esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
We herein reported the preparation of a P(BHMP-AM)-Zn\({}^{2+}\) hydrogel (BHMP = 3- [bis(carboxymethyl)amino]-2-hydroxypropyl-2-methyl-2-propenoate, AM = acrylamide) as a simple and low-cost conductive hydrogel that was obtained via a facile polymerization and metal complexation approach, as confirmed by Fourier transform infrared spectroscopy. This hydrogel exhibited excellent adhesion to the skin, good tensile properties, and suitable biocompatibility. In addition, the tensile strain exceeded 2000% when the tensile stress was set at 150 kPa, and the compressive rate was 17 MPa at a compressive stress of 85%. Moreover, the P(BHMP-AM)-Zn\({}^{2+}\) hydrogel was found to exhibit antibacterial properties, leading to antibacterial activities of 10.92 and 65.92% against Escherichia coli and Staphylococcus aureus, respectively. Furthermore, when the hydrogel was attached to a joint of the human arm, it was able to sensitively convert the movement of the joint into an electrical signal, thereby indicating the potential of this hydrogel for use as a wearable sensor material. Additionally, when P(BHMP-AM)-Zn\({}^{2+}\) was prepared as an adhesive material and attached to a joint, the hydrogel underwent deformation upon movement of the joint. This resulted in a change in resistance, ultimately indicating that it was possible to monitor joint movement by detecting changes in the resistance value. Overall, the device prepared using this hydrogel was able to rapidly and accurately detect the activity of a human joint, in addition to transferring the movement signals into electrical signals. The above results, therefore, demonstrate that the P(BHMP-AM)-Zn\({}^{2+}\) hydrogel appears to be a suitable candidate material for use in the low-cost and simple preparation of wearable devices.  
[PERSON] and [PERSON] designed the study. [PERSON] performed the experiments. [PERSON] and [PERSON] wrote the manuscript. All authors also made significant contributions by discussing the results and enhancing the manuscript for intellectual content. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Natural Science Foundation of China under Grant No. 51975367.  
Not applicable.  
The data presented in this study are available in the article.  
The authors declare no conflict of interest.
# Design, Implementation, and Field Testing
## 4 System Implementation
### Gesture Recognition and Activity Detection
```
1<RegisterRules>
2<Configuration>
3<TypeBoneOrientation</Type>
4<AlertBody>\"Registration</AlertBody>
5<DownstreamJoint>\"WriteLeft</DownstreamJoint>
6<UpstreamJoint>\"Elboucfier</UpstreamJoint>
7<Plane>\"Transverse</Plane>
8<AlphaAngle>\">\"</AlphaAngle>
9<Margin>Evaluation> \"20</MaxAngleDeviation>
10</Configuration>
11</RegisterRules>
12<ActivityRules>
13<Configuration>
14<TypeBoneOrientation</Type>
15<AlertBody>\"Bend</AlertBody>
16<DownstreamJoint>\"HiPcenter</DownstreamJoint>
17<UpstreamJoint>\"ShoulderCenter</UpstreamJoint>
18<Plane>\"Transverse</Plane>
19<AlphaAngle>\"</AlphaAngle>
20<Minangle>\"> \"30</MinAngleDeviation>
21</Configurations>
22</ActivityRules>
```  
Listing 1: Definition for the registration gesture and the back-bending activity used in this study. For both the registration gesture recognition and the non-compliance activity detection, we depend on the floor plane information. As illustrated in Figure 6, we can derive the vector that is perpendicular to the floor plane, \(V_{p}\). For the registration gesture recognition, we measure the angle \(A_{g}\) formed between the vector from left elbow to the left wrist and \(V_{p}\). For the back-bending non-compliance activity detection, we measure the back flexion angle \(A_{b}\) formed between the vector from the hip center to the shoulder center and \(V_{p}\). Figure 6: Registration gesture recognition and non-compliant activity detection. In earlier versions of our system, the floor plane information is obtained from every skeleton frame for gesture/activity recognition. This requires that the floor must always be clearly visible to the Kinect sensor. Our field study showed that this assumption does not hold because the crowded room could prevent Kinect from determining the floor plane. If the floor plane is not visible to Kinect, no one could register with the KinectServer and no non-compliant activity could be detected. This issue is resolved by taking the floor parameters during the initial deployment instead of taking the parameter from each frame. This is possible because the Kinect sensor is fixed at the wall.
# A Study on the Heat Transfer of Surrounding Rock-Supporting Structures in High-Geothermal Tunnels
## 4 Results and Discussion  
### Validation of the Calculated Result Against the Field Test Result  
#### 4.1.1 Surrounding Rock  
Test Locations 1 and 2 were chosen to compare tested results and calculated results of temperature, as shown in Figure 9. It can be revealed that the temperature of the surrounding rock decreased with time. The closer to the side wall, the greater the temperature dropped. The calculated results also reflected the slight rising of rock temperature near the tunnel sidewall after the accomplishment of the secondary lining construction, especially in Test Location 2. The change laws of temperature are consistent with the experimental curve to some extent.  
However, compared with the test results, the calculated curve deviates from the upside of the experimental curve, but the deviation is small. The difference between tested and calculated results became smaller within the next few weeks. A major reason for this was that in the first few days, tested sections were close to the construction work surface. More cooling measures were taken such as sprinkling water and placing ice cubes to protect the personnel and machinery safety from heat damage. As the working face of the tunnel continued to advance, the heat transfer state of rock and air returned to normal. Nevertheless, the air temperature inside the tunnel was thought of as a constant average value in the prediction model. Moreover, due to the significant effect of cooling measurements on the large temperature difference of the environment, the higher the initial rock temperature, the greater the difference between tested and calculated results.
## 4 Application in Crankshaft Bearing Design
### Analytic Solution Procedure  
The interference between bearing sleeve and block calculated by Equation (3) at operating temperature (100 \({}^{\circ}\)C) is 0.0614 mm; the interference between front bearing block and cylinder bearing bore is 0.028 mm; and that between back bearing block and cylinder bearing bore is 0.037 mm.  
The modulus reduction coefficients of this problem are all set to be 70% with the definition in Section 2.4. Taking the height of the cylinder as its characteristic length, the value is assigned as the diameter of the outer cylinder in tri-layer thick-walled cylinder model.  
The interference fit in sliding crankshaft bearing is equivalent to a tri-layer thick-walled cylinder problem, the contact pressure of which is solved by Equations (14) and (24). The results are then substituted into Equations (26)-(28) to get the stress of each component.  
Figure 10: Support system of sliding crankshaft bearing.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Component**} & \multirow{2}{*}{**Material**} & \multicolumn{2}{c}{**Elasticity**} & \multirow{2}{*}{**Poisson Ratio**} & \multicolumn{1}{c}{**Expansion Coefficient**} \\ \cline{1-1}  & & **Modulus (GPa)** & & & \\ \hline Cylinder & Grey iron & 140 & 0.25 & \(1.039\times 10^{-5}\) \\ \hline Front bearing block & Aluminum alloy & 74.5 & 0.3 & \(2.05\times 10^{-5}\) \\ \hline Back bearing block & & & & \\ \hline Bearing sleeve & Steel & 210 & 0.33 & \(1.112\times 10^{-5}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Material property of the components of crankshaft bearing support system.
# A Software Defined Radio Evaluation Platform for WBAN Systems
###### Abstract  
In recent years, the Wireless Body Area Network (WBAN) concept has attracted significant academic and industrial attention. WBAN specifies a network dedicated to collecting personal biomedical data from advanced sensors that are then used for health and lifestyle purposes. In 2012, the 802.15.6 WBAN standard was released by the Institute of Electrical and Electronics Engineers (IEEE), which regulates and specifies the configurations of WBAN. Compared to the prevailing wireless communication technologies such as Bluetooth and ZigBee, the WBAN standard has the advantages of ultra-low power consumption, high reliability, and high-security protection while transmitting sensitive personal data. Based on the standard specification, several implementations have been published. However, in terms of evaluation, different designs were implemented in proprietary evaluation environments, which may lead to unfair comparison. In this paper, a Software-Defined Radio (SDR) evaluation platform for WBAN systems is proposed to evaluate the RF channel specified in the IEEE 802.15.6 standard. A narrowband communication protocol demonstration with a security scheme in WBAN has been performed to successfully validate the design in the proposed evaluation platform.  
software-defined radio; wireless body area network; evaluation platform +
Footnote †: journal: Sensors
# A DRL-Based Satellite Service Allocation Method in LEO Satellite Networks
## 4 Proposed Method
### MCF-Based Method  
As a benchmark for evaluating our method, we selected an advanced algorithm in the field of resource allocation, which is summarized in the flowchart below. This algorithm represents the state of the art in edge user service allocation algorithms known to date. In contrast to the algorithms described in the Introduction, this algorithm considers four types of resources and achieves high-level efficiency in user service allocation at a relatively low algorithmic complexity. The maximum capacity first (MCF) algorithm assesses users' computational demands, prioritizes them based on the assessment, and strives to allocate  
Figure 5: Proposed algorithm flowchart.  
users to servers with higher resource utilization [40]. This heuristic algorithm, based on a greedy approach, has demonstrated outstanding performance in ground networks, making it a suitable choice for comparison in our work.The scheme's concrete steps are depicted in Figure 6.
# Dynamic Routings in Satellite Networks: An Overview
## 3 System Model and Characteristics
### Network Characteristic  
In the satellite networks, the relative movement of the satellite nodes has certain regularity, and the positions are not fixed as the general ground nodes. The change in link quality caused by the relative motion of satellite nodes is an urgent problem in space networks. Because of the frequent changes in links, network management becomes over-dependent on time, and the whole network topology plans will be influenced. The following are the main features of the satellite network: (a) _broadcast mode operation allows for multi-access communication; (b) large communication capacity for multiple service transmission; (c) the ability to compose complex network topologies; (d) safety and reliability._ Traditional routing strategies in spatial networks have many problems, such as the frequent handover of links, the high routing overhead, and the poor quality of service.  
Since the dynamic satellite topology structure is predictable, the centralized control scheme based on software-defined network (SDN) technologies is adopted to design the routing mechanism. The ground SDN controller completes the routing calculation. By combining inter-satellite and terrestrial user links, a private multiprotocol label switching (MPLS) protocol can carry and exchange multi-layer network protocols. The ground station SDN controller handles routing protocols such as open shortest path first (OSPF); configures network address mapping tables for tags, satellites, and terminals; and deletes MPLS and label distribution protocol (LDP) switching paths.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Architecture** & **Network Scenario** & **Control Pattern** & **Performance** & **Limitations** \\ \hline MEO-LEO & Satellite & Central & Short delay, low power requirements & Dynamic Inter-satellite Links \\ \hline GEO-LEO & Satellite-terrestrial & Central & Large network coverage, and high on-star processing power & Dynamic link connection,and high implementation costs \\ \hline GEO-MEO-LEO & Satellite & Central & Robust link and superior coverage performance & High construction and maintenance costs, difficulty of implement \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of subnetworks.
# Road Extraction Based on Improved Convolutional Neural Networks with Satellite Images
## 5 Conclusions  
Deep learning has been applied in various fields, and road extraction was developed with convolutional neural networks. This research proposes improved networks, D-DenseNets, for reducing model size and increasing IoU scores. For the structure of the improved networks, a D-Dense block module was proposed according to the dense connection structure. In addition, D-DenseNet was constructed for semantic segmentation by using D-LinkNet as the center part with a D-Dense block. Meanwhile, D-DenseNet also introduced a stem block instead of an initial block for obtaining more detailed information. The two datasets were used to build and evaluate. Low-level road extraction from the team dataset of the WorldView-4 satellite showed that D-DenseNet (k = 24) + stem block has the highest IoU score and a smaller size compared with the selected models. Further, the results from public datasets showed that D-DenseNet not only the best IoU scores and a smaller size, but better performance for extraction in complicated scenes. The two experiment results show that the proposed D-DenseNet based on the D-Dense block shows better performance than the original D-LinkNet and DenseNet in terms of IoU scores and model size. So, the improved network can be applied for low-level road extraction.  
In terms of model size and IoU scores, the function of each part of the network is the next research area, and each hyperparameter in the training phase should be carefully considered. Meanwhile, some postprocessing methods should be introduced to improve the quality of output images, which are still necessary for further processing to improve the presentation quality of low-level road extraction.  
Conceptualization, L.H.; formal analysis, L.H. and Y.L.; funding acquisition, D.T.; investigation, B.P.; methodology, Y.L.; resources, Y.L.; software, B.P.; supervision, D.T.; validation, B.P.; writing--original draft, L.H.; writing--review and editing, L.H. and D.T. All authors have read and agreed to the published version of the manuscript.  
The Key Projects from the Ministry of Science and Technology of China (No. 2020 YFA0608203); the Science & Technology Plan Project of Sichuan Province (NO 2021 YFS0335).  
Not applicable.  
The data in the research is available. Please contact the corresponding author by sending e-mail to [EMAIL_ADDRESS].  
\begin{table}
\begin{tabular}{c c c} \hline
**Model’s Description** & **Model Size (MB)** & **IoU (\%)** \\ \hline D-LinkNet (101) + initial block + D-Block & 1.070 & 63.10 \\ \hline DenseNet (k = 24) + stem block + D-Block & 82.4 & 64.38 \\ \hline DenseNet (k = 32) + stem block + D-Block & 111 & 63.02 \\ \hline D-DenseNet (k = 24) + stem block + D-Block & 99.3 & 66.28 \\ \hline D-DenseNet (k = 32) + stem block + D-Block & 169 & 65.25 \\ \hline \end{tabular}
\end{table}
Table 2: Results using the public dataset for selected models.
## 3 Results
### MERIS (Synthetic)  
We applied the 13 existing and four new algorithms in the form of 35 variants for BGA estimation to synthetic Sentinel-3/MERIS/OLCI data for Harsha Lake with limited success (Tables S1, S2 and S4, Figure 12) before severe masking (Figure 12a). MERIS has a narrow band capable of measuring the 620 nm phycocyanin absorption feature. Ten of the BGA (direct) and Chl-a (indirect or proxy) spectrally-oriented semi-analytical algorithms considered here had acceptable performance (Pearson's \(\tau^{2}>0.6\); \(p<0.001\)) when tested against in situ YSI BGA probe data (BGA_PC_RFU) with this sensor in this experiment after severe masking. In order of decreasing performance, the MERISMM092 BDA, MERISBe16 NDPHY, MERISMM12 NDCI, MERISGo04 MCI, MERISWy08 CI and MERISKu15 PhyCI, MERISSi052 BDA, MERISBe162 Bsub, MERISHu103 BDA, and MERISMM143 BDAopt algorithms all estimated BGA well in this exercise. We agree that MERIS/OLCI can be a part of operational water quality monitoring systems [20]. However, high-spatial resolution CASI data show that there was significant spatial heterogeneity in the values of BGA on a scale much finer than either Sentinel-3/MERIS/OLCI (300 m) or MODIS (250 m) pixels. Index imagery with raw and normalized index Type 1 (Pearson's r) linear regressions are shown for the best performing algorithm (MERISMMO92 BDA) in Figure 12.  
Type 1 Standard Error of Regression (Standard Error of Estimate or S) values track our Pearson's \(\tau^{2}\) values (Table 3). The Type 2 geometric mean regressions [41] show an ordering of correlation similar to those of Type 1 regressions [17; 49] with somewhat poorer results for Landsat-8 (Table 4).  
Figure 12: _Cont._
## 4 Analysis of Existing Solutions
### Overview of Existing Educational Applications in Education
#### 4.1.1 Examples of Gamified Educational Applications  
Gamification, as the process of integrating game elements into a non-game environment, has been widely accepted in the education sector. Various applications employ reward mechanisms, scoring, competition, and levels to encourage students' active participation and learning. Some of the most well-known examples of gamified educational applications include the following:  
1. Kahoot!--a popular quiz platform that allows teachers to create quizzes where students respond in real time. The competitive element and visually appealing interface foster student engagement [36].
2. Duolingo--a language-learning app that uses gamification to motivate users to learn daily. The point and level reward system, along with daily challenges, allows users to compete against themselves or others [37].
3. Classcraft--an educational platform that transforms the classroom into an interactive game where students take on character roles and collaborate to achieve learning goals. Gamification promotes teamwork and social-emotional development [38].
4. Quizlet--an application that allows the creation of digital flashcards and quizzes. Gamified elements, such as playing against the clock and achieving high scores, make learning more interesting and stimulating [39].  
These applications demonstrate that gamification can successfully motivate students, but they also highlight the need for careful design to avoid potential overwhelm or dissatisfaction among students.
# Reliable Visual Exploration System with Fault Tolerance Structure
## 3 Active Controller Based on Semantic Segmentation
### Ground Recognition  
In most of the existing controllers, the localization of the mobile robot is needed for path planning. However, due to the fragility of visual tracking, it is hard to obtain global localization all the time. A reactive strategy is used in our system, it is able to perform ground recognition from in a single image.  
For ground recognition, without loss of generality, two assumptions are made: the monocular camera is placed in the direction of the heading of the mobile platform; secondly, the camera is fixed with a pitch in which the ground can be observed most of the time; an illustration is shown in Figure 2.  
Our system uses the Pascal context pre-trained model, whose output is the segmented image with the same size as the input image. In addition, neither prior-knowledge nor localization is needed for this segmentation. An example of the segmentation results is illustrated in Figure 3.  
Figure 2: Illustration of the set-up of the camera; it is fixed on the mobile robot platform in the heading direction.
# Influence of Cavitation and Shaft Deformation in the Analysis of Lubrication of the Stern Bearing
## 5 Results and Analysis
### The Influence of Cavitation  
In order to verify the accuracy of the calculation model and program, the parameters of sliding bearings in Refs. [37; 38] are used. The bearing parameters are shown in Table 1.  
[PERSON] et al. demonstrated that the bearing capacity obtained by considering the influence of cavitation in the steady state can be three times higher than that without considering cavitation [39]. [PERSON] [40] also emphasized in the literature that cavitation can enhance the bearing capacity. The cavitation pressure in case 1 tested by [PERSON] et al. is applied in the simulation [41]. The calculations using our proposed algorithm also support this conclusion. As shown in Figure 7, under the same load, the eccentricity of the bearing when considering the cavitation effect is lower than that without cavitation.  
The distribution of bearing oil film pressure under different boundary conditions is calculated. As shown in Figure 8, the maximum film pressure is lower when the cavitation effect is considered. Under a 1.6 kN load, the maximum oil film pressure is 0.251 MPa when the cavitation effect is considered, while the maximum oil film pressure increases  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Radius (mm)**} & **Radius Clearance** & **Length–Diameter** & **Cavitation Pressure** \\  & **(mm)** & **Ratio** & **(kPa, Gage)** \\ \hline
50 & 0.1455 & 1.333 & \(-72\) \\ \hline
**Angular Velocity** & **Dynamic Viscosity** & **Ambient Pressure** & **Density (kg/m\({}^{3}\))** \\
**(rad/s)** & **(Pa-s)** & **(kPa, Gage)** & **Density (kg/m\({}^{3}\))** \\ \hline
48.1 & 0.0127 & 0 & 950 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of bearing for investigation of cavitation.  
Figure 7: Effects of cavitation on the eccentricity of journal bearing.  
to 0.291 MPa when the cavitation effect is neglected. Due to the effect of cavitation, the eccentricity is reduced, and the maximum oil film pressure is reduced.  
The effect of mesh size is also studied. In the simulation, four meshes with total elements count of 120, 480, 1920, and 7680 are applied. The calculated eccentricity of the bearing under a total load of 2000\(N\) varies with the different meshes, with values of 0.57088, 0.56524, 0.56276, and 0.56134, respectively. The relative difference in eccentricity calculated with 120 elements compared to that with 7680 elements is 1.67%. Based on this result, it can be concluded that the result approaches convergence in the calculation and that the mesh meets the requirements for the calculation.