esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Optical Analysis  
Figure 7 shows the PL spectrum for the Mg-doped GaN films (_black line_), which was decomposed into four components. The \(a\) emission peak consists of a shoulder located at 3.44 eV (360.83 nm-UV region), which corresponds to the band-to-band transition for the GaN hexagonal. In this same spectrum, the \(b\) peak has a predominant emission in a blue-violet band, with a range from 2.80 to 3.16 eV (443.03-392.56 nm). This emission is typical of Mg-doped GaN films and is related to the recombination of the deep donors of gallium vacancies occupied by magnesium atoms (MgGa) with acceptors MgGa [17; 24; 25; 26]. The \(c\) emission energy is located in a range from 2.51 to 2.6 eV (494.22-477.11 nm) and presents a green luminescence (GL). This increases with excitation intensity in deep defects as Mg-O binds and also agrees with EDS elemental composition (Figure 3d). These defects might be native and also be related to the excess of gallium [24]. The point defects for thin films, such as in interstitial defects, vacancies, and nano-crystallites, could widen the peaks, which agrees with the X-ray diffraction patterns of Figure 2. In this case, oxygen non-intentional impurities could occupy lattice sites into the GaN structure [27]. An energy emission located at 2.26 eV (548.67 nm) (_d_ peak) is related to yellow luminescence (YL), where Ga vacancies (VGa) and substitutional atoms of oxygen could be responsible for yellow luminescence [17; 19]. On the other hand, for the spectrum of Mg-doped GaN films, the \(e\) peak presents a high red luminescence emission band with a range from 1.7 to 1.8 eV (729.41-688.88 nm), which could be due to the high incorporation of Mg in GaN films [24; 28]. Figure 8 shows the PL spectrum for the Zn-doped films (red line), which was decomposed into four components. The \(a\) emission peak is in a range from 2.89 to 3.0 eV (429.23-413.50 nm), where this blue luminescence (BL) might be related to excitons bound to the Zn acceptors [19; 24]. For Zn-doped GaN films, luminescence emissions, such as the red, yellow, and green bands, are less known. However, [PERSON] et al., demonstrated that the Zn doping introduces four acceptor-like centers in the GaN, which produced broad peaks of green, yellow, and red luminescence, in addition to the blue band [29]. The Zn-doped GaN films spectrum showed an emission energy at 2.6 eV (477.11 nm) (_b_ peak) for the green luminescence (GL), while the \(c\) peak had an emission range from 1.8 to 2.2 eV (689.16-563.86 nm) for the yellow luminescence (YL). On the other hand, the \(d\) peak had an emission energy of 1.84 eV (674.18 nm) for the red luminescence (RL), which might be also produced by oxygen non-intentional impurities. These values would be an indicator of the obtaining of p-type GaN, as [PERSON] mentions.  
Figure 7: Photoluminescence spectrum for the Mg-doped GaN films deposited by radio-frequency magnetron sputtering.
# Semi-Coprime Array with Staggered Beam-Steering of Sub-Arrays
## 2 SCA: Basic and Proposed Forms
### Sca  
SCA, proposed in [33], is the union of three SAs called hereafter SA1, SA2 and SA3 having \(PM\), \(PN\) and \(Q\) elements with spacing \(QN\lambda/2\), \(QM\lambda/2\) and \(\lambda/2\), respectively. Figure 1 depicts a typical set of SAs of an SCA. Array factor (\(AF\)) of the \(i\)th SA of an SCA can be expressed as:  
\[AF_{i}(\theta)=\sum_{n}w_{n,i}e^{i(n-1)kd_{i}\sin\theta},i=1,2,3 \tag{1}\]  
where \(\theta\) is the angle with respect to the broadside direction of the array, \(d_{1}=QN\lambda/2\), \(d_{2}=QM\lambda/2\) and \(d_{3}=\lambda/2\), and \(w_{n,i}\) is the weight of the \(n\)th element of the \(i\)th SA. Here, \(P\), \(Q\), \(M\) and \(N\) are positive integers and \(M\) and \(N\) are coprime. It may be noted that the total number of elements is \(L\)=\(PM+PN+Q-1-P\).  
In the basic SCA, the three SAs are employed with uniform excitation amplitudes or weights, to generate beam patterns, steered towards angle \(\theta_{0}\). It should be noted that all the SAs are beam-steered towards the same angle \(\theta_{0}\). To unify the three patterns, in [33], a minimum processor was employed; therefore, the overall beam pattern of an SCA can be expressed as:  
\[B(\theta)=\min\Bigl{(}|AF_{1}(\theta)|^{2},|AF_{2}(\theta)|^{2},|AF_{3}( \theta)|^{2}\Bigr{)} \tag{2}\]
## 1 Introduction
In this case, purely relying on a large number of vibration monitoring points is not a cost-effective solution to realize global vibration impact assessment. Therefore, how to conduct real-time vibration intensity assessment over the entire construction site rather than at several discrete monitoring points remains challenging. In this study, we combine the empirical formulas with the localization of vibratory pile driving to address this issue. We draw upon the concept and methodology proposed by [PERSON] and [PERSON] [24], who attempted to localize the unknown impact source on construction sites by using the time of arrival (ToA) of impact waves recorded by transducers and the coordinates of these transducers. However, whether this localization method derived from the simulations of an impact source can be extended to other vibration sources remains unknown at present. For example, an impact source and a vibratory pile driving source correspond to different wave generation mechanisms and wave propagation characteristics. Moreover, ground wave propagations and source localization performances are affected by multiple influencing factors, which have not been considered in the literature [24]. Therefore, in this study, we investigate the time-based localization method to localize a vibratory pile driving source on construction sites and subsequently realize a global prediction of vibration intensities over a broad area. The transient finite element analysis was performed to simulate the wave propagation from one vibratory sheet pile driving source to the far-field in an infinite half-space soil domain, on which the time-based localization method for vibratory driving source was based. The effects of the layout of monitoring points, pile toe depths, ground conditions, vibratory driving frequencies,and pile-soil friction coefficients on the localization performance were systematically investigated through numerical simulations. Field validation tests were performed on a construction site with vibratory sheet pile driving work under construction. Finally, a site-specific empirical formula was adopted to predict vibration intensities at different distances from the localized vibratory driving source.
# Energy Demand and Supply Simultaneous Optimization to Design a Nearly Zero-Energy House
## 2 Materials and Methods
### The Case Study Building  
The case study building was represented by a reference layout for a single-family house located in Turin, Northern Italy. According to the Italian legislation, this city is included in the \"E\" thermal zone, meaning that the temperature for the design heat load is equal to \(-8\)\({}^{\circ}\)C, and that the number of heating degree-days (HDDs) can range from a minimum of 2100 to a maximum of 3000.  
The building consisted of two floors, and it had a net internal floor area of 155 m\({}^{2}\). A kitchen, a large living room, one bedroom, and one bathroom were on the ground floor, while two bedrooms, a working/relaxing space, and a bathroom composed the first floor.  
Concerning the reference building envelope, the external walls were made up of brick blocks, and the structure was a concrete frame type. The pitched roof had a surface of nearly 70 m\({}^{2}\) with a major proportion of it (about 60%) being south-oriented and, therefore, ideal for the installation of solar energy renewable sources. To maximize solar gains, the majority of the large glass openings were located on the south-oriented walls, with the overall window area being approximately one-fifth of the gross floor area. To avoid the risk of overheating during the summer season, shading overhangs protected the openings without nullifying the beneficial effects during the winter season.  
Figure 2: The main phases of the simulation-based optimization method.  
Concerning the systems, a reference configuration was considered for the initial scenario according to current Italian legislation [29]. The system for space heating and domestic hot water (DHW) was a gas condensing boiler. The cooling equipment was represented by an electric chiller. Reference values of generation, distribution, and emission efficiencies were also considered in accordance with current legislation. Each room had a different thermal zone with a different occupancy profile and an independent HVAC system control.
## 3 Comparison of Fetal Heart Rate Monitoring Techniques  
The fetal heart rate or fetal electrocardiography monitoring thorough abdominal FECG electrodes, i.e., through non-invasive fetal electrocardiogram (NIFECG), has widespread clinical acceptance as it is suitable for long-term recordings without any surgery, unlike invasive FECG. This section illustrates the commonly used fetal heart rate monitoring techniques.  
This section illustrates the commonly used non-invasive fetal heart rate monitoring techniques in addition to FECG.
# Nighttime Image Dehazing Based on Point Light Sources
## 4 Experiment
### Supplementary Experiments  
In addition, we conducted fog removal experiments under different fog concentrations. Since there are few datasets of nighttime images, we choose the dataset source for the concentration comparison experiment as DCIM, which is a low-light image to simulate the restored nighttime scenes. The fog concentration is set manually to generate the corresponding dataset. Alpha indicates the fog concentration of the composite image, the higher the fog concentration, the higher the alpha value, and the lower the visibility of the image. The synthetic dataset is defogged and the results are evaluated using PSNR, SSIM, and NIQE metrics. Comparison experiments with different fog concentrations are shown in Figure 7. For the evaluation results, the best and worst evaluation scores are removed and the evaluation average of each method is obtained. The results are shown in Table 5.  
From the longitudinal perspective, the performance of all methods decreases to some extent as the haze density increases and less information is visible. In terms of cross-sectional metrics, the NDIM algorithm performs the worst in PSNR and SSIM metrics. Due to the presence of daytime images in the dataset, DCP has an advantage in the defogging task. In the case of dense fog, our algorithm still outperforms the other two nighttime defogging methods. In future work, we may need to explore further adaptive methods for removing different concentrations of dehazing.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**PSNR**} & \multicolumn{2}{c}{**SSIM**} & \multicolumn{2}{c}{**UQI**} \\ \hline
**Methods** & **Z-Value** & **_p-Value_** & **Z-Value** & **_p-Value_** & **Z-Value** & **_p-Value_** \\ \hline DCP & \(-3.62\) & \(<\)0.05 & \(-8.47\) & \(<\)0.01 & \(-6.91\) & \(<\)0.05 \\ NDIM & \(-9.82\) & \(<\)0.05 & \(-9.85\) & \(<\)0.01 & \(-5.06\) & \(<\)0.05 \\ OSPF & \(-9.01\) & \(<\)0.05 & \(-9.86\) & \(<\)0.01 & \(-4.78\) & \(<\)0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistical results between ours and compared algorithms on Wilcoxon Signed-Rank Test.  
Figure 6: Statistics of the results. (**a**) Histogram of results; (**b**) Box diagram of different methods.
## 4 Materials and Methods  
### Chemicals and Samples  
Two truffle samples, both black and white ones, were obtained from Kunming, Yunnan province, China. In this research, BT and WT were used to represent the black truffle and white truffle samples, respectively. Truffle samples was harvested with the best quality according to the experience of farmers and free from any quality deterioration or decay. Immediately after harvest, the ascocarps were wrapped with aluminum foil paper, vacuumized and sealed in vacuum packing bags. Afterwards, the samples were transported to our laboratory by air (ice bags were used during transportation) and stored at \(-20\)\({}^{\circ}\)C for further preprocessing within a month. For each kind of truffle, all of the ascocarps were cut up, being fully mixed and thawed at room temperature (\(20\pm 2\)\({}^{\circ}\)C) for about 1 h right before the extracting procedure, respectively.  
The solvent, diethyl ether (AR), was purchased from Sinopharm Chemical Reagent Co., Ltd. (Shanghai, China) and was redistilled before used. The solvent, methanol, and the internal standard, 1,2-dichlorobenzene, were of HPLC grade. The methanol was from Thermo Fisher Scientific Inc. (Shanghai, China) and the internal standard was from Aladdin Industrial Corporation (Shanghai, China). For the measurement of retention indices (RI), a mix of n-alkanes ranging from hexane to triacentane was used (Sigma-Aldrich Co., St. Louis, MO, USA).
# An Architecture to Manage Incoming Traffic of Inter-Domain Routing Using OpenFlow Networks
## 7 Conclusions  
The BGP protocol does not provide an effective mechanism to control inbound traffic of advertised network prefixes. This work tackled this gap in the literature and proposed a new architecture to manage incoming traffic to multi-homed ASes where their ISPs, using OF networks, provide new services to allow customer ASes to apply network management tasks into the ISPs infrastructure. This work proposed and executed experiments on how the proposed architecture could be used to provide more network traffic control using the capabilities of OpenFlow networks. The results indicated that the effective of the inbound control mechanisms to manage incoming traffic for the inter-domain routing was dependent on the logic executed in applications of the proposed architecture.  
Furthermore, the proposed architecture was extensively discussed including the limitation of the OpenFlow technology, previous works, the mechanisms to exchange control information among SDN domains, the attractiveness of deploying the architecture in the current Internet, and future works. In conclusion, the results and the extensive discussion of the proposed architecture makes this work a step further in the literature of the mechanisms for managing Internet traffic.
## 4 Discussions  
### Engineering Properties Determined via Chemical Composition  
Figure 15 shows the measured values of CaO content and CaO/SO\({}_{3}\) rate according to the admixture mixing rate. As the mixing rate increased, the CaO content and CaO/SO\({}_{3}\) rate of GGBFS decreased to 82.2% and 90.2%, respectively. In the case of FA, conversely, the CaO content decreased to 34.7% and the CaO/SO\({}_{3}\) rate increased to 110.9%. Moreover, the representative chemical components (SO\({}_{3}\), CaO, and Fe\({}_{2}\)O\({}_{3}\)) tended to decrease, whereas CaO, Fe\({}_{2}\)O\({}_{3}\), and SO\({}_{3}\) increased as the mixing rate of GGBFS increased. For FA, SiO\({}_{2}\), Al\({}_{2}\)O\({}_{3}\), and Fe\({}_{2}\)O\({}_{3}\) increased, whereas CaO, MgO, and SO\({}_{3}\) decreased.  
Figure 16 shows the measured values of CaO/SiO\({}_{2}\) and SiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) molar rates according to the admixture mixing rate. As the GGBFS mixing rate increased, both the CaO/SiO\({}_{2}\) and SiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) molar rates decreased. In the case of FA, however, the CaO/SiO\({}_{2}\) molar rate increased and the SiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) molar rate decreased. When regression analysis was conducted using the triangular graph of the CaO/SiO\({}_{2}\) and SiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) molar rates, it was confirmed that GGBFS is more beneficial in terms of long-age strength and less beneficial in terms of rapid hardening compared to FA.  
Figure 17a shows the compressive strength of the fabricated specimens by the CaO content. As the unit CaO weight went up, the compressive strength also tended to increase. When the CaO content was between 49.49% and 58.67%, however, the compressive strength of GGBFS was lower than that of FA at ages of 3 and 7 days. This appears to be because GGBFS is less beneficial in terms of rapid hardening  
Figure 16: CaO/SiO\({}_{2}\) and SiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) molar rates in terms of the mixing rate.  
Figure 15: Results of the CaO content and CaO/SO\({}_{3}\) rate in terms of the mixing rate.  
compared to FA, as indicated in the results of Figure 16. Figure 17b shows the compressive strength development rate of each admixture by age. As the CaO content increased, the compressive strength development rate tended to slightly decrease. In the case of the GGBFS specimens, in particular, the strength development rate was higher for strengths at the early ages.  
Figure 18 shows the compressive strength of each admixture type by the CaO/SO\({}_{3}\) rate. As the CaO/SO\({}_{3}\) rate increased, the compressive strength of the GGBFS specimens showed a tendency to increase except at 56 days of age, whereas that of the FA specimens showed a tendency to decrease at all ages.
# A GPU-Accelerated Method for 3D Nonlinear Kelvin Ship Wake Patterns Simulation
## 5 Numerical Simulations and Discussion
### Verification of the GPU Solver
#### 5.2.3 Capability  
In the proposed GPU solver, like the CPU solver [12], three parameters can be used to regulate simulation results: namely source strength, source type and Froude number. The wake characteristics can be regulated by adjusting these parameters. For example, the wake waves of vessels of high speed and small overall length can be generated by using the Rankine source, lower source strength and larger Froude number; oppositely, it is appropriate to choose a small Froude number and a higher strength Kelvin source. As shown in Figure 13, the simulation patterns of the GPU solver are compared with the real ship wave patterns. It is found that the simulation patterns are consistent with the real ship waves, while the proposed GPU solver can also generate high-quality simulation patterns for a 3D nonlinear ship wave.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Mesh Size** & **CPU Solver** & **Exp.** & **GPU Solver** & **Accelerated-Up Ratio** \\ \hline \(121\times 41\) & \(8.96\times 10^{1}\) s & \(1.61\times 10^{1}\) s & \(5.60\times 10^{0}\) s & 16.1 \\ \(181\times 61\) & \(2.70\times 10^{2}\) s & \(1.22\times 10^{2}\) s & \(1.13\times 10^{1}\) s & 23.9 \\ \(241\times 81\) & \(8.70\times 10^{2}\)s & \(5.51\times 10^{2}\) s & \(5.37\times 10^{1}\) s & 16.0 \\ \(301\times 101\) & \(2.56\times 10^{3}\) s & \(1.78\times 10^{3}\) s & \(1.32\times 10^{2}\) s & 19.3 \\ \(361\times 121\) & \(6.08\times 10^{3}\) s & \(5.04\times 10^{3}\) s & \(2.95\times 10^{2}\) s & 20.6 \\ \hline \end{tabular}
\end{table}
Table 4: The comparisons of runtime between the CPU solver and GPU solvers on different mesh sizes; the CPU solver is proposed by [PERSON] et al. [12], one GPU solver is proposed in this paper, and the other GPU solver is proposed by [PERSON] [15] (results of Exp.).  
Figure 12: The runtime of the GPU solver and CPU solver at different mesh sizes; red bars represent the GPU solver results and blue bars represent the CPU solver results.
## 3 Results
### Efficiency of Object Detection
#### 3.3.4 Performance Comparison of vePOD Faster R-CNN and vePOD YOLO  
Both vePOD Faster R-CNN and vePOD YOLO protect data privacy for vehicles and achieve the same detection accuracy as their original networks. Compared with vePOD YOLO, vePOD Faster R-CNN achieves a higher detection accuracy of around 8.4 percentage of MAP, especially for small clustered objects due to its two-stage detection approach.  
Moreover, we find from the experiments that the execution time of vePOD YOLO increases more when \(k\) becomes greater, e.g., from 2 to 3, compared with vePOD Faster R-CNN. This is because (1) in vePOD Faster R-CNN, when the number of secret shares \(k\) increases, the secure ROI pooling becomes less complex compared with the secure max pooling in vePOD YOLO; and (2) the execution time increase of vePOD YOLO comes from the computation cost of the four secure max-pooling layers, while in vePOD Faster R-CNN, the execution time increase is from one secure ROI pooling layer.  
Figure 10 shows that vePOD YOLO transfers less data between the vehicle and edge servers. This is because vePOD YOLO employs image-level secret sharing. In contrast, feature-level secret sharing needs to transfer both feature-map secret shares and region proposals from the vehicle to edge servers.  
Secret shares need extra storage space. In Figure 10, we can see that vePOD Faster R-CNN on two edge servers for each vehicle's image needs 280 bytes of additional space to store secret shares compared to using the feature maps. VePOD YOLO under the same setting takes an extra 160 bytes compared to using the raw image.  
Both Figures 5 and 8 indicate that the secure functions prolong the execution of those layers containing nonlinear operations. To make vePOD more suitable for real-time object detection, there are several possible ways to improve its performance. For example, we can leverage process-level parallelism, e.g., using multi-threading, to speed up the  
Figure 10: Comparison of storage and communication costs using vePOD Faster R-CNN and vePOD YOLO.  
Figure 9: Execution time of the secure max pooling in vePOD YOLO with two and three edge servers.  
repeated/iterative invocations of a secure function; inside a secure function, we can identify and maximize data reuse to remove redundant computations and exploit instruction-level parallelism to shorten each invocation. Another main cause of the performance degradation is the use of Python code in the implementation. Currently, all the secure functions are written in Python. This eases the implementation, but at the cost of performance. We plan to rewrite the secure functions using lower-level programming languages, such as C or assembly, to speed up their execution, especially in nested loops.
# Multiuser Equalizer for Hybrid Massive MIMO mmWave CE-OFDM Systems
## 2 System Characterization  
In this section we describe the transmitter, the channel model adopted, and the receiver. We consider an uplink mmWave mMIMO system whose modulation technique is the CE-OFDM. The system has \(N_{c}\) available subcarriers. It is assumed \(L\)-length data symbol blocks, and \(U\) UTs sharing the same radio resources. Each UT employs one RF chain, has \(N_{tx}\) transmit antennas and sends one data stream per subcarrier. At the receiver side, the BS employs \(N_{rx}^{RF}\) RF chains and has \(N_{rx}\) receive antennas, with \(U\leq N_{rx}^{RF}\leq N_{rx}\).
## 3 Methodology
### Feature Extraction and Selection
#### 3.2.6 Root Mean Square Energy  
The root means square energy (RMSE) feature uses a spectrogram to extract the information on signal energy over time. To extract the RMSE, this research work used a frame length of 2048 and a hop length of 512, where frame length is the size of samples from the audio signal used for energy calculation and hop length for performing an intermediate STFT. The RSME is defined as  
\[\sqrt{\frac{1}{N}\sum_{n}|x(n)|^{2}} \tag{8}\]