esa-sceva/satcom-chunk-collection huggingface dataset:
# Metal Release and Cell Viability of 316L Stainless Steel Sputter-Coated with N-Doped a-C:H Coatings
## 3 Results and Discussion  
### As-Deposited Characterization  
To complement the as-deposited characterization of the sputtered a-C:H:N coatings previously reported [13], a new summary can be found in Table 1 for the coatings in the study.  
Nitrogen quantification by EDS analysis revealed a doping range between 5 and 10 at.%, confirming previous XPS results (Table 1). The N content in the films increased with the reactive N\({}_{2}\) flow, associated with a decrease in hydrogen incorporation (from \(\sim\)33 to 25 at.%), as quantified by Elastic Recoil Detection Analysis (ERDA) and Rutherford Backscattering Spectrometry (RBS). Moreover, both the previous XPS and Raman spectroscopy analysis corroborated a progressive graphitization of the C-based matrix upon doping, confirming that nitrogen promotes the formation of C=N bonds and enhances the total sp\({}^{2}\) hybridization of carbon in the sputtered a-C:H:N coatings.  
Representative cross-section and surface SEM morphologies of the as-deposited undoped and doped coatings can be seen in Figure 1. A well-defined columnar layer with a thickness of approximately 360 nm characterizes the Ti-based adhesion-promoting interlayer, just below a contrasting thicker (800-900 nm) and dense top external layer (Table 1). Thus, doping with N does not seem to change the featureless morphology of the undoped a-C:H layer by using the selected sputtering conditions of this study, as shown in Figure 1b for the coating with the highest N content (CH_9N). Nevertheless, the typical surface conical defects [26] become rare, allowing the observation of a more \"scratched\"/\"groove\"-like morphology of the coating that followed the imperfect surface mechanical polishing of the SS surface. Even so, surface roughness was not significantly affected by the N doping effect (Ra between \(\sim\)2.9 and 3.4 nm), accompanied by a decrease in the WCA from \(\sim\)81 to 73\({}^{\circ}\)[13] (Figure 2a).  
The EDS cross-section elemental distribution maps (Figure 1) confirm the architecture of the coatings under study. Immediately above the Si substrate, a strong Ti K\(\alpha\) signal, originated from the interlayer, was detected, followed by a wide carbon band due to the top layer. While carbon seems to be well distributed through the coatings' thickness, the nitrogen doping element appears to be more concentrated in the adhesion layer (Figure 1b). This nonhomogeneous distribution of nitrogen during film formation was expected. In fact, titanium has a higher affinity for nitrogen in comparison to carbon [\(\Delta\)H\({}^{\text{o}}\)\({}_{\text{f}}\) (TiN) = \(-\)80.4 kcal/mol, \(\Delta\)H\({}^{\text{o}}\)\({}_{\text{f}}\) (TiC) = \(-\)57 kcal/mol] [27]. Thus, when the coatings are deposited under an N\({}_{2}\)-containing reactive atmosphere, preferential nitrogen interdiffusion can occur from the growing top C-based layer to the presputtered interlayer.
## 2 Materials and Methods
### System Design
#### 2.2.6 IoT Platform  
The IoTP allowed the operating status and feeding data from multiple production lines, units, and farms to be gathered from the ESFs and enabled terminal devices (smart phone and computers) to browse these data remotely. The IoTP ran on the Alibaba eccs.n4.small cloud server, which had a single-core VCPU, 2 GB RAM, 100 GB hard disk, and 0.5 Gb/s network bandwidth.  
The IoTP could be divided into three parts: receiving, data, and display layers. The receiving layer was responsible for collecting and parsing the messages uploaded by the central controller, as well as verifying the legitimacy of the data in order to prevent malicious attacks. The data layer stored the ESF operating status and feeding data provided an interface for the display layer to query the data, and also made scientific and appropriate suggestions according to the feeding situation of the sows. The display layer used visualization to enable users to view real-time and historical data, including information relating to the feeding situation, feeding scheme, and the ESF operating status.  
The receiving layer was constructed using the Spring Boot backend framework and Java programming language, and data were stored in a MySQL database. The data-browser software in the display layer was built using the Vue frontend framework and JavaScript programming language. Coupling was decreased and scalability advanced because of the separation of the frontend and backend. Figure 7 shows the layered architecture of the IoTP as well as the data-browser software viewed on a smart phone.
## 4 Discussion
There are only a few studies that have investigated the effects of tumor therapeutic irradiation on the bond strength and clinical outcomes of an adhesively attached composite restoration. Their results vary widely and are partly even contradictory; some concluded that irradiation does not impair the adhesive bond [35; 36; 78], while other studies showed reduced adhesive bond strength values [34; 37; 57; 79]. [PERSON] et al. [35] investigated in vivo irradiated teeth of tumor patients, i.e., those that had been exposed to the intraoral conditions during and after irradiation; which might be hyposalivation, reduced saliva pH, and changed diet and oral hygiene. This is consistent with the fact that radiation caries has a multifactorial origin [6]. On the other hand, however, the study design allowed the analysis of only a few samples from a small number of patients for whom the clinical circumstances of radiation therapy were inherently individual: The samples were exposed to different radiation doses (60-70 Gy total dose), and the study did not indicate how each individual sample was affected by the direct radiation field [35]. In the study accomplished by [PERSON] et al. 2016 [78], the testing procedures were carried out under in vitro conditions, but the specimens were irradiated with a single dose, which is not consistent with the clinical procedure [13; 39; 40]. Another study that examined universal adhesives on irradiated dentin, as well, was the study of [PERSON] in 2020 [79]. In this publication, he observed a significantly decreased bond strength of three different universal adhesives when applied in a single layer. The universal adhesives were only used in self-etch mode, but not in etch-and-rinse mode, just as in our study. This corresponds to our findings for the material Xeno(r) Select, which revealed a significantly reduced bond strength in self-etch mode after irradiation. Overall, it strengthens our thesis that these impacts appear to be material-dependent [79].
# First Results from Sentinel-1A InSAR over Australia: Application to the Perth Basin
## 5 Results  
### Interferograms  
In total, we produced 11 consecutive Sentinel-1A interferograms and 21 consecutive TerraSAR-X interferograms. We evaluate the performance of the stratified atmospheric correction by comparing the interferogram standard deviation before and after application of ERA-I [60; 76]. Before comparison, non-ERA-I-corrected interferograms were de-ramped using the method described in Section 4.3. The reduction in interferogram standard deviation between the two datasets is comparable, with a 14% reduction for Sentinel-1A and 13% reduction for TerraSAR-X (Figure 3A,B).  
In a second test, we compared the correlation between pixel displacement and pixel elevation before and after correction using the coefficient of determination \(R^{2}\) metric [60; 63]. Larger values of \(R^{2}\) are observed for the non-corrected datasets (Figure 3C,D), suggesting that application of ERA-I has reduced the effects of stratified atmospheric noise and the correlation between displacements and topography. However, we do not expect this method to correct all atmospheric signals, and in both datasets, we observe other phase patterns including quasi-systematic banding, which has been attributed to gravity waves in the atmosphere and observed in other coastal locations [76].  
Due to its longer sensor wavelength, Sentinel-1A interferograms are \(\sim\)20% more coherent than TerraSAR-X interferograms, but overall, both datasets are coherent over much of the study area (Figure 4A,B). Incoherence in both datasets is greatest in agricultural/bushland regions northeast of the Perth central business district close to the Swan Valley and southeast of Fremantle (Figure 2). Incoherence in the Sentinel-1A dataset also occurs over forests east of the Darling Scarp.
# Yield Prediction of Winter Wheat at Different Growth Stages Based on Machine Learning
## 3 Results
### Pixel-Level Spatialization of Yield  
Based on the aforementioned research, the stability and superiority of the ensemble voting model were confirmed, particularly in predicting winter wheat yields during the heading stage. Therefore, this study adopted the ensemble voting model, utilizing various feature variables during the wheat heading stage as inputs, to predict the winter wheat yield in Henan Province for the year 2021. By employing the yield spatialization model, the predicted results at the county level were mapped to a more refined pixel scale. The enhancement in spatial resolution allowed for more accurate capture of the geographical variations in winter wheat productivity, unveiling local growth characteristics and yield fluctuations within specific regions.  
Figure 10 demonstrates the spatial distribution of winter wheat yield at the pixel scale in Henan Province. It can be observed from the figure that the high-yielding areas of winter wheat in Henan Province are mainly concentrated in the eastern region, and the yield in the western region is relatively low. In addition, the planting structure of winter wheat is more fragmented in the western region. The more concentrated distribution of  
Figure 8: Accuracy of single growth period models. Subfigures comprise a (**a**) heatmap of \(\mathbb{R}^{2}\) for the validation set, (**b**) heatmap of \(\mathbb{R}^{2}\) for the test set, (**c**) heatmap of RMSE for the validation set, (**d**) heatmap of RMSE for the test set, (**e**) heatmap of MAE for the validation set, and (**f**) heatmap of MAE for the test set.  
Figure 9: Scatter plots of ensemble voting’s yield predictions during the heading stage. Subfigures comprise a (**a**) scatter plot for the validation set and (**b**) scatter plot for the test set.  
high-yielding plots highlights the existence of more favorable conditions for winter wheat cultivation in the eastern region. The results of this spatialization of yields help us to more comprehensively understand the geographic variability of winter wheat yields in Henan Province and provide visual support for agricultural decision-making and management.
## 3 Incorporation of DANNC and Collision-Obstacle Avoidance for a Group of UUVs
### Obstacle Avoidance for a Group of Multiple UUVs  
For operations in hazardous environments, we define two environment zones (see Figure 4). Zone 1 is an environment with the obstacles, where the group of UUVs will change a trajectory to avoid obstacles. Zone 2 is an environment with a narrow area, where the group of UUVs will change the desired size of formation to be able to overcome that area.  
**Assumption 4**.: _For zone 1, the static obstacles or dynamic obstacles are identified by the vision system or the sonar sensors. That means the distance from the obstacles to the UUV is predefined and updated continuously._  
A smooth curve can be defined using the control point \(A0,A1,A2,A3\), as shown in Figure 4. Note that the Bezier curve does not pass through the intermediate points \(A2\), \(A3\), however, it passes through the points \(A0\) and \(A3\). The implementation steps are outlined as Algorithm 1. For more details, the readers can refer to the previous works [29].  
**Assumption 5**.: _For zone 2, the position and distance \(h\) of narrow area are predefined through sensors._  
By defining a desired formation as time-variant function, we have  
\[\delta_{ij}=\delta_{ij}\left(t\right)=\delta_{j}(t)-\delta_{i}(t) \tag{29}\]  
Figure 4: Partition of two regions of a UUV for obstacle avoidance.  
The idea here is to compare the distance from the UUV leader to the narrow area. When the UUV leader is in a narrow area, a group of UUVs will compare a desired formation \(\delta_{ij}(t)\) with the distance from the narrow area \(h\). It will have to satisfy the following conditions  
\[\delta_{ij}(t)=\begin{cases}\delta_{ij}(t)&\text{if }\delta_{ij}(t)<h-\Delta\\ h-\Delta&\text{if }\delta_{ij}(t)\geq h-\Delta\end{cases} \tag{30}\]  
where \(\Delta\) is a given value, which depends on the size of the UUV to ensure that the group of UUV moves through a narrow area without collision, and \(h\) is the smallest distance in the narrow area.
## 6 Performance Analysis by Monte Carlo Simulation
### Simulation Results  
The outcomes of the proposed methods for estimating the point and interval parameters are displayed in Tables 3 and 4. They provide the results and some interesting information. The following comments can be made:  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & & & \multicolumn{2}{c}{**ML**} & & \multicolumn{2}{c}{**MPS**} & \multicolumn{2}{c}{**Bayesian**} \\ \hline \(\varphi\) & \(\delta\) & \(n\) & & **Bias** & **MSE** & **LACI** & **Bias** & **MSE** & **LACI** & **Bias** & **MSE** & **LCCI** \\ \hline \multirow{8}{*}{0.5} & \multirow{3}{*}{30} & \(\varphi\) & 0.2473 & 0.0782 & 0.5112 & 0.1569 & 0.0369 & 0.4355 & 0.0449 &  
As the sample size increases, the estimates become increasingly precise, indicating that they are asymptotically unbiased.  
When the MSE value is close to zero, the parameter estimates are from the best unbiased estimator.  
The MSE decreases in each estimate as the sample size increases, indicating consistency among the different estimates.  
At true value \(\varphi=0.5\) and as the value of \(\delta\) increases from 2 to 4, the MSE of both estimates, based on three different techniques, decreases.  
As the true value of \(\varphi\) increases, the MSE of both estimates, based on three different techniques, increases at the same true value of \(\delta\).  
The MSE and length of the CI for the MPS estimates is smaller than the ML estimates for all true parameter values.  
Both parameter estimates have the largest MSE for the three proposed methods at the true value of \(\varphi=\delta=4\), except a few cases.  
In the majority of situations, we conclude that the MPS estimates are preferable compared to the ML estimates due to their precision measures being the smallest.  
As \(n\) grows larger, the length of the CI (ML: LACI; MPS: LACI; Bayesian: LCCI) for the estimates decreases, suggesting that the CI is the shortest.  
For all true parameter values, the LACI of the MPS estimate is lower than the LACI of the ML estimates.  
The length of the CI for both estimates obtains its largest value, based on the three suggested methods, as the true values of the parameters increase.
# Multi-Tier Cellular Handover with Multi-Access Edge Computing and Deep Learning
## 2 Preliminaries
### Far and Aggregated MEC  
While the traditional cloud computing services reside in the core, multi-access edge computing (MEC) enables decentralized cloud-like services close to the edge of a network, allowing for lower latencies and higher throughputs for users [9]. Figure 1 highlights their key differences. This paper focuses on the two edge data center architectures, (i) far edge  
\begin{table}
\begin{tabular}{c l l} \hline \hline
**Event** & **Description** & \\ \hline A1 & Serving cell becomes better than a threshold \\ A2 & Serving becomes worse than a threshold \\ A3 & Neighbor becomes offset better than serving \\ A4 & Neighbor becomes better than a threshold \\ A5 & Serving cell becomes worse than threshold 1, and neighboring cell becomes \\ A6 & Neighbor become offset better than secondary cell \\ \hline \hline \end{tabular}
\end{table}
Table 1: Handover trigger events for intra-RAT handover.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Event** & **Parameter** & **Minimum** & **Maximum** \\ \hline A1, A2, A4, A5 & RSRP threshold & \(-156\) dBm & \(-31\) dBm \\ All & Hysteresis & \(0\) dB & \(15\) dB \\ A3, A6 & Offset & \(-15\) dB & \(+15\) dB \\ \hline \hline \end{tabular}
\end{table}
Table 2: Event parameter ranges.  
and (ii) aggregated edge, to achieve a handover latency that approaches the user plane eMBB and URLLC latency targets of less than 4 ms and 1 ms, respectively [10].
## 3 Analysis of Rotor Parameter Influence on Electromagnetic and Mechanical Stress Performance  
### Pre-Treatment of Finite-Element Model  
Ansys software ([[https://www.ansys.com/](https://www.ansys.com/)]([https://www.ansys.com/](https://www.ansys.com/))) is a large-scale general finite-element analysis (FEA) software developed by American Ansys Company, which can carry out electromagnetic, mechanical strength, thermal, acoustic, and fluid research. The electromagnetic and stress calculations in this paper are based on Ansys software. In this section, the 2D finite-element model (2D-FEM) was built in Ansys Workbench, as shown in Figure 2. The calculation of rotor stress is performed in the tool kit for static structures in the Ansys workbench, and the analysis of electromagnetic characteristics under no load and rated load is performed in the Maxwell module. During the analysis of the influence of rotor design parameters on motor performance, we kept the other parameters unchanged and changed only one parameter.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Rated power (kW) & 140 \\ Rated speed (rpm) & 18,000 \\ Rated voltage (V) & 380 \\ Stator outer diameter (mm) & 170 \\ Stator inner diameter (mm) & 110 \\ Air-gap length (mm) & 0.5 \\ Axial length (mm) & 200 \\ Surface speed (m/s) & 103 \\ Slot fill factor & 75\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main stator parameters of the HSPMM.  
Figure 1: The model of the HSPMM. (**a**) Integral structure and (**b**) rotor structure of the IPM motor.  
Figure 2: The model of electromagnetic optimization.  
The parametric model of the rotor structure is shown in Figure 3. The PM can be divided into two segments by one stiffener or into three segments by two stiffeners. The distance between the PM slots and the rotor outer diameter is called bridge thickness, THg. Stiffener thickness, THg, is the gap between three-segment PMs, that is, the sum of the thickness of two stiffeners. The rib width, WR, indicates the distance between the two PM slots. Combined with the preliminary analysis and design experience, the initial design rotor parameters are presented in Table 2.
## 4 Experimental Results and Discussion
### Dataset and Metrics  
The public benchmark dataset we used in this section is IARPA Multi-View Stereo 3D Mapping Challenge [29], which contains 50 panchromatic satellite images captured by WorldView-3 satellite. The acquisition time of these satellite images is from November 2014 to January 2016. The nadir resolution of the satellite images is 30 cm, and these images cover an area of 100 km\({}^{2}\) near San Fernando, Argentina. The IARPA MVS3 DM dataset also provides airborne lidar data with 20 cm nadir resolution that can be used as ground truth DSM.  
The metrics of the experimental results evaluation in this paper is followed the previous quantitative evaluation [1; 2; 7; 18], which contains completeness (CP), root-mean-square error (RMSE) and median error (ME). All these three metrics require aligning the reconstructed DSM with the ground truth DSM and comparing them with pixel-wise. Among the three metrics, CP stands for the percentage of altitude errors less than 1m in a reconstructed 3D point cloud, RMSE represents the root-mean-square error of the altitude, and ME is defined as the median altitude error.
## 2 Flight Altitude Feature Extraction Method
### Trend Filtering  
The altitude signal is as follows:  
\[X=\Big{\{}x_{1},x_{2},\cdots,x_{i},\cdots,x_{n_{lp}}\Big{\}}=\Big{\{}x(1),x(2), \cdots,x(i),\cdots,x(n_{hp})\Big{\}}, \tag{1}\]  
where \(x_{i}\) with \(x_{i}=x(i)\) is the \(i\)th sample point in \(X\), \(n_{hp}\) is the length of \(X\), and the subscript \"\(hp\)\" means the pressure height.  
The process of trend filtering involves calculating the average of the \(k\)-adjacent data, which will replace the middle element of these \(k\) data. To signal \(X\), assuming each interval composed of \(k\)-adjacent data is almost stable, if we use the mean to replace the middle element of the interval, then the data jumps in the interval will be filtered out. \(k\) is called the width of the sliding window.  
The key step of trend filtering is to set a sliding window width \(k\). If the minimum time for an aircraft to rapidly dive or jump is \(\Delta t_{\min}\) and the sampling frequency for the flight parameters is \(f_{s}\), the sliding window width for trend extraction is set to \(k=\big{\lfloor}f_{s}\Delta t_{\min}\big{\rfloor}\), where the operator \(\lfloor\cdot\rfloor\) represents rounding down. Variable \(k\) denotes the fewest sampling points taken by a rapid climb or descent action and \(\Delta t_{\min}\) is an experience value determined by the aircraft's performance. After trend filtering, we can obtain the trend of \(X\) as:  
\[\hat{X}=\Big{\{}\hat{x}_{1},\hat{x}_{2},\cdots,\hat{x}_{i},\cdots,\hat{x}_{n_{ lp}}\Big{\}}=\Big{\{}\hat{x}(1),\hat{x}(2),\cdots,\hat{x}(i),\cdots,\hat{x}(n_{ hp})\Big{\}}, \tag{2}\]  
where \(\hat{x}_{i}=\hat{x}(i)\).  
Trend filtering is equivalent to lowpass filtering to \(X\) with \(2f_{s}/k\), and trend extraction does not compress the total amount of data. For data with a sampling frequency hundreds of times more than the trend frequency, the effect of the trend filtering method is more pronounced than that of the lowpass filter when extracting trend terms, such as the Butterworth filter or Chebyshev filter.
# A Pulse Shaping Based Optical Transmission System of 128 QAM for DWDM with N \(\times\) 904 Gbps
## 5 Conclusions  
We simulated and demonstrated a pulse shaping-based optical transmission of 128 QAM for DWDM system with N \(\times\) 904 Gbps. Gaussian optical filtering is used to produce a photonic carrier. The spectrogram and constellations were obtained by the professional optical communication simulation software. The performances of the transmission system influenced by OSNR and the bandwidth of  
Figure 12: The relation curve between EVM and transmission rates with constellation-diagrams.  
GOF were analyzed. The relationships between the bandwidth of GOF and BER, OSNR and BER, the bandwidth of GOF and EVM, OSNR and EVM, bit rate and BER, and bit rate and EVM were shown via the curve diagrams. By analytical calculation, theoretical verification, and the comprehensive algorithm compensation, the ultra-speed DWDM transmission system with the wavelength spacing of 87.5 GHz and the single-channel bit rate of 904 Gbps was realized. Meanwhile, the multi-carrier photonic signals were efficiently transmitted over 80 km in a standard single-mode fiber with the optical spectral efficiency of 10.33 bps/Hz.  
In the future study, we will further optimize the configuration of parameters to improve the transmission rate, and to decrease BER and EVM. We will also try to utilize the methods of optical Nyquist-samples to ameliorate the optical spectral efficiency, and then improve the entire performance of the transmission system.  
[PERSON] wrote the manuscript. [PERSON] reviewed the paper prior to submission. [PERSON] provided the concept and simulation design of the study. [PERSON] partially revised the paper. All authors discussed the results, analyzed the data and commented on the manuscript.  
This work is supported by the Science and Technology Plan of Guangzhou in 2017 (Grant No. 201707010173) and the Scientific Research Fund of Guangdong Polytechnic of Science and Technology in 2018 (Grant No. XJPY2018002).  
The authors declare no conflict of interest.