esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
A pin-on-disc dry sliding wear test of three alloys processed by the electromagnetic stirring method was conducted at room temperature. The alloys included as-cast Al-5 Si-1 Cu-0.5 Mg alloy, as-cast SiCp/Al-5 Si-1 Cu-0.5 Mg, and SiCp/Al-5 Si-1 Cu-0.5 Mg alloy with T6 heat treatment. The conclusions of this investigation are as follows:  
Figure 13: SEM images of SiCp/Al-5 Si-1 Cu-0.5 Mg composites after T6 heat treatment under normal loads of **(a)** 15 N, **(b)** 30 N, **(c)** 60 N; **(d)** edging morphology of the as-cast Al-5 Si-1 Cu-0.5 Mg pin sample at 90 N.  
(1) After T6 heat treatment, the morphology of the eutectic silicon was spheroidal, and some light grey \(\gamma\) phases precipitated. The mechanical properties of SiCp/Al-5 Si-1 Cu-0.5 Mg alloy with T6 treatment performed the best of all. After T6 treatment, the hardness, ultimate strength, and elongation of the alloy were 101.86 HV, 273.64 MPa, and 6.12%, respectively. These values increased by 27%, 42.85% and 60.63%, respectively, when compared with the ones of as-cast Al-5 Si-1 Cu-0.5 Mg.  
(2) As the applied load increasing, the wear rates and friction coefficients of the alloy increased. The SiCp/Al-5 Si-1 Cu-0.5 Mg alloy with T6 treatment achieved the lowest wear rate and lowest friction coefficient of all the alloys. This indicated that T6 heat treatment benefited for the improvement of the wear properties of the alloy.  
(3) As the sliding speed increasing, the wear rates and friction coefficients of the alloy decreased. SiCp particles and Al\({}_{2}\)Cu phase had positive effects on the wear behavior of the alloy. With the sliding speed increase, the amount of delaminating grooves on the worn surface was decreased and the worn surface became smooth. The SiCp/Al-5 Si-1 Cu-0.5 Mg alloy with T6 treatement achieved the lowest wear rate and lowest friction coefficient of all the alloys.  
(4) Abrason and oxidation wear were dominant at low loads (15, 30 N). When the applied load increased to 60 N, plastic deformation was also observed on the wear surface. At a high applied load (90 N), plastic deformation and delamination were the main mechanisms of wear for the as-cast Al-5 Si-1 Cu-0.5 Mg alloy. With SiCp addition, the degree of delamination was decreased. After T6 heat treatment, the worn surface of SiCp/Al-5 Si-1 Cu-0.5 Mg alloy became smooth and flat, and no cracks were observed on the wear surface. As a result, the wear resistance of the SiCp/Al-5 Si-1 Cu-0.5 Mg alloy was improved.  
This work was supported by the national natural science foundation of china (No. 51364035), Loading Program of science and Technology of College of Jiangxxi Province (No. KJLD14003). [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON], [PERSON] and [PERSON] contributed reagents/materials/analysis tools; [PERSON], [PERSON] and [PERSON] wrote the paper.  
The author declares no conflict of interest.
## 4 Empirical Analysis  
### Data Source and Processing  
This paper takes Lixia District, Licheng District, Huaiyin District, Tianqiao District, and Shizhong District of Jinan City as the research scope, and discusses the charging stations of new energy vehicles within the scope as the research object.  
When analyzing the charging station network in the five districts of Jinan City, the data sources are divided into two categories, namely the geographical location information of the charging station and the location information of the bus lines. In order to avoid errors caused by subjective factors in the analysis of the layout of the charging stations, this paper captured the geographical location information of the charging stations in Lixia District,Shizhong District, Licheng District, Tianqiao District, and Huaiyin District of Jinan City through the open platform of AmAP, as well as the data information of all bus lines in these five districts, so as to lay a foundation for the subsequent visualization analysis by ArcGIS.  
First of all, Tuba bus, bus network, 8684, local network, and other websites are used to obtain the relevant information of bus routes and route stations. Such websites provide bus line names divided by numbers and letters, and use Python programming to climb all the bus line information in the required research area. Then, the AmAP open platform is used to provide API services and information such as line name, route site, and site location, which is then verified. In the same way, the open platform of AmAP is used to obtain the relevant information of new energy vehicle charging stations in the required areas, and the data of the charging stations are crawled by Python programming language. Finally, Python programming is used to parse the data, and the extracted data are preliminarily cleaned and organized, stored as csv files, and imported into Excel for the next stage of data processing and analysis.  
The data of bus lines and new energy vehicle charging stations obtained initially need to be further structured and arranged to facilitate the establishment of the subsequent network adjacency matrix, and then generate complex network models. There are duplicate stations passed by the original bus line, and the name description of the same station may be different for different bus lines. In this case, the corresponding location coordinates of the stations are used for further consolidation processing. For charging stations and bus line stations, the charging station and bus line stop rarely overlap directly. In this paper, the nearby charging station is extracted according to the bus line, and the distance of 100 m is taken as the limit. If the vertical distance of the selected bus line at the charging station is less than or equal to 200 m, the charging station is selected. Finally, a total of 1274 bus lines and 75 new energy vehicle charging stations were obtained.
# Mapping Winter Wheat with Multi-Temporal SAR and Optical Images in an Urban Agricultural Region
## 2 Study Area and Datasets
### Optical Satellite Data  
Landsat-8 launched in February 2013 and carries two sensors, the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS). The Operational Land Imager (OLI) is a nine-band push broom scanner with a swath width of 185 km, eight channels at 30 m spatial resolution, and one panchromatic channel at 15 m spatial resolution [37]. Four Landsat-8 OLI images were obtained for the following dates in 2016: 25 February, 12 March, 28 March, and 29 April. These images are Level 1T products downloaded from the USGS (US Geological Survey). In addition, a GaoFen-1 (GF-1) P/MS image with a spatial resolution of 2 m on 29 April 2016 was acquired. As the first satellite of the Chinese High-Resolution Earth Observation System [38], the GF-1 satellite was launched from Jiuquan Satellite Launch Centre (Gansu province, China) in April 2013, and carries two panchromatic/multi-spectral (P/MS) and four wide-field view (WFV) cameras [39]. The acquired GF-1 P/MS (Level 1A product) image served as reference data, with one panchromatic band and four multispectral bands.
## 4 Inspection Results
### Borescope Inspection Software  
The AI software achieved an inspection accuracy of 47.4%, with a processing speed of 30 to 60 images per second. This translates to 17-33 ms per image, which on average results in a processing time of 25 ms. Repeating the assessment of the dataset led to the same inspection results, thus the software was 100% consistent.  
AI was able to successfully detect anomalies on the blade edges (Figure 3a) and airfoil surfaces (Figure 3b). However, one of the challenges the software experienced was to differentiate defects and deposits. In several cases acceptable deposits were marked as defects (Figure 3c,d). Since the software was developed to assist the human operator, it was trained to pick up any anomalies and present the results to the operator, who then makes a serviceability decision. Thus, highlighting deposits is a safety cautious approach and is desirable to assure no critical defects are missed. However, it is concerning that other critical defects were missed (Figure 3f) although their appearance is somewhat similar to the previously mentioned deposits (Figure 3d), i.e., salient due to the high contrast between the defect and blade.  
Performance was highly dependent on the borescope camera perspective and lighting. Particularly difficult to detect were defects on blades that were poorly lit, as shown in Figure 3e. Furthermore, the location of the defect in the image (centre vs. corner) might have influenced the detectability (Figure 3d). Without a detailed understanding of how the algorithm works, no further interpretations of the results can be made.  
It shall be noted that for the purpose of this study, borescope stills were presented to the AI. This was done to allow for an 'equal' comparison of the human operator and software performance. In practice however, the AI would be presented with a borescope video (i.e., a series of images), thus having multiple opportunities to detect a defect when the blade rotates past the borescope camera. This would further provide some slightly different angles and lighting conditions depending on the relative position of the blade,  
Figure 2: Sample outputs of piece-part inspection software: (**a**) correctly detected nicks—true positive, (**b**) marked deposit and root radius—false positives, (**c**) missed bend—false negative, (**d**) missed airfoil dent—false negative. Software detections are indicated by red bounding boxes. Missed defects are highlighted by blue circles.  
camera and light source. Therefore, the inspection performance is likely to improve when processing borescope videos and could be analysed in future work.
## 3 Results and Discussion
### Characterization of Nanocellulose Gels and Films
#### 3.2.8 Surface Morphology of Films  
Figure 8 shows the SEM images of CNF\({}_{1}\) films, uncoated (left) and wax-coated (right). The images show a remarkable difference in the surface morphology of nanocellulose films. The uncoated CNF film shows a micro-structured fibrous texture typical of pure nanocellulose films. The surface of the wax-coated CNF film displays a smoother and continuous layer. The applied wax layer alters the surface roughness and enhances hydrophobic and barrier properties and how they document the contact angle results with water and the permeability to water vapor.  
Figure 8: SEM of surface morphology of uncoated CNF films (left) and wax-coated (right) (magnification: 1000\(\times\)).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Films**} & \multirow{2}{*}{**CA\({}_{Water}\)**} & \multicolumn{3}{c}{**Surface Energy (mN.m\({}^{-1}\))**} \\ \cline{2-4}  & & **Total** & **Dispersive** & **Polar** \\ \hline CNF\({}_{1}\) & \(52.53\pm 2.01\) & 46.05 & 28.85 & 17.19 \\ CNF\({}_{2}\) & \(49.96\pm 2.45\) & 49.63 & 29.11 & 20.52 \\ WC CNF\({}_{1}\) & \(110.93\pm 1.61\) & 28.76 & 28.75 & 0.01 \\ WC CNF\({}_{2}\) & \(108.17\pm 1.03\) & 28.93 & 28.91 & 0.02 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Water contact angle and surface free energy (results presented as mean \(\pm\) standard deviation).
## 1 Introduction
As for a standalone positioning system, the LEO signal could be used to realize positioning by its Doppler frequency as a Signal of Opportunity (SoOP) [18; 19]. Moreover, LEO satellites have the potential to transmit navigation signals [20]. Nowadays, it is also considered to transmit the Pseudorandom Noise (PRN) signal as GNSS system to get a more precise position [21]. Given these functions, the GNSS receiver can use the position information from LEO positioning to estimate the Doppler of the GNSS signal. Moreover, by the navigation signals transmitted by the LEO satellite, the receiver can extend the coherent integration time and further compress the loop bandwidth. A novel LEO augmented GNSS system and receiver structure is proposed in this paper so that the augmentation to the signal processing can be explored. In this designed system, LEO transmits GNSS ephemeris through its communication channel. Meanwhile, it can be used to locate the ground receiver by its Doppler frequency. In weak signal environments, the receiver realizes positioning using the LEO signal by Doppler shift first. The receiver could also obtain GNSS ephemeris information from the LEO system. Then, the ephemeris, as well as the estimated location and Doppler from the LEO satellite is used to reduce the GNSS signal searching range and extend the coherent integration time to capture weak signals in the acquisition stage. Meanwhile, the receiver clock bias and drift could be estimated in the LEO positioning. With this information, the receiver could achieve longer coherent integration and narrower loop bandwidth to reduce the noise and track the weak signal. Unlike the GNSS/INS deep coupling technology, LEO communication satellites can locate the receiver autonomously, estimate the basic position of the GNSS receiver, and provide much more aiding information, including ephemeris, navigation data, Doppler shift, and clock information assisting for the acquisition and tracking of the GNSS signal. In this contribution, we describe the new assisted acquisition and tracking scheme, with the analysis and verification of its performance by the simulation results. The paper is organized as follows: Section 2 introduces the LEO-assisted GNSS receiver structure and describes the signal acquisition and tracking method. Section 3 analyzes three types of errors from the LEO satellite assistance. Section 4 gives the simulation results of the assisted acquisition and tracking performance. A verified platform is realized in Section 5, and the GPS signal is simulated to test the performance of this structure, which is compared with an unassisted system to show the advantages. Finally, conclusions are drawn in Section 6.
# Influence Analysis of Liquefiable Interlayer on Seismic Response of Underground Station Structure
## 3 Liquefaction Distribution Characteristics  
In the numerical calculation process, the excess pore pressure ratio (EPPR) \(\mathbf{r}_{u}\) is used to represent the liquefaction degree of the soil [24], which is defined as shown in Formula (8):  
\[\mathbf{r}_{u}=1-\frac{\sigma_{\text{m}}^{\prime}}{\sigma_{\text{m0}}^{\prime}} \tag{8}\]  
where \(\sigma_{\text{m}}^{\prime}\) is the average effective stress of the element during the dynamic calculation, and \(\sigma_{\text{m0}}^{\prime}\) is the average effective stress of the element before the dynamic calculation.  
In order to reflect the distribution characteristics of liquefaction zones of liquefiable interlayer sites under different influencing factors, the EPPR clouds of the sandy interlayer after the end of seismic action in different cases are given in Figure 6. When the EPPR is greater than 0.7, the site is considered to be initially liquefied, and when the EPPR is equal to 1, the site is considered to be completely liquefied.  
From Figure 6, it can be seen that the liquefaction range of the liquefiable interlayer in each case is basically symmetrically distributed, and the soil near the side wall of the station structure does not liquefy. The reason for this is that the stiffness difference between the station structure and the liquefiable soil layer is too large, and the excess pore water pressure of the soil near the side wall cannot easily accumulate and develop. In case a, when the liquefiable interlayer is located in the upper part of the structure, partial liquefaction occurs in the middle part of the shallow sandy soil on the surface. In case d, when the relative density of the sand interlayer is 30%, almost all the soil in the liquefiable interlayer is liquefied, except for that near the side wall of the structure, and no liquefaction occurs in the liquefiable interlayer in case e, which has the highest relative density. In both case c and case g, liquefaction occurs at the bottom of the structure because the average effective stress at the bottom of the structure is much lower than that at other locations at the same depth due to the presence of the station structure, and liquefaction is more likely to be triggered by the seismic action. In both case b and case f, liquefaction only occurs at the two ends of the negative level of the structure, away from the side walls. In summary, the liquefaction of the liquefiable layer is most significant in cases d and g, which will have an important impact on the station structure and should be taken into account in the analysis.  
Figure 5: Time history and Fourier spectrum of earthquake acceleration. (**a**) Acceleration time history curve; (**b**) Fourier spectra of acceleration.
## 3 Methods  
### Canopy Cover Estimation  
#### 3.1.1 Estimating Canopy Cover from Airborne LiDAR  
The canopy cover can be estimated from discrete-return LiDAR data using various approaches based on the assumption that nadir gap fraction (i.e., the opposite of canopy cover) is equivalent to the canopy transmittance to the forest floor. The simplest method is quantifying the proportion of LiDAR pulses that were intercepted by the tree canopy. Note that the canopy elements that are much smaller than the pulse area are not considered in the estimation of canopy cover, thus resulting in underestimation. Conversely, the canopy elements with the size of less than 100% of the pulse area are also not considered, thus the canopy cover would be overestimated. The first echo cover index (FCI), as the common one of the echo-number-based methods, was used to be the proxy of canopy cover as the probability of a non-first echo is conditional on that of the first echo and thus may bias the estimation [23; 33]. To minimize the negative effect of the oblique pulses on canopy cover (the metric from nadir direction) estimation, we used the near-vertical pulses [16], i.e., the scan angle of less than 10\({}^{\circ}\).  
\[FCI=(\sum R_{canopy}^{single}+\sum R_{canopy}^{first})/(\sum R_{all}^{single }+\sum R_{all}^{first}). \tag{1}\]
## 7 Results and Discussion
### Comparative Analysis of Client Selection Strategies
#### 7.3.2 Loss Reduction over 300 Rounds  
The training loss curves across all datasets further illustrate the trade-offs among selection strategies. The accuracy-only strategy demonstrated the fastest initial loss reduction, especially in MNIST Figure 22 and EMNIST Figure 23, due to its repeated selection of clients with strong local models. However, this advantage diminished over time as the same high-performing clients were repeatedly selected without regard for battery or privacy. As these clients began to drop out or contribute less due to constraints, the model's ability to continue improving stalled, and loss reduction slowed significantly in later rounds. Random selection exhibited unstable and fluctuating loss behavior, particularly on EMNIST and CIFAR-10, due to the inconsistency in client quality and data contribution. Battery-only and privacy-only strategies maintained high loss throughout training, reflecting their limited capacity to select impactful clients. These approaches often neglected clients with high gradient contribution, resulting in slower and less effective learning. RBPS, on the other hand, achieved the most stable and consistent loss reduction across all datasets. It followed a near-optimal trajectory on MNIST and EMNIST and continued to reduce loss steadily on more complex tasks like Fashion-MNIST Figure 24 and CIFAR-10 Figure 25, where other strategies plateaued. By balancing selection across learning potential, battery cost, and privacy risk, RBPS maintained strong contributors in the loop longer, resulting in more sustained and efficient loss convergence.
# On Construction of a Campus Outdoor Air and Water Quality Monitoring System Using LoRaWAN
## 4 Experimental Results
### IDW Visualization  
The logging data are transmitted, after processing, by LoRa and uploaded to the ThingSpeak website per minute. Our real-time display system uses the OSM map (Open Street Map). It can display a variety of information, as shown in Figure 13a. Figure 13b presents the IDW map values after IDW calculation. It merges into the DataFrame of the map's grid layer and gives the corresponding density color according to the sensor value of each grid. The color rendering refers to the sensor's values classification from the source data. Because it is difficult to see the concentration diffusion using StepColorMap, this study uses LinearColorMap with gradient effect to better present the concentration diffusion. Finally, we use the Folium framework to create a Leaflet map and overlay the grid layer files of the map to complete drawing the grid's map.  
Figure 12: The Equipment of Packet Loss Rate Test.
# Exploiting Mobile Edge Computing for Enhancing Vehicular Applications in Smart Cities
## 2 Literature Review
### Architecture  
Different architectures have been recently proposed for the implementation of edge computing in vehicular environments [1, 5, 6, 31]. The authors in [5] tried to address latency in the internet of vehicles (IoV), by integrating edge computing and SDN. Edge computing is utilized to offer a highly virtualized platform that provides computation, storage, and networking services between end devices and traditional cloud computing data centers. The network architecture is divided into four layers: cloud computing layer, SDN control layer, edge computing layer and infrastructure layer.  
Similarly, the authors in [1] proposed a four-tier architecture for urban traffic management with the convergence of VANETs, 5G networks, SDNs, and mobile edge computing technologies. The authors used a paradigm of road accident rescue to validate the high efficiency of the proposed architecture.  
Three measures were proposed to improve the rescue system. These are remote video diagnosis and initial assessment, early-warning and no-go area, and green rescue lane for emergency vehicles. The proposed architecture may decrease traffic congestion and improve the ability to manage urban traffic for faster rescue. In the following, we alter the architecture provided in [1] for a generalized VEC architecture that consists of three layers: the traffic sensing, the mobile edge computing and the cloud server layer as shown in Figure 1. These layers are briefly described below: