esa-sceva/satcom-chunk-collection huggingface dataset:
# Bio-Inspired Dielectric Resonator Antenna for Wideband Sub-6 GHz Range
## 3 Spiral Shell DRA Analysis and Optimization
Conversely, as shown in Figure 5b, as the h\({}_{\text{DRA}}\) is increased, the SsDRA gain improves in the range between 2.5 GHz to 4.5 GHz (red and yellow curves are relevantly higher than the blue curve), while it shows negligible changes around 5.5 GHz. Scattering Parameter S\({}_{11}\) and Realized Gain as a Function of the Exponential Function f(\(\theta\)) of the Optimized SsDRA  
In order to show the effect of the planar exponential function f(\(\theta\)) on the SsDRA performance, the coefficient c\({}_{\text{theta}}\) was varied by considering three values: 0.16, 0.18 and 0.20. The geometrical sizes of the SsDRA were kept unchanged: t = 2 mm, h\({}_{\text{DRA}}\) = 29 mm and h\({}_{\text{PIN}}\) = 17 mm. Figure 6a reveals that, when this coefficient is reduced, the lower cut-off frequency shifts at higher frequencies (from 3.5 GHz to about 4 GHz), while the minimum of the scattering parameter shifts from 5 GHz to 5.5 GHz. Figure 6b shows that when c\({}_{\text{theta}}\) is reduced, the antenna gain undergoes a reduction at 3.5 GHz (blue curve), while it exhibits a slight increase (about 0.4 dB) at 5.5 GHz (red curve). Figure 4: **(a)** Scattering parameter S\({}_{11}\) as the pin height h\({}_{\text{PIN}}\) is varied from 17 to 19 mm and the DRA thickness t is varied in the range 1–2 mm, h\({}_{\text{DRA}}\) = 29 mm; reference monopole antenna with a height of 17 mm is reported for comparison; **(b)** Realized gain as the pin height h\({}_{\text{PIN}}\) is varied in the range 17–19 mm and the DRA thickness is varied in the range 1–2 mm, h\({}_{\text{DRA}}\) = 29 mm; reference monopole antenna with a height of 17 mm is reported for comparison. Figure 5: **(a)** Scattering parameter S\({}_{11}\) as the DRA height is varied in the range 10–29 mm, considering h\({}_{\text{PIN}}\) = 17 mm and t = 2 mm; **(b)** Realized gain as the DRA height is varied in the range 10–29 mm, considering h\({}_{\text{PIN}}\) = 17 mm and t = 2 mm. Finally, we underline that, if \(\theta\) is varied in the range 0-2\(\pi\) (1 complete round), while other parameters are fixed--t = 2 mm, h\({}_{\text{DRA}}\) = 29 mm, h\({}_{\text{PIN}}\) = 17 mm and c\({}_{\text{theta}}\) = 0.2--the SsDRA bandwidth experiences a significant reduction, decreasing down to about 1.5 GHz, while the realized gain value approaches the monopole one, in the frequency interval from 4.5 GHz to 6 GHz.
## 5 Conclusions  
In this work, combining the DIC technology, five different rock types tested with three typical loading methods were employed to investigate their effects on the failure load and fracture behavior of disc specimens in the Brazilian test. The key findings are summarized below:  
1. The failure load and deformation of the Brazilian discs are strongly affected by the loading type. The average tensile strength determined by the Type III test is the highest among the three test types for all rock types, while the Type II test is the lowest. Additionally, the vertical deformation response to load of the disc specimens tested by Type II test specification is the largest, while the Type I test resulted in the lowest.  
2. The Type II test is evidently not applicable to measuring the tensile strength of rocks. The other two testing methods may lead to an invalid failure mode for rock materials with high stiffness and tensile to compressive strength ratios. In this regard, other testing methods and rock types need to be further investigated, which is also the next research direction of this work.
3. In the Brazilian test with the ISRM standard specification, the maximum equivalent stress point (possible crack initiation point) based on the Mohr theory generally tends to occur further away from the disc center with the increasing ratio of tensile to compressive strength in rock specimens. Nevertheless, this is not absolute. It was found that the maximum equivalent stress of a disc specimen is co-controlled by the material stiffness and its tensile-compression ratio.
4. It is not recommendable to evaluate the validity of the Brazilian test only by visually observing the post-fracture pattern of the disc specimens. In fact, DIC can enable us to understand the damage evolution process and failure modes of the specimens.  
Conceptualization, D.L.; data curation, Z.H.; funding acquisition, D.L.; investigation, B.L. and Q.Z.; validation, D.L.; writing--original draft, B.L.; writing--review and editing, D.L., Z.H. and Q.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Outstanding Youth Science Foundations of Hunan Province of China (No. 2019 JJ20028) and the Key projects of the National Natural Science Foundation of China (No. 41630642).  
The authors declare no conflict of interest.
# Optimizing Apache Spark MLlib: Predictive Performance of Large-Scale Models for Big Data Analytics
## 3 Methodology  
The proposed methodology for predicting the performance of machine learning (ML) operators in Apache Spark is illustrated in Figure 1. The process launches with the foundation of the infrastructure (Step 1), during which a multi-node Apache Spark cluster is developed. This cluster brings together a leader node and a range of follower nodes, all linked via a private network, which provides the ability for distributed data processing. For the data creation in Step 2, we use data generators specifically for some of the ML algorithms such as K-Means, Random Forest, and Word2 Vec. The datasets, in terms of their size and dimensions, allow for a wide range of inputs to assess operator performance.  
After creating the dataset, in Step 3, the ML operators run with different parameters and collect metrics inclusive of execution time, CPU utilization, and memory usage. The collection of these performance metrics that show operator resource use and time efficiency is carried out while the execution is in progress; they serve as the groundwork for model training. In Step 4, the metrics go into a prediction model in Step 5, where they are trained to project the performance of ML operators according to the dimensions of the input dataset. The final model predicts important performance indicators, including execution time and memory usage, giving the ability to optimize and plan computational resources for future operations.  
Figure 1 offers an overarching view of the system architecture, while Figure 2 shows the internal architecture of the workflow for dataset preparation, training, and validation. The latter diagram emphasizes the logical steps, starting from synthetic dataset generation and culminating in model training and performance evaluation, effectively complementing the high-level depiction in the former.  
In Step 3 of Figure 1, the ML operators are executed with different parameters and the metrics include execution time, CPU, and memory usage. However, the collection of these performance metrics indicating operator resource use and time efficiency are performed in progress of the execution and form the basis for model training. In Step 5, the metrics obtain predicted in a model to learn how to predict the performance of ML operators on the basis of the dimensions of the input dataset in Step 4. It also predicts the execution time and memory usage of important performance indicators in order to allow the planner to optimize and plan computational schedules for future operations.  
Figure 1: Illustration of the methodology.  
Figure 2: Workflow for dataset preparation, training, and validation.
# A Range-Extension Method for an Indoor Standard Device for Large-Scale Length Measurement
## 2 Indoor Standard Device for Large-Scale Length Measurement
### Regulated Environment  
Control of temperature, pressure, and humidity: the pressure remained unregulated, while the humidity was automatically controlled through a dehumidification system, and the temperature was regulated via an air conditioning system.  
Figure 1: Major components of an indoor standard device for large-scale length measurement.  
Compensation of temperature, pressure, and humidity: A measuring and compensation system was implemented to measure the real-time environmental readings of atmospheric pressure (p), air temperature (t), and air humidity (f) along the laser path, which were then used to calibrate the laser interferometer values based on the Edlen equation. Specifically, a comprehensive temperature measurement system consisting of 30 temperature sensors was employed to enable consistent temperature distribution. Along the laser path, 28 sensors were precisely placed at two-meter intervals along the Z1 wall, while the remaining two were installed on the Z3 wall, two meters and four meters above the ground, respectively. Furthermore, a dedicated temperature detection system utilizing 12 sensors was set up to monitor the temperature of materials under test. These sensors included two near the arc corridor on the Z1 wall, four on the long-scale moving target platform, two placed flexibly, and four at the shorter scale on the Z3 wall. An overhead view of the laboratory that houses the indoor standard device for large-scale length measurement is illustrated in Figure 2.  
To measure air pressure and humidity, we utilized sensors for monitoring air pressure, temperature, and humidity. The accessories for these sensors were customized based on the specific laboratory requirements. Also, they should facilitate the adjustment of sensor positions to ensure stable and accurate length measurement and the measurement and compensation of precise environmental parameter inputs.
# Noise-Assessment-Based Screening Method for Remote Photoplethysmography Estimation
## 3 Method
### Dataset  
In the present study, data were collected from Sangmyung University, replicating the conditions of an established open dataset. Twenty participants were included, comprising nine males and eleven females, engaged in four distinct scenarios, with each scenario lasting for two minutes. During these scenarios, participants were instructed to execute the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Related Works** & **NIR Applicable** & **Screening Method** & **rPPG Extraction** \\ \hline Green [11] & & Face ROI detection \\ DistancePPG [12] & & SNR-based ROI selection \\ CHROM [6] & & Chrominance \\ POS [7] & & Chrominance \\
3D CNN [13] & & Supervised learning \\ Physformer [17] & & Supervised learning \\ [PERSON], [PERSON] et al. [18] & & Unsupervised learning \\ [PERSON], [PERSON] et al. [16] & & Face ROI detection \\ Proposed method & & SNR-based ROI selection \\ \hline \hline \end{tabular}
\end{table}
Table 1: Related rPPG works.  
following tasks: maintain a steady gaze, speak while maintaining a steady gaze, rotate their faces at varying angles without speaking, and rotate their faces at varying angles while speaking. As depicted in Figure 3, infrared cameras equipped with a 940 nm bandpass filter and infrared illuminator were utilized for data collection. Moreover, ground truth signals were obtained by leveraging a contact photoplethysmography (PPG) device. Among the acquired dataset, two subjects exhibited light skin values, leading to significant saturation. Consequently, the data from these two subjects were excluded, resulting in a dataset containing information from 18 individuals. The final dataset, consisting of 72 cases, was employed for both training and testing purposes. Details of camera equipment and experimental setup of the dataset are shown in Table 2.
# Numerical Simulation of Water Migration during Soil Freezing and Its Resulting Characterization
## 1 Introduction
### Materials  
Kaolin clay was selected for the water migration experiments in this study. Compared with montmorillonite clay, the unique reticulated microstructure of kaolin particles reduces water permeability, resulting in higher retention of free water. This characteristic enhances water migration, making kaolin more suitable for studying the freezing and swelling patterns of soil. Additionally, the physical parameters of the experimental soil were determined in the laboratory according to the GOST 20276-2012 Russian standard [19]. The basic properties of the soil are presented in Table 1[19].
## 2 Theoretical Analysis and Design of the MRD-Based Roller Cage Shoe  
### Basic Structure of MRD-Based Roller Cage Shoes  
This paper suggests employing a combination of MRDs and disc springs in series to create the cage shoe buffer structure, as an alternative to the conventional cage shoe buffer for semi-active vibration damping. MRDs are vibration dampers with smart material MRF as the force-transmitting medium. Adjusting the current in the excitation coil can obtain different strengths of the magnetic field. When MRF is under the action of the external magnetic field, the rheological properties of the MRF will be changed to produce resistance to movement; as a result, the energy of the movement will be consumed. In response to this, the purpose of vibration damping, which has the advantages of a continuously adjustable damping force, large output force, and fast response speed, can be achieved.  
In this paper, the design of the MRD-based roller cage shoe buffer damper is illustrated in Figure 3. During operation, the MRD is situated in the upper section of the cage shoe buffer, while the disc spring assembly is positioned in the lower part. The MRD primarily consists of a piston rod, cylinder barrel, and end caps on both sides. The damper adopts a single-cylinder double-outlet rod structure. One end of the piston is securely fastened to the swing arm of the roller cage shoe, providing guidance and support. The opposite end is linked in series with the disc spring via a damping baffle plate, ensuring ample space for the piston rod's movement and facilitating the transmission of damping force. The piston rod has a groove where the coil is tightly wound. It also contains a blind hole structure serving as a lead channel. The two end wires connect to the external program-controlled power supply through this channel. The end caps on both sides connect to the cylinder barrel, creating the MRF chamber. To prevent any MRF leakage, a sealing assembly is in place between the piston rod and cylinder barrel. The topside cover features a liquid-filling port, while the bottom cover is securely affixed with a disk spring sleeve. The guide bar for the disc spring is bolted to the base, allowing the disc spring set to move axially along the guide bar. This arrangement serves as supplementary vibration damping during the buffer's regular operation.
## 2 Network Model and Underwater Acoustic Propagation
### Underwater acoustic propagation  
In this section, we review the energy consumption characteristics of a typical acoustic modem.  
The passive sonar equation [22] expresses the signal to noise ratio (SNR) of an emitted underwater signal at the receiver:  
\[SNR=SL-TL-NL+DI\geq DT \tag{1}\]  
where SL is the source level, TL is the transmission loss, NL is the noise level, DI is the directive index, and DT is the detection threshold of the sonar. The quantities of the Equation (1) are in _dB re_\(\mu\)_Pa_, where  
Figure 1: Underwater acoustic moored monitoring system.  
the reference value of 1 \(\mu Pa\) equals to \(0.67\times 10^{-18}\)_Watts/m\({}^{2}\)_[22]. We use the notation _dB_ to signify _dB re \(\mu Pa\)_ in the rest of this paper.  
The noise level in shallow water is related to shipping activity, wind level, biological noise, seaquakes etc. In this paper, we take the value of the NL to be 70 dB in shallow water [22]. The deep sea is more quiet than shallow water, and we consider NL to be 50 dB [24]. DI and SNR are related to acoustic modems and hydrophones, we take DI = 3 dB and SNR = 20 dB respectively [16].
# Enhancements and Challenges in CoAP--A Survey
## 3 Applications of CoAP
### Real-Time Remote Monitoring  
Another application of CoAP is the real time remote monitoring of data in the domain of healthcare. The subsection describes how CoAP is used for real time remote monitoring.  
Remote Monitoring and Real-Time Display in Healthcare  
Another application of CoAP in implementing a remote healthcare monitoring system is presented in [21]. The proposed approach can monitor the vitals of patient and display them in real time using a web browser. The CoAP protocol is implemented in the Mozilla Firefox Web browser which acts as a client and the sensors attached to patient's body act as CoAP server. Two implementations of CoAP are performed in simulating the proposed approach: Erbium which is a REST service for Contiki OS and Copper which is an add-on for the Mozilla browser. The web browser acting as a CoAP client with Copper add-on implementation uses the GET method of CoAP to access the resources from server.  
CoAP is implemented in various domains for applications such as interoperability and integration with other protocols and standards, streaming services, security, resource observation and discovery and many other. Section 3 describes the use of CoAP in these applications; however, the basic architecture of CoAP has many limitations that need to be fulfilled. For example, the congestion control mechanism in basic CoAP is inefficient and does not perform well in dynamic network conditions, which led to the development of several congestion control mechanisms proposed by researchers. Similarly, the limitations of interoperability of CoAP with other protocols for instance the interoperability of CoAP with HTTP for cross protocol authentication was not possible in basic CoAP. Moreover, the basic CoAP runs on UDP and uses DTLS for security. The DTLS is not a very secure protocol and is vulnerable to hacking attacks. In addition, high cost and computational power requirements of DTLS also make it less suitable. Hence, to make CoAP more secure, various enhancements in the CoAP security are proposed which include enhancements in the DTLS protocol, use of hashing techniques for CoAP security instead of using DTLS, etc. For using the CoAP for streaming services in constrained devices, basic CoAP employs the stop-and-wait mechanism to send packets which is an inefficient method for sending content between nodes. Therefore, here again there is a need for enhanced version of CoAP for employing CoAP in streaming services. To fulfil the limitations in basic CoAP for various applications, numerous enhancements in the architecture of basic CoAP are proposed by the researchers. The enhancements are described in Section 4 in detail.
###### Abstract  
Space object recognition in high Earth orbits (between 2000 km and 36,000 km) is affected by moonlight and clouds, resulting in some bright or saturated image areas and uneven image backgrounds. It is difficult to separate dim objects from complex backgrounds with gray thresholding methods alone. In this paper, we present a segmentation method of star images with complex backgrounds based on correlation between space objects and one-dimensional (1D) Gaussian morphology, and the focus is shifted from gray thresholding to correlation thresholding. We build 1D Gaussian functions with five consecutive column data of an image as a group based on minimum mean square error rules, and the correlation coefficients between the column data and functions are used to extract objects and stars. Then, lateral correlation is repeated around the identified objects and stars to ensure their complete outlines, and false alarms are removed by setting two values, the standard deviation and the ratio of mean square error and variance. We analyze the selection process of each thresholding, and experimental results demonstrate that our proposed correlation segmentation method has obvious advantages in complex backgrounds, which is attractive for object detection and tracking on a cloudy and bright moonlit night.  
star image segmentation; correlation thresholding; 1D Gaussian morphology; complex backgrounds +
Footnote †: journal: Applied Sciences  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON] Segmenting Star Images with Complex Backgrounds Based on Correlation between Objects and 1D Gaussian Morphology. _Appl. Sci._**2021**, _11_, 3763. [[https://doi.org/10.3390/app11093763](https://doi.org/10.3390/app11093763)]([https://doi.org/10.3390/app11093763](https://doi.org/10.3390/app11093763))
# Recent Advances in Wearable Devices for Non-Invasive Sensing
## 2 Materials and Structural Design
### Materials
#### 2.1.5 Hydrogels  
Hydrogels can form conformal contact with the human body because their mechanical properties are similar to those of the interfaces between human skin and the underlying tissue [41]. Electrolytes and ionic liquids are commonly used to achieve ion transportation in hydrogels [65]. A polyacrylamide hydrogel containing LiCl was used in making ionic interconnects [66]. The hydrogel worked as an ionic conductor and also provided high stretchability and transparency. [PERSON] et al. demonstrated a hydrogel-based aquatic electronic skin that has excellent self-healing properties [67]. The electronic skin consisted of a fluorocarbon elastomer and a fluorine-rich ionic liquid. The ion-dipole interactions between those two materials enabled fast and repeatable electro-mechanical self-healing in wet, acidic, and alkali environments.
## Appendix A Implemented Web Pages and Use Cases
### Agency Sign-in and Password Reset  
Figure 11 shows the agency sign-in page, the reset password page, and the automatically generated email containing a link to reset the password. The sign-in page on the left includes a special checkbox, which ensures that the operation is being done by a human but not a software bot. The password is masked by bullets for privacy while the user is typing it.  
If the user forgets the password by any chance, the person can the click the link provided on the sign-in page to reset the password, as shown in the middle figure in Figure 11. An automatically generated email will be then sent to the user's email account, as shown in the rightmost figure in Figure 11. This email contains a link for the user to reset the password.