approach to measuring tool degradation: (**a**) appearance of a freshly manufactured tool, (**b**) appearance of a tool after wear [57]. Figure 2: Density profile of the chipboard utilized in the conducted experiments. to train and evaluate the performance of the Convolutional Neural Network (CNN) models in classifying the condition of the drilled holes. The evaluation of the drill's condition was routinely performed using a Mitutoyo microscope (model TM-500), enabling the classification of each hole into one of three categories:  
* Good--represents a new, unworn drill that is suitable for continued use;
* Worn--applies to a used drill that either needs manual inspection to determine its state or is still in a condition acceptable for ongoing production activities;
* Requiring replacement--describes a used drill that is in a condition deemed unserviceable and must be replaced without delay. Table 1 provides a summary of data collection and drill wear measurements. The table includes three columns: Drill Number, Green/Yellow/Red, and Total Images. The Drill Number column lists the five different drills used in the experiments, numbered from 1 to 5. The Green/Yellow/Red column shows the number of images categorized into three classes (Green, Yellow, Red) for each drill, indicating the condition of the drill bit. Specifically, Green represents a new or minimally worn drill bit, Yellow indicates a moderately worn drill bit, and Red signifies a significantly worn drill bit. The Total Images column provides the total number of images collected for each drill. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Drill Number** & **Green/Yellow/Red** & **Total Images** \\ \hline
1 & 840/420/406 & 1666 \\
2 & 840/700/280 & 1820 \\
3 & 700/560/420 & 1680 \\
4 & 840/560/280 & 1680 \\
5 & 560/560/560 & 1680 \\ Total & 3780/2800/1946 & 8526 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of image data collection and drill wear classification. Figure 4: For illustration purposes, examples of drilling holes are categorized into three classes. The “Green” class examples are listed in the first row, followed by the “Yellow” class examples in the second row, and examples of the “Red” class are displayed in the third row. Refer to for additional details. Drill 1 has 840 images classified as Green, 420 as Yellow, and 406 as Red, summing up to a total of 1666 images. Drill 2 has 840 Green, 700 Yellow, and 280 Red images, with a total of 1820 images. Drill 3 has 700 Green, 560 Yellow, and 420 Red images, totaling 1680 images. Drill 4 has 840 Green, 560 Yellow, and 280 Red images, also totaling 1680 images. Drill 5 has an equal distribution of 560 images in each category, amounting to 1680 images. The total count for all drills combined is 3780 Green, 2800 Yellow, and 1946 Red images, leading to an overall total of 8526 images.
# P System with Fractional Reduction
## 3 Principles and Algorithms of Fraction Simplification
### Division Algorithm  
#### 3.2.1 Principle  
The division algorithm, also known as [PERSON]'s algorithm [21; 22] for finding the greatest common divisor of two numbers, is considered to be the earliest algorithm in the world (300 BCE). It first appeared in [PERSON]'s _Principia Geometrica_ (Book VII, Propositions I and II), and in China, it can be traced back to the _Nine Chapters of Arithmetic_, which appeared in the Eastern Han Dynasty.  
The greatest common divisor of two natural numbers is the largest positive integer that can divide them simultaneously. The division algorithm is based on the principle that the greatest common divisor of two integers is equal to the greatest common divisor of the smaller number and the remainder of the division of the two numbers.  
For example, the greatest common divisor of 1254 and 390 is 6 (1254 = 6 \(\times\) 209; 390 = 6 \(\times\) 65); the process of deriving the greatest common divisor using these two numbers is as follows:  
1254% 390 = 84  
390% 84 = 54  
84% 54 = 30  
54% 30 = 24  
30% 24 = 6  
24% 6 = 0  
So the greatest common divisor of these two numbers is 6.  
The proof of this algorithm is as follows:  
Let the two numbers be \(a\) and \(b\) (\(b<a\)) and denote by \(gcd(a\),\(b\)) the greatest common divisor of \(a\) and \(b\). \(r=a\) mod \(b\) is the remainder of \(a\) divided by \(b\), and \(k\) is the quotient of \(a\) divided by \(b\). The division algorithm method is to prove that \(gcd(a,b)=gcd(b,r)\).  
Step 1: Let \(c=gcd(a,b)\), then let \(a=mc\) and \(b=nc\);  
Step 2: It follows from the premise that \(r=a-kb=mc-knc=(m-kn)c\);  
Step 3: It follows from the result of step 2 that \(c\) is also a factor of \(r\);  
Step 4: It can be concluded that \(m-kn\) and \(n\) are mutually exclusive; otherwise, one can set \(m-kn=xd\) and \(n=yd\) (\(d>1\)), then \(m=kn+xd=kyd+xd=(ky+x)d\), and then \(a=mc=(ky+x)dc\) and \(b=nc=yd\), so the maximum convention of \(a\) and \(b\) becomes \(cd\), not \(c\), contradicting the previous conclusion.  
From this, it follows that \(gcd(b,r)=c\), and subsequently \(gcd(a,b)=gcd(b,r)\).
## 3 Experiment Results  
In order to verify the feasibility of the developed methodology and equipment, some experiments were performed in this section. The three experiments that tested the accuracy and stability of the measuring device are: (i) measurement accuracy and repeatability verification; (ii) the effect of feed speed on the accuracy of measurement; and (iii) pixel threshold effect on measurement value.  
As shown in Figure 8, the prototype of the measuring device used for the experimental test was fabricated and assembled. The experiments were carried out on a three-coordinate workbench, and the probe device was vertically fixed on the jig. The plate-like parts to be tested was fixed on the test bench below the probe, and the probe axis was parallel to the axis of the holes. The control unit used in this experiment was NXP's LPC1768 microcontroller, which is based on ARM Cortex-M3 core. The wavelength of the laser diode used in the laser transmitter was 650 nm. The threshold of Value, Hue, and Saturation in the image screening process were both kept fixed. The resolution of the capacitive grating displacement sensor was 0.01 mm. The motor used here is a DC type with a rated voltage of 6V. The frame rate of image uptake is 30 frames per second. It is worth mentioning that in this study we only carry out preliminary experiments to get measurement accuracy and repeatability of the proposed device. The purpose is to verify the feasibility of the measurement method and the corresponding measuring instrument. The device was not compensated and optimized before measurement experiments. In a follow-up study, the accuracy compensation algorithm will be designed to further optimize the measurement accuracy.  
Figure 7: Control flow chart of the measurement process.
# Remote Sensing Image Segmentation of Marculture Cage Using Ensemble Learning Strategy
## 2 Materials and Methods
### Introduction to the Statistical Method of Breeding Density  
Since the semantic segmentation used in this paper is to detect and segment the image at the pixel level, this paper adopts the method of traversing each point in the image. If the point belongs to the mask area, the sum of the segmentation pixels is increased by one, as shown in Figure 6.  
Each square in the figure represents a pixel, A represents the pixel area occupied by the detection frame, and B represents the pixel area occupied by the detected object mask. During the test, all the pixels in A are traversed. When a pixel area is in the A-B area, it is judged as False; when a pixel is in the B area, it is judged as True, and the total number of pixels required is determined. The point value is incremented by one. In the experiment, the number of pixels occupied by each tile is calculated (as in Equation (2)), and then the total number of pixels is obtained by summing the pixels of each tile (as in Equation (3)). The total number of points and the total number of pixels in the picture are used to calculate the cage breeding density (as in Equation (4)).  
\[P=\sum_{i=0}^{k}a_{i} \tag{2}\]  
\[P_{c}=\sum_{j=0}^{m\times n-1}P_{j} \tag{3}\]  
\[\rho=\frac{P_{c}}{P_{total}} \tag{4}\]  
Figure 6: Schematic diagram of area calculation.  
In Equations (2)-(4), \(P\) represents the number of pixels in the mask area in a single tile, \(k\) represents the number of objects segmented in the tile and \(a_{i}\) represents the number of pixels occupied by the mask segmented object in the tile \(P_{c}\) represents the total number of pixels in the identified cages, m and n represent the times of dividing the high-resolution image into rows and columns respectively, \(P_{total}\) represents the total number of pixels in the image, and \(\rho\) represents the cage breeding density.
## 4 Simulation and Experiment Results  
The MPPT was tested in practice. Figure 4 shows the field hardware architecture and the MPPT control interface [3].  
Figures 5-7 show the experiment results of CV, CV-positive pulse and CV Reflex charging. We used FPGA hardware for data acquisition, and LABVIEW for data feedback, calculation and  
Figure 4: Solar tracking power supply system. (**a**) Onsite hardware architecture; (**b**) MPPT control interface.  
Figure 3: PSO flow chart.  
display. The data shown in the various figures were recorded once every 2 s, and 30 min of charging was drawn into a single curve instantly. The experiment results focused on the temperature in different charging modes. It can be seen in Figure 8 (the temperature curve comparison diagram) that the CV increases by 4 \({}^{\circ}\)C-5 \({}^{\circ}\)C and continuously in the case of low current. In terms of the CV-positive pulse, the temperature is slightly better after the charge mode has been switched, but it is still worse than it is in the CV-Reflex mode used in this study. The temperature remains steady when the reflex is switched to 800 s.  
Figure 5: CV charging. (**a**) Power end voltage, current and power curves; (**b**) battery end charging voltage, current and power curves; (**c**) SOC (%), actual voltage and temperature (\({}^{\circ}\)C) curves inside battery.  
Figure 6: CV-positive pulse charging. (**a**) Power end voltage, current and power curves; (**b**) battery end charging voltage, current and power curves; (**c**) SOC (%), actual voltage and temperature (\({}^{\circ}\)C) curves inside battery.
## 2 Single-Polarized 3D Absorber  
### Unit Cell Structure  
The unit cell structure of the proposed single-polarized 3D absorber is shown in Figure 1. It is composed of a notch-slot resonator, a coupled U-shaped microstrip line and a reflecting plate, whose period is \(L_{p}\times L_{p}\) along the \(x\)- and \(y\)-directions. The notch slot is printed on one side of the Rogers RO4350B substrate with a permittivity of 3.48 and a loss tangent of 0.001, while the coupled microstrip line is on the other side. The \(z\)-oriented parts of the coupled microstrip line are open-ended, and the \(y\)-oriented part is embedded with a chip resistor with a resistance of \(R\) in the center.  
The parameters in Figure 1 are as follows: \(L_{p}=11.0\) mm, \(t=0.8\) mm, \(L=18.0\) mm, \(L_{s}=15.0\) mm, \(W_{s}=5.5\) mm, \(L_{end}=1.0\) mm, \(W_{end}=2.5\) mm, \(W_{m}=1.0\) mm, \(L_{m1}=5.4\) mm, \(L_{m2}=9.0\) mm, \(h=6.0\) mm and \(R=200\)\(\Omega\).
# Feature Detection of Focused Plenoptic Camera Based on Central Projection Stereo Focal Stack
## 3 Feature Detection Based on CPSFS
### Calculation of Plenoptic Disc Data Based on Multi-Layer CPSFS  
In practice, there is usually a depth range for real scene. As detection methods based on two-layer CPSFS cannot effectively detect the features across the scene, we solve this problem by using CPSFS with multiple layers.  
Specifically, there is \(N\) layers of refocus planes with plenoptic disc radius \(R_{m}=R_{0}+m\Delta R\), \(m=1\), \(\ldots,N(N>2,\Delta R>0)\). For each two layers out of \(N\) layers of refocus planes, the plenoptic disc data of SIFT features are calculated using the algorithm in Section 3.2. In order to avoid repeated detection, the detected features with close plenoptic disc center and similar descriptors are regarded as the same. Therefore, we calculate the average plenoptic disc data and descriptors of all repeated features.  
The choice of parameters \(R_{0}\), \(N\), and \(\Delta R\) directly affects the detected results. Generally speaking, when \(\Delta R\) is about 1-2, the algorithm can get good results and speed. The choice of \(R_{0}\) and \(N\) should rely on the working distance of camera. Empirically speaking, \(R_{0}\) is usually about 3-5 and \(N\) is about 5-8. The detailed simulation is shown in Sections 4.1.2 and 4.1.3.
## 2 Related Work  
The application of the IoT to the SG is a relatively new field, nonetheless underpinned by the wide experience of the academic community in WSN and MANET [4; 16]. [PERSON] et al. [14] looked into different enabling technologies available for the SG, paying special attention to the benefits and drawbacks of each one, whereas [1] directly explored the architecture of the future IoT-based power grids as well as the key technologies that make this possible. Similarly, [27] analyzed the positive impact of the IoT on the energy domain, highlighting the sheer potential of such innovation in our daily lives. Finally, [28] elaborated on the positive impact that Fog Computing--a computing paradigm closely related to the IoT--may have on the Smart Grid arena. All in all, although these works study the enabling technologies in depth, they do not address the actual impact on the SG-oriented IoT of communication issues (such as the working frequency band).  
Regarding the study of wireless propagation, [PERSON] et al. [29] studied the effects on the percentage of packet losses of incorporating two radio interfaces in WSN nodes: one working at 2400 MHz and another in the 915 MHz band. Although very accurate and illustrative, the work was focused on WSNsoperating in forests, which does not share many similarities with the IoT for SGs. [PERSON] et al. [30] analyzed two Smart City critical infrastructures' propagation environments in the 400 MHz, 868 MHz and 2400 MHz bands. However, this study concentrates on elaborating empirical propagation models and does not evaluate/quantify their work with any figure of merit that may demonstrate the impact of the obtained models on actual IoT networks. Along the same lines, reference [31] proposed an accurate industrial propagation model capable of predicting low-level propagation characteristics with high accuracy in different frequency bands, whereas reference [32] provided near-ground path loss measurements for wireless deployments in indoor corridors for the different working frequencies. In general terms, the available literature--and these two studies in particular--focus on studying the low-level propagation phenomena applicable to different frequency bands, irrespective of the impact of such phenomena to the upper technologies (802.15.4, WiFi, Bluetooth, etc.) or the overall context (WSN, IoT, etc.).  
To the best of our knowledge, there is no scientific literature that jointly analyzes, quantifies and compares the effects of different working frequencies on an IoT deployment. Similarly, we have not found any study that proposes a dual-band propagation model for the Smart Grid.
# Development and Biomechanical Evaluation of a Back Exoskeleton to Support Lifting Tasks
## 2 Exoskeleton Functionality and Design
### Exoskeleton Design
#### 2.2.5 Smart Textile User Interface  
To enable the intuitive control of the exoskeleton and allow users to adjust key parameters, such as the supporting force, a wearable user interface was developed using smart textiles (Figure 3). The interface is designed as a wrist-worn device, ensuring easy accessibility while maintaining comfort and flexibility during use. The base structure consists of fabric materials, which allow the interface to be securely wrapped around the wearer's wrist.  
The embedded hardware within the textile structure includes an Arduino microcontroller equipped with a Bluetooth module, which enables wireless data transmission to the exoskeleton. The system is powered by a 4.7V LiPo battery to ensure a compact and portable power supply for prolonged operation.  
The user interface provides seamless control over the exoskeleton's functions. A power button allows the user to switch the system on and off, while the plus and minus buttons enable real-time adjustment of the supporting force. The current force level is visually represented by an RGB-LED bar, which offers instant feedback on the exoskeleton's assistance intensity. Additionally, a menu and reset button are integrated for further customization and system management.  
To enhance usability and durability, the interface buttons are made of conductive yarn, allowing for touch-sensitive interaction while maintaining a lightweight and flexible design. However, physical manual buttons are implemented for essential functions, such as powering the interface and activating a key lock function to prevent unintended inputs. The use of smart textiles, wireless communication, and intuitive controls is intended to ensure user-friendly and efficient interaction with the exoskeleton, enhancing both functionality and user experience.
## 3 Establishment of the Model
### Objective Function  
#### 3.3.1 Transportation Cost  
(a) In-transit transport cost is related to the freight volume, unit transport cost, and transport distance loaded by the means of transport:  
\[C_{1}=\sum_{i\in n}\sum_{j\in n}\sum_{k\in J}qd^{k}_{ij}c^{k}_{ij}x^{k}_{ij} \tag{1}\]  
\begin{table}
\begin{tabular}{l c} \hline \hline \(n\) & The set of transport nodes: A, B, C,... G \\ \hline \(h\) & The set of transport nodes: B, C,... F \\ \hline \(J\) & The set of transport modes: highway, railway \\ \hline \(i\) & The \(i\)th node \\ \hline \(j\) & The \(j\)th node generally refers to the next node of node \(i\) \\ \hline \(k\) & The \(k\)th mode of transport \\ \hline \(l\) & The \(l\)th mode of transport \\ \hline \(q\) & Freight volume (unit: t) \\ \hline \(S_{i}\) & The time when the goods arrive at the node \(i\) \\ \hline \(T_{\min}\) & The lower limit of time allowed for the goods from the beginning of transportation to the end of transport \\ \hline \(T_{\max}\) & The maximum amount of time allowed for the goods from the beginning of transportation to the end of the transport \\ \hline \(d^{k}_{ij}\) & The distance between node \(i\) and node \(j\) using the transport mode \(k\) \\ \hline \(c^{k}_{ij}\) & The unit transportation cost of the transport mode \(k\) between node \(i\) and node \(j\) \\ \hline \(c^{k}_{ij}\) & The transportation cost of the node \(i\) from transport mode \(k\) to transport mode \(l\) \\ \hline \(d^{k}_{ij}\) & The transportation time of the transport mode \(k\) between node \(i\) and node \(j\) \\ \hline \(c^{k}_{ij}\) & Unit transport time of node \(i\) converted from mode \(k\) to mode \(l\) \\ \hline \(c_{k}\) & Unit carbon emissions using the transport mode \(k\) \\ \hline \(E_{\max}\) & Maximum allowable carbon emissions during transportation \\ \hline \(x^{k}_{ij}\) & When the transport mode \(k\) is adopted between node \(i\) and node \(j\), the value is \(1\) and \(0\) otherwise \\ \hline \(y^{kl}_{i}\) & Node \(i\) takes a value of \(1\) when it is converted from transport mode \(k\) to transport mode \(l\); otherwise, it is \(0\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Symbol description of parameters and decision variables.  
(b) Regarding node conversion cost, the conversion cost will occur when a node changes the means of transport, which is related to the freight volume and the unit conversion cost between different modes of transport, as follows:  
\[C_{2}=\sum_{i\in h}\sum_{k\in J}\sum_{l\in J}q\varsigma_{i}^{kl}y_{i}^{kl} \tag{2}\]
# Algorithmic Design of an FPGA-Based Calculator for Fast Evaluation of Tsunami Wave Danger
## 2 Materials and Methods
### Pipeline (Stream) Algorithm  
The reference algorithm assumes that the original image is stored in random-access memory. However, in the case of hardware implementation, this is not always the case; more often the image is a stream, the pixels come in turns, the image is not stored anywhere in its entirety, and the processor has no way to obtain an arbitrary pixel. In this case, it is necessary to transform the algorithm by making it stream-oriented; this is the first step to implementation on an FPGA, and can be achieved as shown in Listing 3:  
```
void filter(std::stream & input, int width, int height,  Coeff kernel[KERNELSIZE][KERNELSIZE], std::ostream & output){  int ox = -KERNELSIZE/2, oy = -KERNSIZE/2;  int ix = 0, iy = 0;  Pixel stencil[KERNELSIZE][MAXWIDTH];  for(int i = 0; i < width*(height + KERNELSIZE/2) + KERNELSIZE/2;  if (iy < height)  stencil[iy % KERNELSIZE][ix] = readPixel (input);  if ((ox \(\geq\) 0) && (oy \(\geq\) 0) ){  Pixel result = 0.0;  for (int ky = 0; ky < KERNELSIZE; ky++) {  for (int kx = 0; kx < KERNELSIZE; kx++) {  int sx = ox + kx - KERNELSIZE/2;  int sy = oy + ky - KERNELSIZE/2;  sx = (sx < 0)? 0: ((sx \(\geq\) width)? (width-1): sx);  sy = ((sy < 0)? 0: ((sy \(\geq\) height)? (height-1): sy))  result += stencil[sy][ss] * kernel[ky][kx]; } WritePixel (output, result); }  bool ixend = (ix == width - 1);  ix = ixend? 0: ix + 1;  iy = ixend? (iy + 1): iy;  bool oxend = (ox == width - 1);  ox = oxend? 0: ox + 1;  oy = oxend? (oy + 1): oy; }
```  
**Listing 3** Stream-based convolutional filter.  
Here, the pixels come sequentially through the streams of the C++ language. The algorithm remembers the previous lines of the image in the _stencil_ array. As the pixels arrive, they are convolved with the pixels stored in the _stencil_ array. In total, the KERNELSIZE number of lines must be stored. Since the data required for the convolution of the first pixel will appear only when the KERNELSIZE line arrives, such a streaming algorithm has a delay.
# Sunspot Detection Using YOLOv5 in Spectroheliograph H-Alpha Images
## 4 Materials and Methods
### Performance Metrics  
The main performance metrics used for assessment of the model are precision (\(P\)) and recall (\(R\)). Precision is a measure of the predictive value, and recall is also known as a measure of the sensitive value. They are defined as follows:  
\[P=\frac{TP}{TP+FP} \tag{1}\]\[R=\frac{TP}{TP+FN} \tag{2}\]  
In the equations, \(TP\) means true positive, \(FP\) means false positive and \(FN\) means false negative. Another performance metric used is mean average precision (\(mAP\)), which is the average precision of the model when a given threshold is imposed for detection. A threshold of 0.5, for example, requires that the bounding box of a sunspot detected by the network overlaps at least 50% with the bounding box designed when marking the dataset.
# Research Progress of Visual Inspection Technology of Steel Products--A Review
## 4 Detection Methods
### Method Based on Machine Learning
#### Moving Center Hypersphere  
The moving center hypersphere (MCH) is a way to compress a reference sample. The basic idea of the MCH is to use a hypersphere to represent a cluster of points to approximate each sample with a number of hyperspheres. The center of the hypersphere is then moved, and its radius is expanded so that it should contain as many sample points as possible, and ultimately contain all of the sample points in the space. Two recent articles fully illustrate the novelty of this method. In 2017, a method with quantile hypersphere based on machine learning (QH-ML) was employed in Chu [83] for six kinds of defects on a steel surface. Soon after, in 2018, a defect classification model was established in Gong [84], which was a multi-hypersphere support vector machine (MHSVM) with additional information. It is not hard to see that this method has good generalization ability.