esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, we introduce a model that can simultaneously perform semantic segmentation and edge detection. The proposed convolutional neural network SoUNet was used to output the crack segmentation images and crack edge images. Finally, the two output results were linearly fused to improve detection accuracy. When compared to previous semantic segmentation models, our method can increase the MioU value by up to 5.55 and increase the MPA value by up to 10.41.  
The semantic segmentation part of SoUNet is based on the U-Net structure of the vgg16. The convolution feature map on each scale was fused in pairs, and the low-resolution fusion feature map was further fused to a higher resolution after passing through the convolution layer. The edge detection part extracts the feature map of each scale based on the U-Net. The low-resolution feature maps were trained and fused to the high-resolution features, and the crack edge image was outputted. The edge detection part is also the side-output part of the entire network. In addition, the crack dataset contains the pavement surface of cement and asphalt, and it also contains images of water stains and shadows. Therefore, the dataset is closer to the actual situation. The experimental results demonstrate that the edge detection part of the proposed method achieves ODS-F 33.14, OIS-F 34.11 on our dataset. Its MioU, the semantic segmentation evaluation indicator, reaches a value of 69.32. Both the intuitive and numerical results are better than those of other segmentation methods based on deep learning. The experimental results also show that SoUNet performs well in rough asphalt pavement images, is less affected by water stains and shadows, and has the potential to deal with multi-interference pavement conditions.  
In the future, we plan to develop a new pavement detection network that is more accurate for identifying types of pavement cracks. We will enrich the pavement dataset and add crack images of various scenes to make the dataset closer to the actual situation. In addition, we will also use the model for other tasks that need both semantic segmentation and edge detection, such as pit contour detection in pavement distress detecting task, road edge line detection in an automatic driving task, and diseased organ contour recognition in picture medicine.  
Conceptualization, H.X.; methodology, P.L.; software, P.L.; validation, B.Z.; investigation, B.Z. and F.Y.; resources, F.Y.; writing--original draft preparation, P.L.; writing--review & editing, H.X.; visualization, P.L.; supervision, R.G.; project administration, H.X.; funding acquisition, R.G. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (11862008).  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Antenna Design
As a result, a new opposite-polarity Vivaldi antenna design was realized in this study. Table 2 presents the comparison of Vivaldi antenna studies and the antenna designed within the scope of this study. The proposed antenna stands out with its small size, high bandwidth, high gain, and directivity when compared to other Vivaldi antennas in the literature.
# Modular Spiral Heat Exchanger Thermal Modelling
## 1 Introduction  
The spiral type of heat exchanger is applied in many types of process industry due to a number of positive features of its design. Improved compactness of the exchanger arises from an enhanced heat transfer coefficient in curved channels. Applications in the chemical process industry are enabled by the single channel design of the SPHE and its resistance to fouling. The low thermal leakage to the ambient environment and the ability of SPHEs to absorb thermal dilatation are advantageous for high temperature applications.  
[PERSON] [1] provided a comprehensive set of correlation formulae for the heat transfer coefficient and the pressure drop for various SPHE configurations, which are potentially useful in the preliminary stages of heat exchanger design. Studies by [PERSON] [2] and [PERSON] et al. [3] focused on SPHE design utilizing available pressure heads for particular applications.  
[PERSON] et al. [4] made use of developed thermal correlations for the dedicated optimization of SPHE performance and reduction in costs.  
[PERSON] and [PERSON] [5; 6; 7] used Laplace transformation and Hermite polynomials to provide a solution for the SPHE energy balance equation and prediction of the effectiveness parameter. [PERSON], [PERSON] [8], [PERSON] et al. [9] and [PERSON] and [PERSON] [10] applied numerical integration and numerical solution tools on the resulting system of differential equations for SPHE performance prediction. Computer algebra systems were used in studies by [PERSON] et al. [11] and [PERSON] [12] to determine closed formulae, which could offer theoretical tools for the study of the heat transfer performance of SPHEs [PERSON] et al. [13] built a 3D numerical model and used a commercial CFD solver for the investigation of the wall heat transfer coefficient.  
The present study proposes a compact, serial thermal coupling of several SPHEs. This configuration is designed to improve the thermal performance of the resulting modular SPHE design. A numerical model for the modular SPHE was developed to predict the heat transfer effectiveness parameter.  
Although similar numerical models have been presented in other studies, to the author's knowledge, no application of the modular SPHE configuration has been presented to date.  
Moreover, there is a subtle difference in the mathematical formulation that is used in the present model compared to those provided in previous reports. The main aim of the study was to investigate the heat transfer effectiveness parameter as a function of the number of spiral turns and the stream temperature profiles in a two-module SPHE.  
The modular design proposed is suitable for constrained space applications and production processes in which there are restrictions, e.g., the injection of high conductivity plastic materials.
# A Carrier-Based Gardner Timing Synchronization Algorithm for BPSK Signal in Maritime Communication
## 1 Introduction
Other research directions on bit synchronization and the Gardner algorithm include frequency-domain bit-synchronization algorithms [26; 27], parallel processing on field programmable gate array (FPGA) [28], the effect of loop delay [29] on system performance, and performance comparisons with various algorithms [30; 31]. The existing improved methods of the Gardner algorithm all detect timing errors based on the baseband signal, which is vulnerable to residual-frequency offset. In addition, the system needs to perform additional frequency-offset estimation and compensation. Based on the characteristics of the carrier signal, this paper proposed a Carrier-Based Gardner (CB-Gardner) algorithm for timing synchronization. The algorithm can directly perform error detection on the carrier signal in the passband, eliminate the carrier frequency offset caused by Doppler effect and local crystal frequency bias, and perform timing synchronous sampling. The main improvements of the algorithm include: (1) changing the error-detection point and the error-calculation formula; (2) simplifying the operation of the numerically controlled oscillator (NCO); and (3) resampling all sampling points on the passband using an interpolator. This algorithm can be applied to single-carrier signals with binary phase-shift keying (BPSK) modulation, especially in wireless communication systems.
## 2 Structural Principle, Detection Mechanism, and Performance Indicators
### Refractive Index Sensing Mechanism  
#### 2.2.1 Microring Sensing Detection Mechanism  
Optical waveguide biochemical sensors built with MRRs as basic components typically have two detection mechanisms to convert biochemical signals into optical signals. One is achieved by detecting the change in the resonant wavelength of the MRR, whereas the other is achieved by detecting the change in the output light intensity at a fixed single wavelength.  
From Equation (2), it can be seen that a change in the effective refractive index causes a change in the resonant wavelength of the MRR. Furthermore, the effective refractive index of the transmission mode in the MRR is affected by the changes in the surrounding environment. During the sensing operation, the variation in the external environment can be measured based on the drift of the resonance wavelength and changes in the light intensity. Commonly used detection methods using MRRs for sensing are those based on the resonance wavelength shift and intensity variation.  
The resonance wavelength drift method measures the measurement based on resonance wavelength drift \(\Delta\lambda\). This sensing method has a wide measurement range. Figures 3 and 4 show the sensing implementation flowchart and detection principle of the resonance wavelength drift method, respectively. Moreover, the method based on the light intensity change measures the intensity change \(\Delta I\) at the resonance wavelength and detects the light intensity change in the output spectrum of the MRR. Figure 4 displays a schematic diagram of the intensity detection method, which can be represented by the resonance wavelength variation of the extinction ratio.
## 4 Materials and Methods
### Co-Expression Gene Networks  
To highlight possible co-expression of genes during fruit development and maturation, and also possible correlations between AsA and DHA concentrations and genes, two separate correlation networks were built for the fruit flavedo and pulp: (i) a gene co-regulation network representing only correlations between genes; and (ii) a network showing only correlations between genes and metabolites (AsA and DHA). For the construction of the networks, a Pearson's correlation analysis was first carried out for each fruit tissue, taking into account all the stages of fruit development and maturation. The input data for the calculation of correlation coefficients (\(r^{2}\)) was the fold-change relative to the values obtained in the flavedo or pulp, respectively, at the immature green stage (June) for each gene (relative expression levels) or metabolite (AsA and DHA concentrations). The \(p\)-values for the correlation of each gene-gene or metabolite-gene pair are also calculated. Correlation analyses were carried out in RStudio (version 1.3.1093, RStudio Team, PBC, Boston, MA, USA) using the function \"cormat\". Networks were built considering only significant correlations (cutoff criteria: \(p\)-value \(\leq\) 0.05), and assembled manually using the software Cytoscape (version 3.8.2, National Institute of General Medical Sciences, Bethesda, MD, USA). Genes and metabolites correspond to the network nodes, which are linked by lines (edges) if a significant correlation exists between two genes or between a gene and metabolite. Positive correlations are shown in red, while negative correlations are in blue, and the color intensity represents the strength of the correlation (absolute value of Pearson's correlation coefficient). Networks were not constructed for petals and leaves data, as not enough dates were registered to construct a reliable correlation network.
# Optimization of Xylose Recovery in Oil Palm Empty Fruit Bunches for Xylitol Production
## 3 Results
### The Surface Plot of Xylose Recovery vs. Pretreatment Time and SL  
The effects of pretreatment time and SL at a constant temperature of \(165\pm 5\)\({}^{\circ}\)C on xylose recovery are shown in Figure 3. The surface plots for the effect of pretreatment time and SL at \(130\pm 5\)\({}^{\circ}\)C and \(200\pm 5\)\({}^{\circ}\)C are not shown because they were similar. The pretreatment time here means the time which proceeded while heating the mixture at a certain temperature. It should be noted that the reactor configuration used in this study did not allow it to be cooled immediately after heating, and it took about 22 h to cool down.  
Figure 3a,b show an opposite trend of xylose recovery; furthermore, there was a curvature of pretreatment time effect on xylose recovery in solid and liquid pretreated OPEFB hydrolysate. This curvature supposedly gave an optimum value of SL and pre-time. Using SL of 15% gave the highest xylose recovery in solid pretreated OPEFB hydrolysate. Furthermore, a combination of the longest pretreatment time of 60 min and SL of 15% gave the highest xylose recovery in solid pretreated OPEFB hydrolysate. On the contrary, xylose recovery in liquid pretreated OPEFB hydrolysate was lowered with the increase in SL and pretreatment time. The highest xylose recovery in liquid pretreated OPEFB hydrolysate was obtained at SL of 5% and a pretreatment time of 15 min. This contrast in pretreatment time and SL combination between solid and liquid pretreated OPEFB hydrolysate showed that optimization should be performed for each biomass matrix. The minimum peak of xylose recovery in liquid pretreated OPEFB hydrolysate shown in Figure 3b was obtained at the pretreatment time of 37.5 min and SL of 10%.
## 3 Results  
We investigate the performance of our model compared to conventional communication systems for speech signals. The systems are tested under AWGN, Rician, and Rayleigh fading channels, with the assumption of perfect channel state information (CSI) at the receiver. These channel conditions are selected to represent a range of real-world wireless transmission scenarios, from ideal Gaussian noise environments to multipath-dominated fading conditions. The experiments utilize the Edinburgh DataShare dataset [25], which provides diverse speech recordings suitable for robust performance evaluation.
## 1 Introduction
### Reliability Theory  
Reliability as it is used in the reliability theory is the probability of an event occurring or the probability of a positive outcome. Evaluating reliability affords a means of assessing the degree of uncertainty involved in geotechnical engineering calculations [2]. The complexity of reliability analysis, and consequently the reduced scope of its application, arises from the mathematical definition of reliability. It is defined as the complement of the probability of failure, which can be written as follows [10]:  
\[\text{P}_{\text{S}}=1-\text{P}_{\text{f}} \tag{2}\]  
where:  
P\({}_{\text{s}}\)--is reliability  
P\({}_{\text{f}}\)--is the probability of failure  
The probability of failure as a probabilistic event is mathematically defined by the following equation:  
\[\text{P}_{\text{f}}=\text{Prob}\{\text{g}(\text{X})\leq 0\}=\text{Prob}\{ \text{M}\leq 0\}, \tag{3}\]  
where:  
X--is a random vector  
g(X)--is the performance function  
M--is the safety margin  
Equation (3) could be rewritten using the reliability integral as follows:  
\[\text{P}_{\text{f}}=\int_{\text{g}(\text{X})\leq 0}\text{f}_{\text{X}}(\text{X}) \text{d}\text{X}, \tag{4}\]  
where:  
f\({}_{\text{X}}\)(x)--joint probability density function of X  
Considering the aforementioned, from a mathematical point of view it can be concluded that the basic task of reliability analysis is to calculate a multidimensional integral with joint PDF of random variables as integrand, and the integration domain defined by the performance function. The reliability problem containing the joint PDF (f\({}_{\text{X}}\)(x)) of 2 random variables (X1 and X2), and performance function g(X1,X2) is visualized in Figure 1.  
The performance function cuts the joint PDF into two parts. The total volume under the surface equals 1. The smaller volume represents the probability of failure (P\({}_{\text{f}}\)), and the larger, its complement, the reliability (P\({}_{\text{s}}\)). The complexity of solving Equation (4) dependson the integrand, the performance function, and the number of variables. Depending on the degree of complexity and the availability of input data, a reliability integral can be calculated by direct integration of a multidimensional integral, or by using analytical or simulation methods [11].
## 3 Methods and Techniques
### Proposed Method for Lung Nodule Localization
#### 3.1.6 CNN Architecture  
Convolutional neural networks are artificial neural networks made up of interconnected nodes meant to simulate the function of neurons in the human brain. In the network diagram, the artificial neurons are portrayed as nodes, and the connections between their input and output are illustrated as arrows. This network has many layers, including convolutional layers, top pooling layers, and fully connected layers. These layers are responsible for a wide array of activities that are connected to the process of learning. CNN's next iteration will have two distinct areas that are entirely different. Its architecture uses maximum pooling, rectified linear units, and multiple layers of volumetric convolution. The second CNN module that is part of the network is referred to by its internal name, the classifier. In hierarchical neural networks, the second module is made up of layers that are entirely connected, in addition to layers that feature different non-linear processes. With the assistance of these two connected neural network modules, it is possible to achieve high-level thinking. Figure 7 shows the actual setup of a CNN operating in a single vector with only one dimension in length produced by combining all of the values present in the layer that came before the highest pooling layer. This results in the vector having only one dimension. It provides a visual depiction of the differences between convolutional neural networks and fully connected neural networks in terms of the connections between their nodes, which is one of the defining properties of each.  
The proposed CNN was employed for image detection and classification. To identify or classify a picture, we ran the trained CNN on test images, which were matrices of pixels in two dimensions. The CNN used a convolution operation to identify and correctly classify a cancerous instance. Similarly to neural networks, CNN draws inspiration from the human brain. The model we used was a modified version of the one initially proposed by [PERSON] and [PERSON] for object recognition. The mathematical procedure known as \"convolution\" uses an input (or \"1\") and an argument (or \"kernel\") to produce an output that illustrates how another changes the shape of one item. Let us use an image to illustrate this. The result of performing a mathematical operation on an image \"\(x\)\", which is a 2D array of pixels with distinct color channels (red, green, and blue--RGB), and a feature detector or kernel \"\(w\)\", is referred to as a feature map. An image \"\(z\)\" results from an image \"\(y\)\", a 3D array of  
Figure 7: The architecture of a CNN.  
pixels with various color channels. The mathematical representation of the classification equation is Equation (1), given below.  
\[s[t]=(x*y)[t]=\sum_{a=-a}^{a=a}x[a]w[a+t] \tag{1}\]  
where:  
* \(S[t]\) is the feature map,
* \(x\) is the input, and \(w\) is the kernel.
## 2 Traditional Test Automation
### Example of a Test Script  
Figure 1 shows a segment of a test for a web application, using _SpiderFixture_. This is a part of _FitLibrary_[4], an open source testing tool that can be used with _FitNesse_[5].  
_FitNesse_ is a wiki-based system for managing tests. A test is represented as a wiki page containing tables and other text, which can be edited using wiki markup. Suites of tests are based on a hierarchy of wiki pages. Wiki markup is used to define tables, include mechanisms, and configuration information about test execution. _FitNesse_ manages test or suite execution by running a separate test framework, passing test information to it, and presenting reports back to the user. Three test frameworks supported are _Fit_, _FitLibrary_ and _Slim_, all of which are based on tables [5; 6; 7].  
_FitLibrary_ is an interpreter for table-based domain-specific languages (DSLs [8]), specialised for test automation like its predecessor _Fit_[6; 7]. Tables conveniently provide a simple syntactic structure. Test feedback is added directly to the tables, so it is easier to see what is going on than if a separate report  
Figure 1: The middle segment of a longer test.  
were provided. The contents of tables are dynamically mapped by _FitLibrary_ into method calls into the underlying code, such as _SpiderFixture_. The action keywords (cells shown in grey in Figure 1) are mapped into the name of a method, and the data cells (shown in white) are the parameters.  
_SpiderFixture_ includes methods for operating on web pages, available to test writers as table actions. For example the action in the first table in Figure 1 corresponds to the _SpiderFixture_ method _click()_ with one parameter (an element locator), while the fourth table corresponds to the method _withAddText()_, with two arguments (an element locator for a text field and the text to add, a credit card number). Elements on the page, such as buttons, selects, and text fields, are located in various ways, including through IDs and Xpaths (such as \"//input(@id='creditCardCardNo')\").  
_SpiderFixture_ is implemented with _Selenium 2_[9], which supports test automation through a range of browsers and _HtmlUnit_[10], a \"headless\" test system. It is convenient to see the changes happening in the browser as a test executes.
###### Abstract  
Protontherapy (PT) is a fast-growing cancer therapy modality thanks to much-improved normal tissue sparing granted by the charged particles' inverted dose-depth profile. Protons, however, exhibit a low biological effectiveness at clinically relevant energies. To enhance PT efficacy and counteract cancer radioresistance, Proton-Boron Capture Therapy (PBCT) was recently proposed. PBCT exploits the highly DNA-damaging \(\alpha\)-particles generated by the p + \({}^{11}\)B\(\rightarrow\)3\(\alpha\) (pB) nuclear reaction, whose cross-section peaks for proton energies of 675 keV. Although a significant enhancement of proton biological effectiveness by PBCT has been demonstrated for high-energy proton beams, validation of the PBCT rationale using monochromatic proton beams having energy close to the reaction cross-section maximum is still lacking. To this end, we implemented a novel setup for radiobiology experiments at a 3-MV tandem accelerator; using a scattering chamber equipped with an Au foil scatterer for beam diffusion on the biological sample, uniformity in energy and fluence with uncertainties of 2% and 5%, respectively, was achieved. Human cancer cells were irradiated at this beamline for the first time with 685-keV protons. The measured enhancement in cancer cell killing due to the \({}^{11}\)B carrier BSH was the highest among those thus far observed, thereby corroborating the mechanistic bases of PBCT.  
protontherapy; Proton-Boron Capture Therapy (PBCT); Proton-Boron Fusion-Enhanced Protontherapy (PBFEPT); BSH; low-energy protons; alpha particles; clonogenic survival; tandem accelerator