esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Example Verification
### Experimental Results and Analysis
#### 4.3.1 Ablation Experiment
A comparison of the heatmaps shows that the proposed method provided a better representation, with the heatmaps focusing more on the edge features of the target, which effectively reduced the rates of missed detections and false positives in images with partial shadows or occlusions. Figure 13 shows the comparison between the test results and the heatmap analysis of the baseline model and the proposed method. It can be seen that both the baseline model and the proposed method could detect the class and location of defects in the image under low-light conditions. However, the detection accuracy of the proposed method was higher compared to the baseline model. The texture of the target region in the image may have been relatively weak, resulting in the lower detection accuracy of the model. A comparison of the heatmaps shows that the proposed method could effectively highlight the features of the defective region, thus improving the detection accuracy of the model. Figure 11: Detection results and heatmaps of seven types of damage in well-lit images. Figure 14 illustrates a comparison between the test results and the heatmap analysis of the baseline model and the proposed method for images with dark lighting conditions. From Figure 14, it can be seen that both the baseline model and the proposed method performed poorly on images with dark lighting conditions. In images with dark lighting conditions, the overall brightness was low, making it difficult to detect surface defect details. The models failed to learn useful information from the images, resulting in incorrect target category detection or no defect detection. Figure 14: Detection results and thermograms of damage in dark and light images. Figure 12: Detection results and heatmaps of damage in partially shaded or occluded images. Figure 13: Detection results and thermograms of damage in low-light images. In summary, the proposed method effectively improved the accuracy of detecting images with good lighting conditions and those with poor lighting conditions, effectively mitigating the problems of missed detections and false positives.
# Rateless Coded Uplink Transmission Design for Multi-User C-RAN
## 1 Introduction
### Contributions  
In this paper, we consider the design of a rateless coded transmission scheme for two-user uplink in C-RAN under a block fading channel. It should be noted that in the proposed scheme, instant channel state information (CSI) for each user is not required. For each transmission round, each user encodes its own message using Raptor code with a pre-optimized degree profile which is fixed over all transmission rounds, and simultaneously transmits to the RRHs. Each RRH compresses its received signals and uploads to the BBU pool for joint signal processing. Upon the BBU pool recovering the messages of both users and feeding back an ACK, the current transmission round ends. We summarize the main contributions of this paper as follows:  
1. We propose a rateless coded uplink transmission scheme for two-user C-RAN with two RRHs, including the quantizer at each RRH and the iterative multi-user detector and decoder based on the belief propagation (BP) algorithm at the BBU pool.  
2. We resort to extrinsic information transfer (EXIT) to analyze the iterative detecting and decoding process at the BBU pool. Based on this, the condition for successfully decoding is derived.
3. Based on the EXIT analysis, we optimize the degree profiles of the Raptor code for each user. Explicitly, we search the optimal degree profiles to minimize the threshold signal-to-noise ratio (SNR) under a fixed average Raptor code length and the condition of successfully decoding over all possible channel states. Therefore, the resulted degree profiles are optimal in an average sense over all possible channel states.  
Note that compared with the single-user case in [38; 39], rateless coded transmission design for multi-user C-RAN uplink is more challenging. Firstly, the receiver structure for a multi-user case is more complicated than for a single-user case, wherein joint de-quantization, multi-user detection and channel decoding should be performed at the BBU pool. Secondly, the rateless code profile optimization scheme for the single-user case cannot be directly adopted here, due to the fact that the EXIT of the decoding process is totally different from the single-user case.  
Organization: This paper is organized as follows. In Section 2, we introduce the system model. The rateless coded uplink transmission scheme is presented in Section 3. In Section 4, EXIT analysis and degree profile optimization are discussed. Simulation results are shown in Section 5, and Section 6 concludes the paper.
## 1 Introduction
In order to research the selectivity of SC4A for the target ions and the combined form of the extracted complex, Na(I), K(I), Rb(I), Mg(II), Ca(II), Ba(II), Pb(II), Cd(II), Zn(II), Co(II), Ni(II), Ce(III), and Y(III) were also extracted with SC4A for comparison. Meanwhile, _p-tert_-butylthiaclix[4]arene (TC4A) and _p-tert_-butylsulfonylclax[4]arene tetraacetate (SC4 AOEt) were applied to extract Cs(I) or Sr(II) to examine the effects of the OH and SO\({}_{2}\) groups on the extraction. The composition of the complex, thermodynamic data for the extraction procedure, extraction mechanism, irradiation stability, and high-temperature resistance of SC4A were also researched.
# Detection of Defects Using Spatial Variances of Guided-Wave Modes in Testing of Pipes
## 3 Methodology
### Threshold Selection  
In cases where a significant proportion of iterations energy is from the T(0, 1) wavemode, the comparisonValue will be zero. However, as shown previously, defect signals will contain a mixture of flexural and torsional wave modes, leading this value to be non-zero. Hence, there is a need for defining a threshold for detecting the defects. In this section, three methods are presented defining this threshold.
## 4 Simulation Experiment and Result Analysis
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Function** & **Algorithm** & **Mean Value** & **Variance** \\ \hline \multirow{6}{*}{\(f_{1}\)} & GOA & \(7.02\times 10^{-4}\) & \(2.03\times 10^{2}\) \\  & BOA & \(2.54\times 10^{-13}\) & \(1.02\times 10^{-12}\) \\  & AEFA & \(1.09\times 10^{-24}\) & \(9.56\times 10^{-2}\) \\  & IAEFA & \(1.29\times 10^{-6}\) & \(4.46\times 10^{-6}\) \\  & SC-AEFA & \(5.79\times 10^{-92}\) & \(6.23\times 10^{-89}\) \\ \hline \multirow{6}{*}{\(f_{2}\)} & GOA & \(6.17\times 10^{-4}\) & \(4.58\times 10^{0}\) \\  & BOA & \(9.22\times 10^{-12}\) & \(1.36\times 10^{-9}\) \\  & AEFA & \(5.79\times 10^{-12}\) & \(9.87\times 10^{0}\) \\  & IAEFA & \(1.46\times 10^{-2}\) & \(4.50\times 10^{-3}\) \\  & SC-AEFA & \(1.32\times 10^{-46}\) & \(6.15\times 10^{-67}\) \\ \hline \multirow{6}{*}{\(f_{3}\)} & GOA & \(2.76\times 10^{2}\) & \(7.73\times 10^{2}\) \\  & BOA & \(1.94\times 10^{-12}\) & \(2.24\times 10^{-13}\) \\  & AEFA & \(1.13\times 10^{3}\) & \(4.27\times 10^{2}\) \\  & IAEFA & \(8.55\times 10^{-4}\) & \(1.26\times 10^{1}\) \\  & SC-AEFA & \(1.83\times 10^{-89}\) & \(7.38\times 10^{-99}\) \\ \hline \multirow{6}{*}{\(f_{4}\)} & GOA & \(9.63\times 10^{-3}\) & \(4.00\times 10^{0}\) \\  & BOA & \(9.88\times 10^{-12}\) & \(1.45\times 10^{-1}\) \\  & AEFA & \(2.00\times 10^{0}\) & \(9.74\times 10^{-1}\) \\  & IAEFA & \(1.30\times 10^{-3}\) & \(9.86\times 10^{-4}\) \\  & SC-AEFA & \(4.39\times 10^{-47}\) & \(2.55\times 10^{-66}\) \\ \hline \multirow{6}{*}{\(f_{5}\)} & GOA & \(1.76\times 10^{2}\) & \(3.00\times 10^{4}\) \\  & BOA & \(2.90\times 10^{1}\) & \(3.58\times 10^{-10}\) \\ \cline{1-1}  & AEFA & \(2.77\times 10^{1}\) & \(2.27\times 10^{3}\) \\ \cline{1-1}  & IAEFA & \(2.40\times 10^{1}\) & \(1.24\times 10^{1}\) \\ \cline{1-1}  & SC-AEFA & \(2.31\times 10^{-1}\) & \(3.36\times 10^{-2}\) \\ \hline \multirow{6}{*}{\(f_{6}\)} & GOA & \(2.39\times 10^{-1}\) & \(7.06\times 10^{-1}\) \\  & BOA & \(5.88\times 10^{0}\) & \(3.76\times 10^{-10}\) \\ \cline{1-1}  & AEFA & \(1.50\times 10^{-2}\) & \(1.59\times 10^{0}\) \\ \cline{1-1}  & IAEFA & \(0.00\times 10^{0}\) & \(0.00\times 10^{0}\) \\ \cline{1-1}  & SC-AEFA & \(0.00\times 10^{0}\) & \(0.00\times 10^{0}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: A comparison of the optimization results of different algorithms.
## 1 Introduction
By detecting and analyzing these newly generated gases, the current partial discharge situation of the power equipment can be obtained. Based on the above analysis, this paper proposes the use of characteristic gas method to detect partial discharge of insulation defects in air switchgear. Firstly, a numerical model and experimental platform were established to simulate the partial discharge process of insulation defects in air switchgear based on the mechanism of air discharge. Secondly, this paper verifies the effectiveness of the numerical model in describing the partial discharge process of insulation defects in air switchgear through the volt-ampere characteristic curve. Finally, numerical models were used to study the discharge process of three typical defect models, and the change curves of CO, NO\({}_{2}\), and O\({}_{3}\) gas volume fractions at different voltage levels and discharge durations were obtained. The inversion relationship between the change of characteristic gas volume fraction and the development of partial discharge was established. This paper lays a theoretical foundation for the design of online monitoring of partial discharge in insulation defects of air switchgear based on characteristic gases.
## 2 Materials and Methods
### Satellite Precipitation Data
#### 2.4.2 GPM IMERG V5  
The GPM project is the result of a collaboration between (NASA) and (JAXA). The GPM satellite carries two primary sensors: the multi-channel GPM Microwave Imager (GMI), and the Dual-frequency Precipitation Radar (DPR). This product is expected to provide the next-generation global observations of rain and snow and improve weather and precipitation forecasts through the assimilation of instantaneous precipitation information [26]. The IMERG V05 uses the Goddard Profiling Algorithm to retrieve precipitation estimates from the GPM constellation using various precipitation-relevant satellite PMW sensors. Thereafter, the precipitation estimates are gridded and inter-calibrated into the GPM combined instrument product, further interpolated, and re-calibrated by the CPC Morphing-Kalman Filter Lagrangian time interpolation and the PERSIAN-Cloud Classification System recalibration schemes. IMERG is the Level 3 precipitation estimation algorithm of GPM, which provides three different daily IMERG products, which include IMERG Day 1 Early Run (near-real-time with a latency of 6 h), IMERG Day 1 Late Run (reprocessed near-real-time with a latency of 18 h) and IMERG Day 1 Final Run (gauged-adjusted with a latency of four months) products [38]. In this study, we evaluated the latest released GPM IMERG Version 5 (IMERG V5), the dataset produced at NASA Goddard Earth Sciences (GES). The IMERG precipitation products have a relatively finer spatial 0.1\({}^{\circ}\) spatial resolution with spatial coverage from 60\({}^{\circ}\) S to 60\({}^{\circ}\) N and temporal (half-hourly) resolution. The daily precipitation data were accumulated to obtain monthly and annual precipitation. The GPM (IMERG V05 final run) precipitation data were downloaded from the PMM website ([[https://giovanni.gsfc.nasa.gov/giovanni/](https://giovanni.gsfc.nasa.gov/giovanni/)]([https://giovanni.gsfc.nasa.gov/giovanni/](https://giovanni.gsfc.nasa.gov/giovanni/)) accessed on 20 May 2022)).
# A Credit-Based Congestion-Aware Incentive Scheme for DTNs
## 3 System Model and Design Goal  
### Network Model  
As shown in Figure 2, the components of the system network model are as follows:  
Trusted Third Party (TTP) as the central authority is responsible for issuing public and private key certificates and completing the verification and credit clearance service for DTN mobile nodes.  
Fixed Network, including Internet and access points (AP), is responsible for connecting DTN nodes and TTP. DTN mobile nodes can obtain public and private keys from the TTP via AP and the  
Figure 2: Network model.  
Internet. When encountering an AP, the message destination node will send the ticket information to TTP through AP, then TTP will achieve the credit clearance for mobile nodes.  
Mobile nodes \(N_{1}\)-\(N_{6}\) in Figure 2 denote some pedestrians who carry equipment with Bluetooth and Wi-Fi function. Some mobile nodes send messages about traffic and location to other remote nodes. The value of these messages will decrease over time. When a node encounters another node with smaller delivery delay, it will forward its messages to the node. When a message is successfully delivered to the destination node, TTP will deduct some credits from the destination node to pay for the forwarding nodes according to the reward scheme in Section 4.1.
## 5 Conclusions  
The CMPES method is proposed to deal with the difficulties in pairing inaccurate analytical and incomplete experimental modal data when updating the dynamic FE model of complex engineering structures. The conclusions are as follows:  
1. To solve the problem that modal data cannot be fully exploited when natural frequencies and mode shapes of complex structures change dramatically, the correlated mode auto-pairing strategy is proposed. The one-to-one CMPs are determined adaptively by the auto-pairing strategy before each iteration based on the one-to-one symbiotic relationship between natural frequencies and mode shapes. This strategy, liberating from dependence on demanding pre-analysis, is suitable for all the 1\(\leftrightarrow\)1, 1\(\leftrightarrow\)n, and 0\(\leftrightarrow\)n cases in dynamic FE model updating.
2. To further screen the CMPs determined by the auto-pairing strategy in each generation and search the global minimum, the population evolution mechanism is used to  
Figure 13: Comparison between the updated analytical and experimental mode shapes. **(a)** The 1 st analytical and the 1 st experimental mode shapes; **(b)** the 2 nd analytical and the 2 nd experimental mode shapes; **(c)** the 3 rd analytical and the 3 rd experimental mode shapes; **(d)** the 4 th analytical and the 4 th experimental mode shapes; **(e)** the 6 th analytical and the 6 th experimental mode shapes; **(f)** the 7 th analytical and the 7 th experimental mode shapes; **(g)** the 9 th analytical and the 8 th experimental mode shapes; **(h)** the 8 th analytical and the 10 th experimental mode shapes; **(i)** the 13 th analytical and the 14 th experimental mode shapes.  
simultaneously urge the updating parameters and the CMPs towards the ideal results. Combined with the auto-pairing strategy, the adaptive switch from penalized to correlated subitem can screen the CMPs further during iteration to ensure that all the potential of analytical or experimental modes can be fully exploited.
3. The examples of a thin plate with non-uniform thickness and small holes validated the accuracy and effectiveness of the proposed method. In the updating of the IMC with different cross-sectional shapes of hollow struts and multi-layer thin-walled complex structure, all the radial modes within the range of four times the operating speed were auto-screened out and updated well. The updated results show the great potential of the proposed CMPES method for complex engineering problems.  
Conceptualization, Y.Z. and Z.J.; methodology, H.S.; software, H.S.; validation, Y.Z. and H.S.; formal analysis, H.S.; investigation, H.S.; resources, Y.Z. and Z.J.; data curation, H.S.; writing--original draft preparation, H.S.; writing--review and editing, Y.Z. and Z.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Young Scientists Fund of the National Natural Science Foundation of China, grant number 51905025.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Multi-Beam NOMA System Model
\tag{9}\]  
At the receiving end, the achievable rate of user \(k\) on LED \(m\) can be obtained as [22]:  
\[R_{k,m}=B_{m}\log_{2}(1+\frac{H_{k,m}^{2}\sqrt{a_{k}P_{m}}}{\sum\limits_{k^{ -1}}^{k-1}H^{2}{}_{k,m}\sqrt{a_{k^{\prime}}P_{m}}+\sum\limits_{m^{\prime}\
eq m }^{K_{m^{\prime}}}\sum\limits_{k=1}^{K_{m^{\prime}}}H^{2}{}_{k,m^{\prime}} \sqrt{a_{k}P_{m^{\prime}}}+\sigma^{2}}), \tag{10}\]  
where \(B_{m}\) is the transmit signal bandwidth, \(H_{k,m}\) is the channel gain of the user \(k\) on the LED \(m\), and \(\sigma^{2}\) is the noise variance. To better characterize the spot distribution of the multi-beam system in the presence of inter-beam interference, we tracked each light path of the LED light source through ZEMAX (Zemax OpticStudio 18.9) software to obtain the spot distribution. Due to the diffusion effect of underwater beams, as the transmission distance increases, the overlapping area of beams from different LED light sources becomes larger, and the inter-beam interference increases. Table 1 lists the simulation parameters of the light paths. Figure 3a depicts a multi-beam array with a semi-spherical structure simulated using ZEMAX software. Different colors indicate different LED beams. Figure 3b-d represent the distribution of light spots at 3 m, 10 m, and 20 m of the semi-spherical multi-beam array, respectively. Due to the diffusion effect of LED beams, as the transmission distance increases, the light spot begins to exhibit aliasing at 3 m. As the transmission distance increases, spot aliasing becomes more pronounced, and the inter-beam aliasing interference increases. In Figure 4, the signal-to-interference ratio (SIR) is used to describe the degree of non-target beam interference to the edge user of the cell [23], who is located at the intersection of two LEDs' irradiation ranges. The communication distance is within 2 m, and the user only receives the signal of the target beam. Edge users are affected by signal interference from adjacent beams at 2 m. As the communication distance increases, SIR sharply decreases. This means that the increased communication distance exacerbates the degree of light spot aliasing, the impact of non-targeted beam signals on cell edge users increases, and system reliability decreases. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of LEDs & 17 \\ LED half power angle & 18\({}^{\circ}\) \\ LED transmitting power & 1 W \\ Number of traced rays & 106 \\ Semi-spherical array radius & 0.5 m \\ \hline \hline \end{tabular}
\end{table} 
Table 1: ZEMAX optical simulation parameters for multi-beam system. Figure 4: Relationship between SIR and transmission distance. Figure 3: ZEMAX software simulation (**a**) Semi-spherical multi-beam array, (**b**) Spot distribution at 3 m, (**c**) Spot distribution at 10 m, (**d**) Spot distribution at 20 m.
# Effects of Reflectance of Backsheets and Spacing between Cells on Photovoltaic Modules
## 3 Results  
### Comparison of Backsheets  
Figure 2 shows the film reflectance graph of the white and black backsheets used in this experiment and Supplementary Materials Figure S1 displays the illustrated explanation of the concept. The average of the white backsheet reflectance was 85.39% in the range of 300 to1200 nm and the peak was 96.67% at 433 nm. The average of the black backsheet reflectance was 42.43% in the range of 300 to 1200 nm and the peak was 93.15% at 986 nm. The average reflectance of the black backsheet was more or less than half of that of the white backsheet. The black backsheet reflected the IR region, which is 750-1200 nm, while the white backsheet reflected the visible and IR region, which is 380-1200 nm. The result shows that black backsheets absorb visible light and how much reflection of visible light affects module efficiency.
# Programmable Data Plane Applications in 5G and Beyond Architectures: A Systematic Review
## 4 Findings and Discussion
### Obtained Results Discussion  
#### 4.4.1 Tunneling and Forwarding  
The works in [46; 49; 55; 59; 64; 68; 69] report processing latencies of 1.23 \(\upmu\)s, 200 \(\upmu\)s, 25 \(\upmu\)s, 0.458 \(\upmu\)s, 6 \(\upmu\)s, 5 \(\upmu\)s, and 0.739 \(\upmu\)s, respectively. The study in [56] reports a latency reduction of 77% compared to common software implementations, while [61] shows a drop of 3.75x compared to a host based UPF deployment using DPDK. Furthermore, [44] specifies an end-to-end latency of 50 \(\upmu\)s, which is useful for checking compliance with the KPIs from [10].  
Regarding throughput, experiments in [60; 69; 75] display 98.7 Gbps, 40 Gbps, and 2 Gbps, respectively, while [47] achieves approximately 100 Gbps. In [80], the obtained data rate is 402\(\times\) higher than that of a pure UPF software design.
## 2 Materials and Methods
### Definition of Energy Zones  
The previous section showed that Greece is divided into four climatic regions (zones), as far as energy purposed for buildings is concerned. This division is based on the criteria of Table 2. The map in Figure 1 deploys the selected 33 sites into regions, which obtain a different color according to these criteria. Furthermore, by isolating the SSR limits of Table 2 (column 4), the notion of energy zones in Greece is introduced for solar applications. The definition relies on the annual solar energy received on a horizontal flat plane all over Greece. For this purpose, the annual energy yield for every HNOS station of the 33 mentioned in Table 3 was estimated. Figure 3a shows these annual energy yields together with the SSR limits of Table 2. Again, four zones are identified, which are named TOTEE zones. It is important to observe that there are no data points in the TOTEE zone C (i.e., below 1550 kWhm\({}^{-2}\)-year\({}^{-1}\)) or above 1850 kWhm\({}^{-2}\)-year\({}^{-1}\). In addition, zones B and C overlap, while zone D is not used at all in accommodating the annual energy yield from any site. To overcome these problems, the span of 1550-1850 kWhm\({}^{-2}\)-year\({}^{-1}\) was divided into 4 equal shares, which are shown in Figure 3b. Each band is called a PV\({}_{0}\) zone; the subscript 0 implies the solar global irradiation received by a horizontal plane (inclination of 0\({}^{\circ}\) to the local horizon). A comparison of these thresholds defining the TOTEE and PV\({}_{0}\) zones is shown in Table 4. Nevertheless, there is a discrepancy in the categorization of the sites according to the climatic zones (colors in the map of Figure 1) and the TOTEE ones (Figure 3a). To realize the problem, Table 3 shows the various categorization schemes according to the above and highlights the mentioned discrepancies. The present work adopted the PV\({}_{0}\) categorization for global horizontal irradiation as a more appropriate and crystal-clear classification scheme and used it in the analysis that follows.  
Figure 2: Definition of the incidence angle \(\theta\). The solar altitude \(\gamma\), the tilt angle of the sloped surface \(\beta\), and the azimuths of the solar position in the sky and of the orientation of the tilted plane \(\psi\) and \(\psi^{\prime}\), respectively, are shown. In the case of the present work, \(\psi^{\prime}\) = 180\({}^{\circ}\) (a south-facing surface). In the graph, the Sun is at its highest \(\gamma\) (local solar noon), which coincides with the southerly direction. The four main orientations in the local horizon of the sloped surface are also indicated (i.e., N(orth), E(ast), S(outh), W(est)).  
Figure 3: Solar energy yield per year on a horizontal plane \(G_{\epsilon,05}\) at each selected site of the HNMS meteorological network. The graph includes (**a**) the four TOTEE zones and (**b**) the four PV\({}_{0}\) zones, shown by single-arrowhead or double-arrowhead lines.  
_Appl. Sci._**2021**, _11_, 1547