esa-sceva/satcom-chunk-collection huggingface dataset:
# Examining Pipe-Borehole Wall Contact and Pullback Loads for Horizontal Directional Drilling
## 2 Analysis of Pipe-Borehole Wall Contact
### Classical Contact Models
#### 2.1.1 Hertz Model  
Hertz theory is one of the most common methods for internal cylinder contact problems. In the Hertz model, the contact pressure distribution is assumed as follows [26]:  
\[p_{c}=q_{0}\bigg{(}1-\frac{x^{2}}{l^{2}}\bigg{)}^{n}, \tag{1}\]  
where \(p_{c}\) signifies the contact pressure, \(q_{0}\) denotes the maximum contact pressure without drilling fluid pressure, \(x\) represents the distance between the point on the interface and symmetry axis; \(n\) signifies the pressure distribution exponent, \(n=1/2\) in the Hertz model.  
The maximum contact pressure between the two cylinders is expressed as follows [26]:  
\[q_{0}=\frac{E^{*}}{2}\frac{\delta}{l}, \tag{2}\]  
where \(E^{*}\) represents the equivalent elastic modulus, which is defined as follows:  
\[\frac{1}{E^{*}}=\frac{\big{(}1-v_{1}^{2}\big{)}}{E_{1}}+\frac{\big{(}1-v_{2}^ {2}\big{)}}{E_{2}}, \tag{3}\]  
where \(E_{1}\) and \(E_{2}\) denote the elastic modulus of the inner and outer cylinders, respectively; \(v_{1}\) and \(v_{2}\) stand for the [PERSON]'s ratio of the inner and outer cylinders, respectively.  
It is noteworthy that Equation (2) cannot be directly used to calculate the maximum contact pressure because the compressive displacement \(\delta\) and the semi-chord length of contact \(l\) are unknown.  
The compressive displacement \(\delta\) can be calculated as follows [27]:  
\[P=\frac{\pi}{4}E^{*}\delta. \tag{4}\]  
The semi-chord length of contact \(l\) in the Hertz model can be determined by the geometric relationship between the semi-chord length of contact \(l\) and compressive displacement \(\delta\) as follows [27]:  
\[l\approx\sqrt{R\delta}, \tag{5}\]  
where \(R\) denotes the relative curvature of the contact and is defined as follows:  
\[R=\frac{R_{1}R_{2}}{\Delta R}. \tag{6}\]  
Substituting Equations (4) and (5) into Equation (2), the maximum contact pressure in the Hertz model can be obtained as follows:  
\[q_{0}=\bigg{(}\frac{PE^{*}}{\pi R}\bigg{)}^{1/2}. \tag{7}\]
# A Systematic Review of Ship Wake Detection Methods in Satellite Imagery
## 3 Results
### Overview of Ship Wake Characteristics
While utilizing multiple-satellite systems greatly increases the frequency of successive observations compared to a single satellite or constellation, it still does not ensure continuous observation throughout the day, unless sparse constellations are also employed. This is an inherent limitation that should be considered when developing a spaceborne monitoring system. Figure 2: (a) Gray-scale optical image of a ship wake. (b) Fourier transform of the same image
# A Novel Data-Aided Frame Synchronization Method Based on Hough Transform for Optical Communications
###### Abstract  
In optical communication systems, frame synchronization is essential for subsequent operations, such as error correction and payload extraction. Various methods, so far, have been proposed in the published literature, but the performance is unsatisfactory under high bit error rate (BER) conditions. We present, in this work, a novel data-aided frame synchronization technique for optical packet transmission systems, in which the transmitter sends a sequence of packets with a specific synchronization word periodically inserted, and the receiver blindly recognizes the synchronization word to attain frame synchronization. The proposed algorithm detects the synchronization word based on Hough transform (HT), a classic method for line detection in digital image processing. The core principle of the algorithm is to exploit the periodicity of the frame synchronization word, which appears as black-and-white spaced stripes on a binary image when the frames are all aligned. Simulations are conducted over a 56 Gbps optical QPSK transmission system, and the results show that our algorithm is still effective in attaining frame synchronization at a BER of 0.3. A comparison is also made between our algorithm and existed autocorrelation (AC)-based algorithm. The results demonstrate that our algorithm has a better error resilience performance. When the BER is higher than 0.03, our algorithm outperforms an AC-based algorithm significantly.  
Keywords:optical communication; frame transmission; synchronization word; blind recognition; Hough transform +
Footnote †: journal:
# Navigation Path Extraction and Experimental Research of Pusher Robot Based on Binocular Vision
## 2 Materials and Methods
### Image Segmentation  
#### 2.6.1 Gabor Filter  
Depending on the problem to be solved and the content of the images, segmentation could be done in a variety of ways. However, considering the complex background of the cowshed, a Gabor filter was used to extract the texture features of the cowshed environment. The Gabor filter is robust enough to image brightness and contrast changes as well as pose changes, and it expresses the most useful local features for image recognition. Therefore, Gabor filters are widely used in texture segmentation, edge detection and content detection [30]. From a biological point of view, the Gabor filter is very suitable for the expression and separation of texture, and its frequency and direction expression is similar to the human visual system. In previous studies, no one has attempted to use this technique in a cattle barn environment, especially in the study of pusher robot navigation.  
In the spatial domain, a two-dimensional Gabor filter is the product of a sine plane wave and a Radial Basis Function (Gaussian kernel). The former is a harmonics function and the latter is online analytical processing. The following is the complex number expression of the two-dimensional Gabor function.  
\[g(x,y;\lambda,\theta,\psi,\sigma,\gamma)\ =\ e^{-\frac{x^{\prime 2}+\lambda^{2}{y^{ \prime}}^{2}}{2\sigma^{2}}}\ e^{i(2\pi\frac{x^{\prime}}{\lambda}+\psi)} \tag{15}\]  
\[\left\{\begin{array}{l}x^{\prime}\ =\ x\cos\theta+y\sin\theta\\ y^{\prime}\ =\ -x\sin\theta+y\cos\theta\end{array}\right. \tag{16}\]  
where, \(x,y\) represent the original pixel coordinate position, and \(x^{\prime},y^{\prime}\) are the pixel coordinate positions after Gabor transformation; \(\lambda\) is the wavelength--the wavelength directly affects the filtering scale of the filter, and its value is specified in pixels; \(\theta\) is the direction of the filter--this parameter specifies the direction of the parallel stripes of the Gabor function, and its value was 0-360\({}^{\circ}\); \(\mathbf{Y}\) is the phase offset of the tuning function; \(\gamma\) is the spatial aspect ratio, which determines the shape of the filter; and \(\sigma\) is the bandwidth, which is the variance of the Radial Basis Function. The relationship between standard deviation and wavelength  
Figure 12: Transformation and smoothing of images. (**a**) Original RGB image; (**b**) global threshold segmentation de-binarization; (**c**) color image with sky removed.  
was \(\sigma\) = 0.56\(\lambda\). The smaller the bandwidth, the larger the standard deviation, the larger the Gabor shape, and the greater the number of visible parallel stripes.
## 3 Results and discussion
### Analysis of the Experiment Results in the Field
Therefore, the weeding effects of the driving weeding device with rake teeth developed in this study and the mechanical inter row weeding parts (including bending elastic tooth weeding wheel, swing weeding roller, spiral cutter tooth weeding wheel, compound weeding wheel and rotary swing weeding roller) widely used at present were compared and analyzed, as shown in Table 5. Compared with the common row weeding device, the driving weeding device with rake teeth has better weeding effect. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Type of Weeding Parts between Rows** & **Average Weeding Rate /\%** & **Literature Source** \\ \hline Driving weeding device with rake teeth & 87.06 & This study \\ Bending elastic tooth weeding wheel & 78.02 & [19] \\ Swing weeding roller & 78.1 & [21] \\ Spiral cutter tooth weeding wheel & 82 & [4] \\ Compound weeding wheel & 83.7 & [14] \\ Rotary swing weeding roller & 86 & [6] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of weeding effect of mechanical inter row weeding parts.
## 4 User-Centric Multiple Incentive Mechanism
### Participation Assessment Algorithm  
#### 4.2.1 The Behavior of Publishing Content  
In OSM, user-generated content is a critical element that attracts users. The richness of content within a OSM platform determines its appeal to users. Only when the quantity and quality of content are assured can more users be attracted and retained, thereby fostering the development of OSM. Publishing content is one of the primary ways for users to earn PV in the NexoNet. The more content a user publishes, the more PV they are rewarded with by the system.  
While users can earn PV by publishing content, measures must be in place to counteract malicious users who might publish a large volume of false or low-quality content in a short period to gain undue PV. To address this, the acquisition of PV must be constrained. The hyperbolic tangent function \(\tanh x=(e^{x}-e^{-x})/(e^{x}+e^{-x})\) exhibits the following characteristics: for \(x\geq 0\), \(x\) is monotonically increasing, and the function's upper bound is 1. By considering the interval between content publications \(\Delta T\) as the function's independent variable and the PV gained as the dependent variable, the PV obtained will significantly decrease as the interval between published content shortens, eventually approaching zero. Therefore, after publishing content, users must wait for a system-defined interval before they can publish again to restore their PV to its original level. The method for calculating the PV gain for publishing content, GCP (Generate Content Participation), is shown in (1).  
\[\left\{\begin{array}{l}\tau_{1}=\frac{\Delta T}{\omega_{1}},\\ \tanh\tau_{1}=\frac{e^{\tau_{1}-e^{-\tau_{1}}}}{e^{\tau_{1}+e^{-\tau_{1}}}},\\ GCP=\sum_{1}^{\tau_{1}}\alpha_{gcp}\times\tanh\tau_{1}\end{array}\right. \tag{1}\]  
\(n_{1}\) represents the number of contents published by a user. \(\alpha_{gcp}\) signifies the upper limit of PV that can be obtained from a single content publication. \(\Delta T=(T_{i}-T_{i-1})\) denotes the time interval between content publications by a user. \(\omega_{1}\) is a variable system parameter, which can be adjusted to set the interval time for publishing content.  
Figure 2 illustrates the relationship between the GCP a user can obtain from publishing content and the time interval \(\Delta T\) between their content publications. If we set \(\omega_{1}=1\) s, when the interval time \(\Delta T=0.5\) s for publishing content, the PV obtained by the user is only \(0.460\alpha_{gcp}\). When a user publishes multiple contents in a much shorter time interval, the PV obtained for each piece of content will significantly decrease. Meanwhile, when the time interval \(\Delta T=3\) s for publishing content, the PV for publishing content can approximately return to its maximum value \(\alpha_{gcp}\).
## 5 Conclusions  
We successfully designed, fabricated, and tested current leads used for the generation of magnetic field in superconducting magnet systems. The vapor-cooled and hybrid current leads were developed, and both current leads successfully generated a magnetic field of 5 T at the operating current of 45 A. Both leads were installed in 5 T superconducting magnet systems, and performance tests were conducted based on measurements of the boil-off rate of liquid helium when electrical current was supplied to the magnet. The boil-off rate of liquid helium decreased by approximately 24% when the hybrid current lead was applied as compared with the vapor-cooled current lead. It was confirmed that the minimization of the generated heat by the hybrid current lead achieves efficient reduction of the liquid helium boil-off rate. As the consumption of liquid helium decreases, the 5 T magnetic field of the superconducting magnet can be maintained for a longer time. The time to maintain the 5 T magnetic field increased, therefore, the constraints of properties measurement were diminished, and the hall coefficient measurement was possible at various angles and temperatures. The Electromagnetic Property Measurement System with a central magnetic field of 9 T and a wide room temperature bore above 2 inches or more will be developed under Korea Basic Science Institute. The design and manufacturing technique, and test results of the current lead obtained through this study will be applied to a 9 T Electromagnetic Property Measurement System.  
Figure 11: Liquid helium level variation with respect to the magnetic field as a function of current lead.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Vapor-Cooled Metal Current Lead (Copper RRR10)** & \multicolumn{2}{c}{**Hybrid Current Lead**} \\ \hline  & Design value & Test value & Design value & Test value \\ Heat leakage & 0.4322 W & 0.4599 W & 0.3396 W & 0.3486 W \\ Boil-off rate & 14.31 L/day & 15.21 L/day & 11.23 L/day & 11.53 L/day \\ Decreasing rate & – & – & 21.53\% & 24.19\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of experimental results as compare with the design value.  
**Author Contributions**: Design, J.Y.J. and Y.J.H.; test and data acquisition, M.S.K.; writing--original draft preparation, M.S.K.; writing--review and editing, Y.S.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Korea Basic Science Institute (KBSI) grant number D010200.  
**Conflicts of Interest**: The authors declare no conflict of interest.
## 3 Results and Discussions  
### SEM Imaging  
Bacterial cellulose obtained from SCOBY by acid hydrolysis presents a crystal shape, indicated by arrows in Figure 1a. The circled particle is a 3D rod-shape crystal of bacterial cellulose, a form previously reported by other research groups [34; 35]. The Image J software analysis of 100 bacterial cellulose crystals indicated dimensions within nano scale, ranging between 62 and 756 nm, with an average 235 \(\pm\) 188 nm Figure 1b. Similar reported values underline a dimension variation on the cellulose source and preparation method [34; 35].  
The evaluation of yeast film microstructures by SEM indicates, by top-view images, (Figure 2a) surfaces without pores or cracks but with protuberances that could be related to CNC presence. This fact correlates well with the surface roughness of these films compared to the control ones when touching the two types of films. In cross-section images (Figure 2b), yeast cells from film forming emulsion are easily recognizable, but nanocellulose particles also uniformly distributed onto each cell surface. The control films, without CNC, present a very uniform surface in top-view (Figure 2c) and in cross-section (Figure 2d) disrupted yeast cells due to sonication treatment, some with changed cellular volume. Similar modified morphology has been previously reported for sonicated yeast cells [36].  
Figure 1: Bacterial nanocellulose crystals (**a**) CNC magnified 30,000\(\times\); (**b**) CNC size distribution.
# A New Fault Diagnosis Method for Unbalanced Data Based on 1 DCNN and L2-SVM
## 2 CNN and the Imbalanced Classification Problem  
### Introduction to CNN  
CNN is a feedforward neural network with convolution calculation and depth structure. It is usually composed of a convolution layer, an activation layer, a pooling layer, and a classification layer.  
1. Convolution layer. It is mainly a feature extractor for the input data, with the structure of multiple inner convolution kernels. Each element of the kernel is assigned a weight coefficient and a deviation value. Each neuron in this layer is attached to other neurons in the neighborhood of the previous layer, which means the size of the connected area relies upon the dimension of the convolution nucleus. Because of the one-dimensional original vibration data inputted in this paper, a one-dimensional convolution operation is adopted. If the input signal is x, its length is N, and the weight vector of the convolution kernel is w\({}_{c}\), the expression of convolution and activation operations is: \[\mathrm{x}_{j}^{1}=\mathrm{f}(\sum_{i\in\mathrm{M}_{j}}\mathrm{x}_{i}^{1-1}*w_ {ij}^{1}+\mathrm{b}_{j}^{1})\] (1) Here, x\({}_{j}^{1}\) is the feature vector output by the j\({}^{th}\) convolution kernel of layer l, M\({}_{j}\) is the feature vector set input by layer l, x\({}_{i}^{1-1}\) is the feature vector output by the ith convolution kernel of layer l - 1, w\({}_{ij}^{1}\) is the convolution kernel weight vector converted from the ithfeature segment of layer \(1-1\) to the jth feature segment of layer \(1\), \(\mathrm{b}_{1}^{1}\) is the bias of the jth convolution kernel of layer \(1\), \(\mathrm{f}\) represents the activation function, with \(\ast\) as the convolution operation symbol. (2) Active layer. As in Equation (1), the convolution is a linear operation, and the result is a linear combination of various features. For better expression of the nonlinear relationship between features, some nonlinear factors are generally introduced through the activation function. At present, the most used activation function in CNN is ReLU; its mathematical definition is \[\mathrm{ReLU}=\begin{cases}\mathrm{x}&\mathrm{if\ x>0}\\ 0&\mathrm{if\ x\leq 0}\end{cases}\] (2)
3. Pooling layer. It is commonly inserted behind the convolutional layer, which can reduce the number of parameters and shorten the time of convolutional operations while effectively avoiding overfitting. The widely used pooling methods are maximum pooling and mean pooling. Since the performance of the former is better than the latter when dealing with one-dimensional time series, the maximum pooling method is used in this paper, and its calculation formula is \[\mathrm{P}_{\bar{\eta}}^{1}=\max_{(j-1)w+1\leq k\leq jw}\Bigl{\{
# Copper Alloys Performance in High-Pressure and Low-Velocity Conditions Using a Custom Tribometer
## 2 Materials and Methods
### Results Validation Approaches
#### 2.2.2 Comparative Study of the Results with Reference Equipment  
Knowing that tribological tests are destructive, they cannot be repeated under the repeatability conditions stated, the uncertainties Type A cannot be evaluated, and the reproducibility of tribological tests is far from trivial [27].  
Aware of this limitation, the results of the pin-on-disc test were compared between two different pieces of equipment. For this purpose, a Plint TE-66 pin-on-disc tribometer was chosen [24].  
The tests were carried out in triplicate using two copper alloys: a nickel aluminum bronze (NAB) obtained through the industrial process of continuous casting, hot extruded at 900 \({}^{\circ}\)C, light draw and stress relieved at 600 \({}^{\circ}\)C, and an industrially manufactured continuous cast high-lead bronze (HLB), with respective chemical compositions detailed in Table 1.  
Both sets of samples were prepared from \(\OUR 50\) mm bars in EDM to \(\OUR 6\times 30\) mm pins, and every contact face was carefully surfaced with 200, 400, and 600 grit sandpaper to ensure the best contact condition between the pin and disc.  
\(\OUR 36\times 5\) mm 1045 steel disc, with a hardness of \(246.17\pm 7.4\) HV, was ground on both sides to obtain a maximum parallelism deviation of 0.01 mm and Ra roughness of \(0.624\pm 0.11\) \(\upmu\)m.  
Pins and discs were ultrasonically cleaned in acetone for 20 min and dried for 10 min at 60 \({}^{\circ}\)C. Posteriorly, they were measured on a 0.0001 g precision analytical balance. The comparative test, whose parameters are in Table 2, was carried out in both equipment (custom and reference) in dry sliding conditions, with a load of 580.07 N, a rotation of 80 rpm, and a 13 mm track radius.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Designation** & **Al** & **Ni** & **Fe** & **Pb** & **Zn** & **Sn** & **Cu** \\ \hline NAB & 11 & 4 & 5 & - & - & - & Remainder \\ HLB & - & - & - & 15 & 8 & 4 & Remainder \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition of evaluated materials (%wt).  
After the test, the pins and discs were subjected to the same cleaning and drying procedure and weighed again to evaluate the specific wear rate (k), defined as the worn material volume (Q) per unit of sliding distance (L\({}_{\text{s}}\)) and per unit of load (W) of normal contact (Equation (7)) [1,5].  
\[k=\frac{Q}{L_{s}\cdot W} \tag{7}\]
## 3 Method
### Graph Construction
#### 3.2.2 Graph Representation  
As a critical part of the graph structure, edges determine the weights of node information propagation and aggregation. In other fields, some researchers regard semantic relationship or co-occurrence frequency of objects as edges [1; 26]. However, as a basic feature, there is still a gap between object semantics and sentiment, making it hard to accurately describe the sentimental relationship. Further, it is challenging to label abstract sentiments non-artificially due to the \"affective gap\" between low-level visual features and high-level sentiment. To solve this problem, we use the semantic relationship of objects in emotional space as the edges of the graph structure. Given the object category, we employ SentiWordNet as a sentiment annotation to label each category with sentimental information. SentiWordNet is a lexical resource for opinion mining that annotates the positive and negative values in the range [0,1] to words.  
As shown in Equations (1) and (2), we retrieve words related to the object category in SentiWordNet, and judge the sentimental strength of the current word \(W\) with the average value of related words \(W^{\prime}\), where \(W_{p}\) is the positive emotional strength, \(W_{n}\) is the negative emotion strength.  
\[W_{n}=\frac{\sum_{i=1}^{n}W_{in}^{\prime}}{n} \tag{1}\]  
\[W_{p}=\frac{\sum_{i=1}^{n}W_{ip}^{\prime}}{n} \tag{2}\]  
Figure 3: Example of building graph model. Given the input image (**a**), Detectron2 can detect the region and categories of objects and (**b**) is the segmentation result. Based on the detection information, we build a graph (**c**) over the corresponding image.  
In particular, we stipulate that sentimental polarity of a word is determined by positive and negative strength. As shown in Equation (3), sentiment value \(S\) is the difference between the two sentimental intensity of words. In this way, positive words have a positive sentiment value, and negative words are the opposite. And \(S\) is in \([-1,1]\) because of the intensity of sentiments is between 0-1 in SentiWordNet.  
\[S=W_{p}-W_{n} \tag{3}\]  
Based on this, we design the method described in Equation (4). We can use a sentimental tendency of objects to measure the sentimental distance \(L_{ij}\) between words \(W_{i}\) and \(W_{j}\). When two words have the same sentimental tendency, we define the difference between the two sentiment values \(S_{i}\) and \(S_{j}\) as the distance in the sentimental space. On the contrary, we specify that two words with opposite emotional tendencies are added by one to enhance the sentimental difference. Further, we build the graph over the sentimental values and the object information. In Figure 3c, we show the relationship among node \"person\" and adjacent nodes, and the length of the edge reflects the distance between nodes.  
\[L_{ij}=\left\{\begin{matrix}||S_{i}|-\big{|}S_{j}\big{|}\big{|}+1,&\quad if\ S_{i}*S_{j}>0\\ 0.5,&\quad if\ S_{i}=0,S_{j}=0\\ ||S_{i}|-\big{|}S_{j}\big{|},&\quad otherwise\end{matrix}\right. \tag{4}\]