esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Velocity Distribution  
In this section, first the velocity distribution in this new offset configuration and its influence on heat transfer are shown in Figure 7. The information presented in this section will be used to explain why a high thermal and low pressure performance are achieved with this new design in next sections.  
In offset strip fin configurations, the flow is facing obstacles and its direction is changing periodically. Therefore, large vortices and recirculation areas are created around the corners at the end of strip fins, as it can be seen in Figure 7a. These recirculation areas are associated with a better and easier mix of the hotter water adjacent to fins and the colder water flowing in the middle of the channels, and thereby improving the heat dissipation. Also, the thermal boundary layer development is disrupted periodically as a result of channel walls being broken up because of strip fin design.  
In Figure 7b, creation of recirculation areas along the curvy fin is shown. For curvy configuration, dean vortices develop along the flow direction and can create recirculation areas and disturb the boundary layer.  
In Figure 7c, the creation of recirculation areas in a U path is shown. The Creation of a U-turn is associated with providing the chances of heat transfer between the inlet and outlet flow, and on the other hand, increasing the length of fluid flow passage inside the heatsink.  
Moreover, U-turns cause centrifugal forces that can contribute to the creation of secondary flows and recirculation areas. These leads to strengthen the mixing of flow adjacent to walls and in the middle  
Figure 7: Recirculation areas at volumetric flow rate of 18 cm\({}^{3}\)s\({}^{-1}\) (**a**) After an offset fin, (**b**) In a curve and (**c**) In a U-turn (red lines show stream lines).  
of channels, which cause making convective heat transfer stronger. As it can be seen, streamlines in the U-turn are denser than those in straight paths, showing that the velocity is higher in this area.
## 3 Main Findings
### Ingredients and Their Effects on Gluten-Free Dough Rheology and Bread Quality  
The majority of gluten-free products described in the literature are made from native and modified starches blended with different hydrocolloids or other improvers, with the aim of improving dough rheological properties, bread appearance and volume, and crumb structure [13]. Recently, a significant increase in demand for better-quality products has increased research into their development [14; 15]. The most noteworthy approaches are based on: the use of gluten-free flours (pseudo-cereals, maize, rice, etc.), and naturally gluten-free sources of starch (potato, maize, rice, etc.); the addition of protein from other production chains (egg, dairy ingredients, soya, etc.); the use of alternative sources of protein to strengthen doughs (insects and legumes); and the addition of gums, hydrocolloids, emulsifiers, and enzymes as improvers. The following sections examine these approaches in depth.  
Various methods and instruments have been used to assess the rheological properties of gluten-free doughs. The most widely used are the Chopin alveograph, the farinograph, the mixograph, the Visco Analyzer, and different types of rheometer. Although initially developed for wheat flour doughs, they are also effective in characterizing gluten-free dough rheology. Similarly, a wide variety of methods have been used to characterize gluten-free breads. Some are based on manual, physical measurements (e.g., the standard millet displacement method for volume, or loaf height measured with a caliper), others use instruments such as a texturometer (for crumb texture characterization), or sensorial analyses can be run with consumers. The most widely investigated parameters are specific volume, crumb and crust moisture, crumb and crust texture, loaf and slice color, and water activity.
# Machine Learning-Based Methods for Code Smell Detection: A Survey
## 3 Procedures of Code Smell Detection Approaches
### Research Questions  
After studying many research articles, the questions have been listed in a simplified manner. Table 2 shows the list of research questions (RQ); some questions have sub-questions. RQ-1, RQ-2, and RQ-5 incorporated complete research questions, and RQ-3 and RQ-4 incorporated sub-questions. RQ-1 studies various sources to evaluate the performance of different ML algorithms. This question explores different ML algorithms to detect smells and determine which methods perform better. RQ-2 discovers popular code smells with the ML algorithms. This question aims to collect information about code smells in studies  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**RQ-No.** & **Research Questions** & **Motivation** \\ \hline \multirow{2}{*}{RQ-1} & Which ML algorithms are used to detect & Study various sources to evaluate the performance of different ML \\  & code smell? & algorithms [11; 12; 13; 14; 15; 16]. \\ \hline \multirow{2}{*}{RQ-2} & Which code smells are measured? & Detection of popular code smells with ML algorithms [11; 12; 13; 14; 15; 16]. \\ \hline \multirow{2}{*}{RQ-3} & What software metrics are used for code & Identification of metrics frequently used to detect code smells [13; 16]. \\  & smell detection? & Identify the most critical metrics for detecting code smells [13; 16]. \\ \hline \multirow{2}{*}{RQ-4} & What are the various datasets used for & Find out which datasets are used to detect code smells [12; 14; 16]. \\  & code smell detection? & List the different dataset languages that are frequently used [12; 14]. \\ \hline \multirow{2}{*}{RQ-5} & What are the performance measurements & Find different evaluation techniques to measure the results of various \\  & of the different ML algorithms used? & ML algorithms [11; 13; 14; 16]. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Research questions.  
nowadays. RQ-3 identifies critical metrics used for the detection of code smells. RQ-3.1 identifies frequently used software metrics. RQ-4 aims to know the different datasets used for identifying code smells. RQ-4.1 collects information on datasets and the used dataset languages. RQ-5 discusses different evaluation techniques to evaluate the results of various ML algorithms.  
In Appendix A, the most relevant 42 research articles are tabulated. These articles are not just a collection of information, but they are the foundation of our study, providing practical insights and guiding our research. Each article has been assigned a unique attribute research article number (ARTN) for this SLR, ensuring their uniqueness, efficiency, and accessibility. Tables A2-A5 include the ARTN, year, tool/technique, smells, algorithms, dataset, performance metrics, and software metrics attributes, all crucial for our research. Tables A2-A5 provide detailed insights into each aspect of the reviewed research articles, facilitating easier navigation and interpretation of the data, thereby enhancing the overall understanding of the research.
# Eco-Friendly Metadome-Antenna Innovations for Wearable Millimeter Wave Radar Sensing
## 2 Materials and Methods
### Eco-Friendly Materials Used for the Antenna and the Metasurface  
Both dielectric and conductor materials used for the sensor (antenna with metaradome) are eco-friendly. It should be noted that, in general, the focus so far has been mainly on reducing the environmental impact of dielectrics, while for metals, the focus has been more conformist, with the sole focus on compliance with RoHS (Reduction of Hazardous Substances) and not so much on whether they are also environmentally friendly or, moreover, whether they truly contribute to the circular economy.
## 2 Data and Methods
### Technical Method Process  
The technical process is shown in Figure 3. Initially, supervised classification utilizing image-based object features is used for the preliminary extraction of landslide areas. The classification result is subsequently refined and analyzed by overlaying slope information, which leads to the identification and selection of the Jiangdingya landslide area. On the basis of this foundation, the application of mathematical morphology processing is implemented. The transformation of the image into a binary format is achieved by employing filtering, denoising, and thresholding methods. Subsequently, morphological operations, such as erosion and dilation, are applied to the binary image to derive the landslide area. Boundary information is extracted and overlaid with DOM data to facilitate recognition and analysis. This study experimented on multiple images using ArcGIS and MATLAB. See Table 1 for specific parameters.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Parameter** \\ \hline System & Windows 10 \\ Cpu & 15-6300 HQ \\ MATLAB & 2021b \\ ArcGIS & 10.8.1 \\ DEM & 5 m resolution \\ DOM & 1 m resolution \\ Processing Time & A few minutes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specific parameters.  
Figure 2: DOM and DEM of Nanyu Town, Zhouqu County.
## 3 Experiments and Results  
### Calibration of Characteristic Parameters  
Before measuring the distance, calibrating the characteristic parameters \(a_{2}\) and \(b_{2}\) of the ranging system is essential. The principle of the calibration experiment is illustrated in Figure 3. A fiber laser with a wavelength of 1550 nm served as the emitting light source. The balanced detector and collimator were the Thorlabs PDB780 CAC and F810 APC-1550 models, respectively. The LPF was the Mini-Circuits SLP-250+. The diameter of the retroreflector was 10 mm, and its maximum distance was approximately 3.6 m from the collimator. The motorized guide rail was a Newport IMS series high-performance displacement platform, which offered a travel range of 1 m with an accuracy of \(\pm 9\)\(\upmu\)m. The AWG was a Keysight M8196A. The DSO was a Teledyne LeCroy LabMaster 10-36 Zi-A, which had a sampling period of 0.4 ns, corresponding to a distance resolution of 60 mm. The other main parameters are listed in Table 1.  
Each step distance of the retroreflector was 2 mm, corresponding to a time delay interval of \(T_{1}=13.33\) ps, which was equivalent to re-interpolating a sampling period into 30 data points. Each distance value \((N_{3}-1)L_{1}\) and its corresponding value \(Z\) are plotted as the black dots shown in Figure 5. Using least squares fitting, the characteristic values \(a_{2}=-3.9189\times 10^{-3}\) and \(b_{2}=9.5414\times 10^{-2}\) were obtained, where the R\({}^{2}\) (coefficient of determination) value \(R^{2}=0.99999\) and the sum of the squared residuals \(RSS=1.4060\times 10^{-6}\) indicated a highly accurate fit, confirming that the model was credible. The characteristic parameters of the theoretical phase discrimination model used in the traditional method were \(a_{1}=3.3355\times 10^{-3}\) and \(b_{1}=0\), which differed from the characteristic calibration parameters. The necessity of the calibration process can be fully explained by a comparison to the subsequent distance measurement results.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Specification** \\ \hline Bandwidth of the low-pass filter & 250 MHz \\ PN code rate & 250 Mbps \\ PN code length & 1560 bits \\ \(N_{3}\) & 30 \\ \(N_{4}\) & 158 \\ Phase modulation depth coefficient & 0.5 \\ Frequency shifts of light by AOM & 400 MHz \\ Sampling rate of DSO & 2.5 GS/s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters.
## 3 Cognitive Mobile BTS
### Cognitive BTS Additional Services: Real-Time Terminal Detection  
As an added value, the interference avoidance mechanism also allows the detection of MSs, since they must emit messages during channel reselection. Let us define \(T_{D}\) as the _detection time_, which should be low for the detection feature to work properly in case of emergency. \(T_{D}\) depends on the state of the MS. In idle mode, it will depend on downlink channel signaling failure detection time plus the time it takes the MS to find the new carrier, as we discussed previously (see also [21]). According to our previous work [21] this time is less than 7.696 seconds, which is acceptable.  
As previously explained, the channel assignment procedure is used when the cell phone is in dedicated mode. If so, once the MS has received the _Assignment Command_, it starts the channel change immediately, so \(T_{D}\) will be much lower than in idle mode. In this case, \(T_{D}\) corresponds to the time for the cognitive BTS to change the operating channel (which is hardware -and software- dependent) plus the time for the MS to process the message and to send the _Assignment Complete_ response. The _starting time_ parameter delivered with the _Assignment Command_ can be used for the MSs to wait some time before launching the channel change process. This extra time allows the hardware to change the carrier frequency effectively. Since there are no disruptions in active conversations during channel change, \(T_{D}\) is less than 50 ms (_i.e._, the threshold for a user to perceive disruption in voice communications).  
Therefore, when detecting a single MS, \(T_{D}\) is satisfactorily low in all cases. In Section 4.5 we also study the case with multiple user terminals.
# A Systematic Review of Virtual Reality Interfaces for Controlling and Interacting with Robots
## 4 Takeaways  
VR interfaces for operating robots have come a long way in the past four years. VR devices becoming commercially available and affordable for many researchers has helped tremendously in furthering the state-of-the-art. There is also a growing VR community to help support development, which lowers the barrier of entry for new developers. This is particularly relevant in the area of system architecture and infrastructure for VR robot interfaces, as there are now several open-source solutions in connecting ROS and Unity making it much easier to get started with VR robot interface development. Additionally, there have been several works that show the promise of VR interfaces over traditional ones, such as 2D computer interfaces. Overall, it has been shown that VR interfaces reduce task completion time, increase operator performance, and are generally preferred over traditional interfaces. These works help support the need for continue development in VR interfaces for robot operation.  
Although, there has been a significant amount of foundational work in VR robot interfaces, there are still several areas that require further development. For example, there has been a significant amount of work in creating VR teleoperation interfaces, even though, teleoperation may not always be a viable option and a shared-control interface may be more appropriate solution, especially for complex systems. However, there has been limited research in shared-control VR interfaces despite the fact that these interfaces bring the advantage of allowing the robot to act semi-autonomously, which in turn allows the user to only provide input when needed and focus on the critical elements of a task. In addition, most VR interfaces so far have been designed for robot manipulators, aerial robots, or mobile robots. Figure 4 displays a heat map of the reviewed papers in each category and the types of robots used. At the moment, there is very limited work in using bipedal humanoid robots. Humanoids are general purpose platforms though and can easily be used in diverse environments designed for humans. However, they are complex dynamic systems and can immensely benefit from the immersive and 3D interaction environment that VR devices provide.  
Furthermore, as VR interface development continues, it is important to continually evaluate the usability and likability of these new advancements among users. It is also important to evaluate these interfaces in real-world applications with actual potential users of the systems.
# Study of the Diffusion of Rejuvenators and Its Effect on Aged Bitumen Binder
## 4 Conclusions  
Molecular dynamic simulation was established in this research to simulate the actual movement of rejuvenors in aged bitumen and to characterize their diffusion coefficient. Then, DSR tests were carried out to further investigate the rejuvenating effect. Based on the discussed results, the following can be concluded:  
1. Molecular dynamic simulation and rheological analysis results indicate that the severity level of aging has a significant influence on the diffusion coefficient and rejuvenating effect of rejuvenators. The investigated rejuvenators diffuse faster and more easily in the long-term aged binder, such as UV aged binder, but diffuse slower in the short-term aged bitumen binder. Consequently, there is a sufficient rejuvenating effect on the long-term aged bitumen and a limited effect on the short-term aged bitumen.
2. Both R-1 and R-2 can reduce the complex modulus and increase the phase angle of the long-term aged bitumen, but R-1 has a greater recovery capacity than R-2. The rejuvenators presented greater rejuvenate ability at lower temperatures than at higher temperatures.
3. Molecular simulation indicates that R-1 has a higher diffusion coefficient in aged bitumen binder than does R-2, while DSR proves that R-1 has greater recovery capacity. Molecular simulation and experiment results from the DSR test match quite well. This means that the molecular simulation proposed in this research can be successfully used to characterize the diffusion coefficient of rejuvenators in aged bitumen.  
This study was supported by the National Natural Science Foundation of China (No. 51408447) and the National Key Scientific Apparatus Development Program from the Ministry of Science and Technology of China (No. 2013 YQ160501). [PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON], [PERSON], and [PERSON] performed the experiments; [PERSON], [PERSON], [PERSON], and [PERSON] analyzed the data; [PERSON] and [PERSON] contributed analysis tools; [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.  
Figure 8: Effect of rejuvenators on UV aged bitumen at lower temperatures.
## 1 Introduction  
Presently, smart cities are turning into innovation hubs by leveraging cutting-edge technology. New services can be supported in these cities because of the existence of connected infrastructure and layers of intelligence that enable adapting, learning, and responding to inhabitants' needs [1, 2, 3, 4, 5, 6]. However, enhancing road safety is a critical concern for developing intelligent cities where vehicles are distributed in large street networks [5, 6]. Fortunately, these vehicles can share safety information continuously. In smart cities, smart driving assistance systems based on vehicular ad hoc networks (VANETs) are used to broadcast data regarding road safety information. Road information is sensitive data; therefore, exchanging traffic information via VANETs must be complete and trustworthy [7]. Concerns about security and veracity remain the principal barriers for adopting VANETs to improve road safety [6, 7]. Malicious nodes can inject false information into the VANET, including bogus traffic incidents [6, 7].  
In VANETs, node communication has greatly improved road safety in large-scale street networks. Smart vehicles communicate, collaborate, coordinate, and share road information and resources to develop smart roads [5, 6]. Security and truthfulness are then required to make such interactions via VANETs effective [4, 5, 6, 7]. Node trust measures a node's ability to behave correctly and actually accomplish what it is supposed to do [7].  
Network throughput is dramatically reduced when compromised nodes attempt to corrupt data in VANETs [5; 6]. A novel incentive scheme that detects and excludes malicious nodes is proposed in this work to address this issue.  
In our scheme, parameters are collected by the road side unit (RSU) to compute the trust value for each node. Collecting reliable information enables the RSU to evaluate node trust accurately. The RSU, on the other hand, is responsible for collecting data from all nodes, including other RSUs. Unfortunately, measuring trust based on an interaction between an RSU and a node is insufficient. The RSU may receive inaccurate information from a node, even if the node provides the certified reputation. Nonreliable information would affect the ultimate trust value. Eventually, the nodes' impression of their surrounding nodes would be inaccurate.  
The remaining sections of this article are structured as follows: Section 1 introduces relevant work and our contributions to the study. Following that, the VANET will be discussed in Section 2. In Section 3, we describe the proposed security system. Then, in Section 4, we will provide some of the performed tests and prove the performance of the VANET under various scenarios using our method. Section 5 represents the conclusion of the article.
# Statistical Analysis and Modeling for Optical Networks
## 5 Statistical Analysis and Modeling for Optical Networks
### Dimensioning in Optical Networks
#### 5.3.4 Statistical Modeling of Shortest Path Lengths
This offers a practical approach to characterizing path length distributions, leveraging the Johnson S\({}_{\text{B}}\) distribution's flexibility and the simplicity of convex area computation. The knowledge of this distribution helps in the estimation of several parameters of the optical networks which depend on the shortest path lengths. As shown in Figure 8, the distribution of the shortest path lengths can be used to identify the optical modulation and demodulation techniques at the end of the node pairs. It can also be used to determine the shortest path related parameters, such as the compensation techniques at the nodes for the transparent optical networks.
## 3 Analysis of Finite Element Simulation Results
### Ductility Analysis of Joints  
Ductility indicates the ability of a material to deform plastically before being subjected to a force that results in fracture. It serves as a crucial indicator of the ability of a structure to withstand deformation. Structural ductility is commonly quantified using the ductility coefficient \(\mu\). The ductility coefficient \(\mu\) is calculated using the following formula: \(\mu=\Delta_{u}/\Delta_{y}\), where \(\Delta_{u}\) represents the ultimate displacement of the structure or member, and \(\Delta_{y}\) represents the yield displacement of the structure or member. The parameters associated with the ductility coefficient can be determined from a typical P-\(\Delta\) skeleton curve. The results of the ductility calculation are typically presented in a calculation diagram, as depicted in Figure 5. The detailed calculation results can be found in Table 2.  
Based on the information in Table 2, the ductility coefficient of the prefabricated joint under a low circumferential reciprocal load was 7.26, while the ductility coefficient of the traditional cast-in-place joint was 6.77. This indicates that the ductility of the prefabricated joint was enhanced by approximately 7.23% compared with the cast-in-place joint. The improved ductility of the prefabricated joint can be attributed to the superior ductility of  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Joint Form** & \(\Delta_{y}\) **(mm)** & \(\Delta_{u}\) **(mm)** & \(\mu\) & \(P_{y}\) **(kN)** & \(P_{u}\) **(kN)** & \(P_{\text{max}}\) **(kN)** \\ \hline Cast-in-place joint & 1.53 & 10.36 & 6.77 & 76.13 & 83.86 & 107.62 \\ Prefabricated joint & 2.36 & 17.13 & 7.26 & 103.24 & 123.51 & 141.77 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Calculation results of joint ductility.  
Figure 5: Ductility calculation diagram.  
steel compared with concrete. The inclusion of steel structures within the joint helped restrict the deformation of the concrete structure during the loading process. This reinforcement provided by the steel structures contributed to the improved ductility of the joint to a certain extent.