esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### X-ray Diffraction (XRD)  
XRD is utilized to study the impact of Cu doping on the anatase to rutile phase transition of TiO\({}_{2}\) at various calcination temperatures. Figure 4a shows the XRD profiles of pure anatase TiO\({}_{2}\) and 0.5% Cu-TiO\({}_{2}\) ( calcined at 500 \({}^{\circ}\)C, 600 \({}^{\circ}\)C, 650 \({}^{\circ}\)C and 700 \({}^{\circ}\)C). XRD spectrum of rutile TiO\({}_{2}\) is also provided for reference. The spectra of undoped samples of TiO\({}_{2}\) for all the given temperatures are given in Figure S1. The anatase and rutile peak intensities are generally observed at 25.4\({}^{\circ}\) and 27.3\({}^{\circ}\), respectively, (101 and 110) [15]. These values were used to calculate the percentage of anatase and rutile of TiO\({}_{2}\) in all samples using the Spurr equation (Equation (2)). Cu-TiO\({}_{2}\) at 500 \({}^{\circ}\)C and 600 \({}^{\circ}\)C exhibited 100% of anatase while the sample at 700 \({}^{\circ}\)C showed 100% of rutile. Interestingly, calcined at 650 \({}^{\circ}\)C yielded a mixture phase constituting 55% of anatase and 45% of rutile TiO\({}_{2}\) (Figure 4b).  
The diffractogram also illustrates the increase in the intensity of the signature peaks of both anatase and rutile phases with increase in calcination temperature. The Schrerr equation (Equation (3)) is used to calculate the average crystalline size (Table 1). The decrease in the FWHM values helped in exhibiting the increase in average crystallite size from 8.83 nm for anatase TiO\({}_{2}\) to 28.8 nm for the mixed phase sample ( calcined at 650 \({}^{\circ}\)C). This is attributed to the thermally promoted crystallite growth [70]. For any photocatalytic application the need for improved visible light activity and high temperature stability are two fundamental desirable properties. In this study, the XRD results shows the presence of mixed phase at a high calcination temperature of 650 \({}^{\circ}\)C by doping a very minute fraction of Cu. This prime reason makes the sample an interesting candidate for further studies and hence, hereafter, the mixed phase sample was used for further characterizations and application. Additionally, anatase (TiO\({}_{2}\)--500 \({}^{\circ}\)C) and rutile (TiO\({}_{2}\)--700 \({}^{\circ}\)C) samples are taken as controls.  
Figure 3: Projected electronic density of states (PEDOS) plots for (**a**) undoped anatase, (**b**) Cu-doped anatase with no oxygen vacancies and (**c**) after formation of a single, charge compensating oxygen vacancy and (**d**) after formation of a second, reducing oxygen vacancy.
# Mapping with Pleiades--End-to-End Workflow
## 4 Methods
### Sensor Modeling, Parameter Optimization and Geo-Location Accuracy Assessment  
The RPC sensor model, as defined in References [40; 41], relates geographic coordinates to corresponding image coordinates (i.e., a map-to-image transformation) by two rational cubic polynomials. The RPC model consists of 80 coefficients and approximates the physical sensor model in a standardized manner. For optimizing an RPC sensor model GCPs and their related image coordinates are employed. The optimization can be formulated in object-space or in image-space [41]. We pursue an adjustment in object-space since then the solution is also dependent on the height. This would not be the case if, for instance, an affine transformation is adjusted in image-space [40; 42]. From the mathematical perspective, the parameter optimization can be formulated as a non-linear equation system. Like in Reference [41], we use [PERSON]'s method to solve the equation system and thus to optimize the sensor model.  
To assess the geo-location accuracies, we follow the protocol that we defined in Reference [48]. The 2D geo-location accuracy of initial and optimized sensor models is evaluated for each images using reference GCPs and also their 2D locations in this image (cf. Figure 2). The 3D GCPs are projected onto the 2D image using the RPC model and the deviations to the reference 2D image coordinates define the model accuracy. Those differences are described using statistical measures such as mean, standard deviation (STD) or root mean square (RMS) errors in pixels. We give those values for across and along track direction such as the absolute length of the differences. Often those statistics are given for GCPs and ICPs.  
For stereo acquisitions also the 3D geo-location accuracy can be determined. In this case, a spatial forward point intersection is performed based on the 2D image coordinates and the sensor models yielding 3D points. Those points are compared with the reference GCPs and ICPs resulting in statistics on 3D locations errors in meters (assuming that the underlying map projection is metric). Here the statistical values are given for East, North and Height direction such as for the absolute Euclidean distance (also called length).
## 4 Hydrodynamic Modeling and Analysis of the Robot
### Hydrodynamic Analysis for the Robot
The paddle in this case performed a rectangle-like spatial stroke trajectory and the cycle changed to 3.2 s. The hydrodynamic model of the robot when the paddles performed a rectangle-like spatial stroke trajectory can be derived from Equation (12) and the speed of the robot in this case is shown in Figure 14. The speed when the robot performed an ellipse-like spatial stroke trajectory was faster than when it performed a rectangle-like spatial stroke trajectory. Below, we compare the two kinds of stroke action in terms of power and efficiency. The power calculation formulas of the robot for the two stroke methods can be derived from Equation (13). The powers under different stroke methods are shown in Figure 15 which clarifies that the robot adopted a rectangle-like spatial stroke trajectory required more energy during the interaction between the paddles and the water. Calculated by Equation (15), the average powers when the robot performed the ellipse-like spatial stroke trajectory and the rectangle-like spatial stroke trajectory were \(6.52\times 10^{-3}\) W and \(5.25\times 10^{-3}\) W, respectively. It means that the paddle performing an ellipse-like spatial stroke trajectory can provide more power for the robot at the same rotational speed of the paddle. Calculated by Equation (16), the efficiencies when the robot performed the ellipse-like spatial stroke trajectory and the rectangle-like spatial stroke trajectory were 0.880 and 0.751, respectively. This means that the generated power can be better applied to the robot motion when the paddle performs an ellipse-like spatial stroke trajectory. Whether it is from the perspective of the speed or the stroke efficiency of robot, it clarifies that an ellipse-like spatial stroke trajectory can improve the moving efficiency of the robot. Figure 14: The speeds of the robot as a function of \(t\) at different situations. The solid black line is the speed of the robot when it performs an ellipse-like spatial stroke trajectory; the dashed blue line is the speed of the robot when it performs an ellipse-like spatial stroke trajectory with two intact paddles; the dotted green line is the speed of the robot when it performs a rectangle-like spatial stroke trajectory with two intact paddles.
## 2 Materials and Methods
### Data Processing  
#### 2.3.1 Fluxes Data  
The NEE, H, and LE data with 30 min time intervals were calculated from the raw data using Eddypro (7.0.7, Li-Cor Inc., Lincoln, NE, USA) software. The calculated fluxes data were quality controlled by deleting the data with a quality flag of \"2\". Abnormal flux data resulting from instrument malfunction, unfavorable meteorological conditions, and values of more than three times the variance with the average were deleted. Moreover, NEE data were deleted when friction velocity was lower than the threshold (0.22 m s\({}^{-1}\)),which was calculated using the REddyProc R package based on the moving point test method ([[https://cran.rstudio.com/web/packages/REddyProc/index.html](https://cran.rstudio.com/web/packages/REddyProc/index.html)]([https://cran.rstudio.com/web/packages/REddyProc/index.html](https://cran.rstudio.com/web/packages/REddyProc/index.html)) (accessed on 9 March 2022)). Both gap filling and NEE partitioning were conducted using the REddyProc R package, and GPP was calculated through NEE partitioning.
# In Situ Assessment of Uplink Duty Cycles for 4G and 5G Wireless Communications
## 3 Results
### Summary of Uplink Duty Cycles in 5G NR, 5G LTE, and 4G LTE  
Table 4 lists the duty cycles per application (or use case) and technology calculated from the in situ measurements recorded at Leuven, Belgium. The duty cycles for Twitch, VoLTE, and WhatsApp ranged between 4% and 22% in time. We remark that, due to the TDD in 5G NR, 5G NR used a higher fraction of the UL-allocated slots. Ping had median duty cycles of 4% and 50% for 5G NR and 4G, respectively. We remark that for 4G, the 95 th percentile of the duty cycle equaled 99%. Finally, the FTP upload and iPerf applications nearly fully used the UL-allocated time, as their median duty cycles equaled close to 92-93% for 4G and 20% for 5G NR. The mean duty cycle can be regarded as an estimate of the time-averaged duty cycle for the considered application per network technology.
## 2 Welding Area Image Acquisition and Defect Classification  
### Welding Area Image Acquisition  
In the laser welding AOI system, the welding area images were obtained using a CMOS digital camera (BASLER Basler acA2500-14 um camera and UTRON HS2514J lens), and a white annular LED light source (OPT-RI7030) with the brightness of 0-255 levels, as shown in Figure 1. Although most recent AOI systems use a CCD camera, which is more expensive than the CMOS camera [20], the CMOS camera is currently used widely in industrial inspection and possesses quite good image quality [21]. The CMOS digital camera used in this research had 5 megapixels, and it yielded a good resolution of the welding area. Therefore, the 3D shape information of the welding area could be described clearly by a two-dimensional (2D) grey image [10]. A white annular light source was set to be applied to the object at approximately 90 degrees, which made the welded part in the image clearer. When working, the white LED light beams were applied on the battery surface and then reflected into the camera. As deep learning requires diversified samples to best simulate real application scenes, we manage to build such case samples during the process of image acquisition. We collected images three times in total. We used several shooting distances, which varied approximately from 40 cm to 50 cm. Additionally, we randomly changed the brightness of the light sources from bright to dark, and the brightness level was approximately from 50 to 150. Using these approaches, the images captured each time were essentially even, and had different brightness and welding area sizes; hence, they fulfilled the requirement of the deep learning algorithm. Using a white annular light source instead of three LED lights of different colors (red, green, and blue) [7,10,13], the proposed algorithm reduced the requirement for the illumination condition; thus, it is convenient to be used in industrial environments and can reduce the dependence on LED light.
## 3 Results and Discussion
### WF Calculations  
The integral WF of a chemically inhomogeneous surface is known to be equal to the sum of the WFs of chemical elements depending on their areas on the surface; i.e., \(WF=WF_{1}S_{1}+WF_{2}S_{2}+\ldots+WF_{n}S_{n}\), where \(WF_{1}\), \(WF_{2}\) and \(WF_{n}\) are the local values of the WFs of chemical elements, and \(S_{1}\), \(S_{2}\),... \(S_{n}\) are the relative areas of the chemical elements on the surface [42]. The WF value of the Al-Mg nanostructured composite, which was mentioned to be between the WF values of Al and Mg, is induced not only by these metals but also by the contribution of the WF of the Al\({}_{3}\)Mg\({}_{2}\) and Al\({}_{12}\)Mg\({}_{17}\) intermetallic phases. The WF values of the Al\({}_{12}\)Mg\({}_{17}\) intermetallic phase and Al and Mg metals were determined using DFT calculations.  
Due to the complexity of the Al\({}_{3}\)Mg\({}_{2}\) phase lattice, consisting of a large number of atoms [3], the WF calculation of this phase using the DFT method was difficult. The WF was calculated for the Al\({}_{30}\)Mg\({}_{23}\) phase [43] with a composition close to the Al\({}_{3}\)Mg\({}_{2}\) phase. The atomic structures of these phases were taken from open databases [44]. The WF calculations  
Figure 2: SEM images (BSE mode) of the Al-Mg nanostructured composite cross-section at different magnifications and element distribution along the line shown at the top of the panel in the form of a relative intensity plot in relation to the scanning distance of the Mg and Al, marked by green and red, respectively.  
of the Al, Mg and intermetallic phases were carried out for low-index planes (001), (101) and (111), corresponding to the _fcc_ lattice.  
The results of the WF calculations are summarized in Table 3. The lowest WF of Mg and the highest WF of Al were in agreement with the WF values of Al and Mg presented elsewhere [41]. The calculated WF values for the Al12 Mg17 and Al30 Mg23 phases were found to be in between those of Al and Mg (Table 3). The integral WF of the Al-Mg sample was calculated considering the relative weight concentration and the smallest/largest WF values (see Table 3) of every existing phase on its surface. In the case of the smallest WF values, the integral WF was 4.0 eV, while for the largest WF values, the integral WF was 4.1 eV. This result was found to correlate with the experimentally measured WF of the Al-Mg sample.
## 3 Results
### Risk Exposure Study  
Based on the evidence of the monitoring activity, the evaluation of risk exposure was performed on the following pesticides, in the corresponding raw materials: pirimiphos-methyl in pasta, bread, and breakfast cereals; cypermethrin, cyfluthrin, deltamethrin, fenvalerate, and phenothrin in pasta, chlorpyrifos in bread. Experimental results, obtained in terms of mg die\({}^{-1}\), NOAEL, and ADI percentages (Tables S1-S3 of Supplementary Data), demonstrated that the estimated risk exposure related to pesticide residues in cereal products such as bread, pasta, and breakfast cereals (evaluated under a high exposure scenario) can be considered very low. Indeed, the percentages of ADI and NOAEL never exceeded values of 0.76% and 0.0086%, respectively. Chlorpyrifos was the pesticide with the highest NOAEL percentage deriving from bread consumption (0.0086% in male adolescents). Pirimiphos-methyl was the most frequently detected pesticide, at the uppermost concentration. Therefore, the highest values of risk exposure deriving from pasta and bread consumption, in terms of ADI percentage, were obtained for this OPP residue. In particular, the highest percentages for bread and pasta consumption were obtained for male adolescents, with values corresponding to 0.76% and 0.64%, respectively. Similar considerations are possible for NOAEL percentages, but referring only to the consumption of pasta (0.0064%). Moreover, pirimiphos-methyl was the only pesticide detected in more than one type of raw material (soft and durum wheat, and oats). Therefore, the risk exposure was also calculated taking into consideration the overall consumption of bread, pasta, and breakfast cereals, within the same day. In this case, the NOAEL and ADI percentages resulted equal to 0.014% and 1.41%, respectively, confirming, also in this case, a very low level of associated risk.  
The risk exposure due to some pyrethroid pesticides, such as cyfluthrin, deltamethrin, fenvalerate, and phenothrin, associated with the consumption of pasta, resulted very low, when not negligible. Indeed, NOAEL and ADI percentages of 0.00018% and 0.041% were observed in male adolescents for fenvalerate and deltamethrin, respectively. Slightly higher values were obtained for cypermethrin, with the highest ADI percentage calculated for male adolescents, corresponding to 0.37%, and NOAEL percentages lower than 0.0002%.
## 3 Results and Discussion
### Impact of ECAP Processing Factors
#### 3.2.2 Vicker's Microhardness
Processing of ZK 30 alloy through four passes of route Bc through a die with internal channel angle of 90\({}^{\circ}\) and 120\({}^{\circ}\) experienced a significant improvement in the HV values by 80.8% and 86.5%, respectively ([PERSON] et al., 2018; [PERSON] et al., 2018). From the [PERSON]'s microhardness findings, it can be concluded that ECAP processing of Mg and its alloys exhibited a notable improvement in the HV values, which can be attributed to the significant grain refinement. LR, SVR, and GPR were employed to simulate and examine the microhardness parameters of the Mg alloy. As per Figure 7 and Table 4, the predicted values for both the training and testing sets of microhardness closely matched the outcomes obtained from experiments. The reported findings were acquired by employing the optimization techniques of the SVM and GPR modeling to meet the needs of reaching modeling with proven reliability that must achieve respectable ratings on both sets of data for training and testing. The best-fit models for the training dataset exhibited R\({}^{2}\) values between 0.88 and 0.99, whereas the scores for the testing dataset were between 0.71 and 0
# Optimization of Machining Parameters for Corner Accuracy Improvement for WEDM Processing
## 3 Experiment and Discussion
### Effect of Parameter on Corner Error
#### 3.1.5 Effect of Wire Tension (WT) on Corner Error  
The \(p\)-value for the WT term was less than 0.0001 and the F-value was large which indicated that this parameter had a highly significant effect on the response. In addition, the second order effect of WT\({}^{2}\) was significant for the 30\({}^{\circ}\) and 60\({}^{\circ}\) corners. Table 10 shows the experimental results of corner machining for different wire tension (WT) parameter settings. It can be seen from Table 7 that when the wire tension (WT) increased from 10 to 14, the corner error decreased for the 30\({}^{\circ}\), 60\({}^{\circ}\), and 90\({}^{\circ}\) corners. However, the machining speed remained almost the same without any significant changes for the different wire tension parameter settings. This proved that adjustment of the wire tension (WT) parameter has little effect on the machining speed.
# M-Emu: A Platform for Multicast Emulation
###### Abstract  
Network layer multicast research is an important field of network research that requires simulators or emulators to support Software-Defined Networking (SDN) as well as to provide a specific structure at the network layer to facilitate packet forwarding, such as a multicast tree. The existing emulation platforms cannot effectively support the emulation of certain key multicast technologies, such as the Grafting Point (GP)-selection method and Rendezvous Point (RP)-selection method, for the following reasons: First, the programmable data plane of the existing emulation platform has many defects, such as the inability to process packet scheduling tasks, the prohibition of dynamic memory allocation and loops with unknown iteration counts, which make it difficult to deploy complex multicast protocols and algorithms. Secondly, at present, no emulation platform integrates network layer multicast emulation functions. As a result, users need to develop the multicast tree construction and maintenance mechanism in advance, which makes experiments laborious. To solve the above problems, based on NS4, we designed a multicast emulation platform, M-Emu. M-Emu presents a Service-Forwarding Architecture, which enables the data plane to deploy arbitrary complex protocols and algorithms. Based on the Service-Forwarding Architecture, M-Emu integrates a Multicast-Emulation Framework, which has a complete multicast tree construction and maintenance mechanism. We explain in detail how the various parts of M-Emu cooperate to complete the multicast emulation with an example and prove that M-Emu is efficient in CPU and memory consumption, etc., through a large number of experiments.  
Keywords:multicast; emulator; efficient; framework; interfaces +
Footnote †: journal:  
_Acticle_  
M-Emu: A Platform for Multicast Emulation
# One-Dimensional Consolidation Properties of Soft Clay under Multi-Stage Loading
## 4 Modified Terzaghi One-Dimensional Consolidation Equation  
[PERSON]'s one-dimensional consolidation theory assumes a constant coefficient of consolidation (\(C_{\text{p}}\)). However, when the foundation is subjected to multi-stage loading surcharge preloading, the coefficient of consolidation decreases with the application of various loads. If the traditional [PERSON]'s consolidation equation is used for consolidation calculations, the calculated value will significantly deviate from the actual situation. Therefore, the traditional [PERSON] one-dimensional consolidation equation needs to be modified based on the consolidation coefficient obtained from experiments. This paper presents a new method suitable for consolidation calculations using the multi-stage preloading method. The traditional [PERSON] one-dimensional consolidation equation can be solved as follows [31]:  
\[\left\{\begin{array}{l}U_{t} =1-\frac{8}{\pi^{2}}\sum\limits_{m=1}^{\infty}\frac{1}{n^{2}}exp(- \frac{m^{2}\pi^{2}}{4}\frac{C_{\text{p}}t}{4})\\ S_{\text{d}} =S_{\text{c}}U_{t}\end{array}\right. \tag{14}\]  
Figure 5: Variation in \(U_{t}\) with \(P\) for multi-stage loading.  
Substituting Equation (12) into Equation (15), the solution of the Terzaghi one-dimensional consolidation equation is modified:  
\[\left\{\begin{array}{l}U_{t}=1-\frac{8}{\pi^{2}}\sum\limits_{m=-1}^{\infty} \frac{1}{m^{2}}\textit{exp}\left(-\frac{m^{2}\pi^{2}C_{c}t}{4H^{2}}\right)\\ C_{v}=\frac{2.33(1+\epsilon_{0})k_{0}}{T_{v}c_{c}}\sigma^{\prime}e^{-\alpha \sigma^{\prime}}\\ S_{t}=S_{c}U_{t}\end{array}\right. \tag{15}\]  
where \(H\) is the maximum drainage distance. \(U_{t}\) is the degree of soil consolidation, \(S_{c}\) is the settlement at time \(t\), and \(S_{c}\) is the final settlement. The modified Terzaghi one-dimensional consolidation equation takes into account the nonlinear effect of soil consolidation and considers the consolidation coefficient, \(C_{v}\), as the variable of stress level. The compensates for the deficiency in the traditional Terzaghi one-dimensional consolidation equation \(C_{v}\) constant. Therefore, the modified Terzaghi one-dimensional consolidation equation better reflects the consolidation behavior of soil under multi-stage loading.