esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Ga Nanoparticles on Graphene/Glass  
First, as a reference system, we consider the case of Ga NPs deposited on a monolayer (ML) of graphene on an insulating glass substrate. Figure 1a shows the real-time evolution of the pseudo extinction coefficient \(\langle k\rangle\) during the Ga NP deposition on the G/glass substrate. As the Ga deposition over the substrate increases (white arrow in the figure), a peak associated with an LSPR appears in the \(\langle k\rangle\) spectra. The LSPR peak red-shifts and increases in amplitude with deposition time, consistently with the increase in the size of the Ga NPs [10; 11], whose final morphology is shown as an inset in the same figure. The Ga NPs have a hemispherical shape, and their diameter has been estimated to be 50-60 nm through AFM. The excited LSPR corresponds to the so-called longitudinal (LO) mode, while the transverse mode (TO) is at energies higher than 7 eV and, therefore, is out of the measurement range [10; 11]. Figure 1b shows the typical near-field distribution of a hemispherical Ga NP on a G/glass substrate excited at the LO mode. The near-field map shows a strong localization of the electromagnetic field (hotspots) at the interface between the Ga NPs and the G/glass substrate. In fact, these hotspots have been proven to enhance the Raman signal of the graphene layer. Figure 1c shows the graphene Raman spectra before and after Ga NP deposition. Interestingly, the spectra are dominated by the characteristic G and 2D bands of graphene, respectively at 1583 cm\({}^{-1}\) and 2698 cm\({}^{-1}\), with a characteristic intensity ratio typical of 1 monolayer (1 ML) graphene and without any significant peak D (at \(\sim\)1350 cm\({}^{-1}\)) indicative of defects, demonstrating that the good quality of the graphene CVD-grown on Cu foil is preserved after being transferred to the glass substrate and the Ga NP deposition. It can also be seen how the intensity of the Raman peaks of graphene on glass is significantly enhanced by the Ga NPs due to the electromagnetic enhancement and hot spots localized at the Ga NP/G interface, as shown in Figure 1b. For both the G and 2D peaks, the intensity increases more than one order of magnitude.  
Figure 1: Ga nanoparticles (NPs) on G/glass substrate. (**a**) Extinction coefficient spectra \(\langle k\rangle\) recorded in real time during Ga NP deposition on the G/glass substrate; the first blue spectrum refers to G/glass, while the last black spectrum refers to the Ga NPs/G/glass; the arrow indicates Ga NP deposition. As an inset is shown an atomic force microscopy (AFM) image of the Ga NP morphology. (**b**) Near-field distribution of the electromagnetic field when the Ga NPs are excited at their longitudinal (LO) mode. (**c**) Raman spectra of the graphene layer/glass before and after Ga NP deposition.
# Evolution of V2X Communication and Integration of Blockchain for Security Enhancements
## 3 Evolution of V2X Technologies
### Evolution of Cellular V2X
#### 3.2.1 Evolution from Lte-V2X to 5G NR V2X
According to the 5 GAA, Rel.16 was expected to be finalized in March 2020, with the physical layer specifications previously completed in December of 2019 and the 5 NX is expected to be commercialized by 2023. The Rel.17 will be among the primary operations of the 3 GPP between 2020 and 2021, with a deadline for conclusion in July 2021 [32]. Figure 5: Evolution of C-V2X towards 5G NR V2X [32].
## 3 Results and Discussion
### Raman Spectroscopy  
Figure 12 shows the Raman spectra of two-layer graphene and the Gr/AgNW/Gr composite. Raman shift at D, G, D', and 2D peaks and intensity ratio of I\({}_{\textrm{C}}\)/2D are listed in Table 4. The higher intensity at D peak in the composite spectra indicates that the AgNW between two graphene layers increases the disorder level of the sp\({}^{2}\) hybridization. Besides, the localized vibration mode of AgNW  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \begin{tabular}{c} **AgNW** \\ **Concentration (mg/mL)** \\ \end{tabular} & \begin{tabular}{c} **AgNW** \\ **Sheet Resistance** \\ \end{tabular} & \
\begin{tabular}{c} **Gr/AgNW/Gr** \\ **Sheet Resistance** \\ **(measured)**\(\Omega\)/\(\Box\) \\ \end{tabular} &
\begin{tabular}{c} **Contact** \\ **Resistance** \\ \end{tabular} \\ \hline
0.2 & 453 & 255.1 & 625.5 \\
0.4 & 305 & 164.7 & 221.7 \\
0.6 & 135 & 102.9 & 67.6 \\
0.8 & 79.9 & 65.3 & 30.1 \\
1.0 & 55.8 & 43.1 & 10.9 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of sheet resistances with different AgNW concentrations.  
Figure 11: (**a**) Force–displacement curves of two-layer graphene and Gr/AgNW/Gr composite. SEM images of (**b**) two-layer graphene and (**c**) Gr/AgNW/Gr nanocomposites after AFM indentation.  
splits the D' peak from the original G peak. The IG/I2D ratio is commonly used to determine the number of graphene layers [9; 16; 21; 22; 23]. As both samples have two layers of graphene, the ratio values are close to each other. The blue shift of the Gr/AgNWW/Gr sample is generated by the decreased Fermi velocity [24], which may correspond to the change in twist angle and layer separation between the two graphene layers induced by the AgNW network.
# A Novel Multipath Transmission Scheme for Information-Centric Networking
## 5 Performance Evaluation
### Experiment Setup  
We implement our proposed multipath transmission scheme based on NS-3 [46], which is designed to meet the needs of academic research and teaching. As shown in Figure 3, we use a real-world topology, the European Academic Network (GEANT2) [47], to comprehensively evaluate the performance of our multipath transport scheme. The default latency and bandwidth between backbone nodes in the topology are 100 Mbps and 1 ms, respectively. In addition, we added a data source node (S) and some receiver nodes (C1 to C5) at the edge of the network, marked with rectangles and triangles, respectively. The default bandwidth between the data source node (or receiver nodes) and the corresponding edge nodes is 1000 Mbps, and the default latency is 5 ms. In our scenario, chunk is the basic data unit, and the default size of chunk is set to 2 MB. A complete chunk will be divided into smaller segments for transmission, and the segment size is set to 1250 bytes. Additionally, each router has a maximum queue length of 1000 packets. To make a comprehensive comparison, we use three transmission methods at the network layer, which are single path methods, i.e., Open Shortest Path First (OSPF) [48], ECMP, and our proposed multipath transmission scheme. OSPF, ECMP, and the proposed multipath transmission scheme belong to the category of network layer. At the transport layer, we use the transmission protocol proposed in previous work [35] to ensure reliable and stable data transmission.  
We evaluate the performance of our scheme based on three metrics: average throughput, average flow completion time (FCT), and average chunk completion time (CCT). The average CCT can reflect the response speed of each transmission method. The shorter the chunk completion time, the faster it can be delivered to the upper layer application. This is among the most important metrics for data transmission.
###### Abstract  
The aim of this study was to evaluate the application feasibility of the nonlocal means (NLM) noise reduction algorithm in low-dose computed tomography (CT) images using an advanced modeled iterative reconstruction (ADMIRE) iterative reconstruction technique-based tin filter with various applied parameters. Low-dose CT images were based on high pitch and tin filters and acquired using slices of the aortic arch, the four chambers of the heart, and the end of the heart. Intensities A2 and A3 as well as kernels B40 and B59 were used as the parameters for the ADMIRE technique. The NLM denoising algorithm was modeled based on the principle of weighting between pixels; the contrast-to-noise ratio (CNR), edge rise distance (ERD), and blind/referenceless image spatial quality evaluator (BRISQUE) were used as image quality evaluation parameters. The CNR result was the highest, with an average of 43.51 in three slices when the proposed NLM denoising algorithm was applied to CT images acquired using the ADMIRE intensity 2 and B59 kernel. The ERD results were similar to those obtained using the ADMIRE intensity 2 and B59 kernel in the CT image acquired using the proposed method. In addition, BRISQUE, which can evaluate the overall image quality, showed a similar trend to the ERD results. In conclusion, the NLM noise reduction algorithm is expected to maximize image quality by preserving efficient edge information while improving noise characteristics in low-dose CT examinations.  
non-local means noise reduction algorithm; low-dose CT; iterative reconstruction; quantitative evaluation of image quality +
Footnote †: journal: Medical Imaging  
Acticle
## 3 System Functional Modules
### Sensor Node Module Hardware and Software Design  
The sensor node module consists of the CPU, the 2.4G wireless transceiver module, and the data acquisition channel (Figure 4).  
Figure 4: System architecture of the node module.  
Figure 3: Architecture of ZKOS.  
The 2.4G wireless transceiver module and the central processing unit (CPU) are integrated into JN5139 for data exchange among the base stations, thus an internal wireless network is built. In order to access various types of sensors the data acquisition channel provides a variety of signal interfaces, including: (1) a standard, multi-channel, 4-20 mA, analog signal interface (e.g., J1-J2); (2) standard interfaces, such as 232 interface, 485 interface and SPI interface; (3) customizable serial I/O interface; (4) interfaces that facilitate the expansion of power supply [9].  
As the 2.4G wireless transceiver module receives the signal from the base station, the awaiting sensor set start to gather the temperature and humidity data, and transmit the data digitally to the CPU in JN 5139. The CPU will pack the data, and transmit the data to the base station through the 2.4 G wireless transceiver module. All transactions are classified into four tasks by the operating system embedded in the CPU:  
Task 1. Schedule the procedure of A/D conversion for all analog channels (\(>\)10 us, may be manually specified), and convert the digital value to the real value.  
Task 2. Schedule the communication procedure for all data channels (\(>\)0.1 ms, may be manually specified), and get the sample data from sensors.  
Task 3. Schedule the data packing procedure (\(>\)1 min, may be manually specified), and send the packet to the base station by the 2.4G wireless transceiver module.  
Task 4. Reset the ON/OFF status and the sampling interval for each sensor according to the message updated by the base station. Once a task is accomplished, the node becomes dormant.
# Shapley's Value as a Resource Optimization Strategy for Digital Radio Transmission over IBOC FM
## 3 Results
### Description of the Proposed Scenario
For each player k in [1, Nj]:  a. For each coalition size lg in [1, Nj]:  i. Initialize nMK to 0.  ii. For each coalition i in [1, n_coal]: - For each player j in [1, Nj]: * If M_Coalitions(i, j) is equal to k: - Set N_ceros to 0. - For each player g in [1, Nj]: + If M_Coalitions(i, g) > 0: * Increment N_ceros by 1. - If N_ceros is equal to lg: * Increment nMK by 1. * Copy the coalition M_Coalitions(i, ;) to Mk_Coalitions(nMK, ;). * Copy the coalition value V_Coalition(i) to Vk_Coalition(nMK). * Break the loop for j.  iii. Initialize Sub_coal as a matrix of zeros with dimensions (nMk, Nj). iv. For each coalition i in [1, nMK]: - Set j to 0. - For each player g in [1, Nj]: * If Mk_Coalitions(i, g) > 0 and Mk_Coalitions(i, g) is not equal to k: + Increment j by 1. + Set Sub_coal(i, j) to Mk_Coalitions(i, g). v. For each coalition i in [1, nMK]: - If Ig is equal to 1: * Set Vk_Sub_coal(i) to 0. - Else: * For each coalition j in [1, n_coal]: + If Sub_coal(i, ;) is equal to M_Coalitions(j, ;): * Set Vk_Sub_coal(i) to V_Coalition(j). vi. Set M_Shapley(k, lg) to the sum of (Vk_Coalition - Vk_Sub_coal). vi. Clear Vk_Coalition, Vk_Sub_coal, Mk_Coalitions, and Sub_coal. 2. For each player S in [1, Nj]: - Set B(S) to (factorial(S-1) \(\times\) factorial(Nj - S))/factorial(Nj). 3. For each player i in [1, Nj]: - Set Z to M_Shapley(i, ;) element-wise multiplied by B. - Set Weight (i) to the sum of Z. ```  
**Algorithm 2**ShapleyValueCalculation  
However, it is noteworthy that despite this limitation, the Shapley value contributes to a more equitable distribution of resources by allocating bandwidth proportionally according to the contribution of each node to the optimization process. This observation underscores the importance of considering not only the absolute number of resources allocated but also their equitable distribution among the nodes within the system.
## 5 Simulation and Experimental Results  
To demonstrate the feasibility of the proposed OTA-C configurations, the proposed biquad and QO were simulated in PSpice using the macro-model of LT1228 IC and were measured in the laboratory using commercially available IC from Linear Technology Inc., LT1228. The power supply was \(\pm 15\) V. The transconductance gain is given by  
Figure 4: The non-ideal OTA with its parasitic resistors and capacitors.  
Figure 5: The influence of parasitic components on the proposed VM biquad.  
g\({}_{\text{m}}\) = 10 I\({}_{\text{B}}\)[24]. This feature makes it possible to electronically control the transconductance gain.
# Self-Supervised Spatio-Temporal Graph Learning for Point-of-Interest Recommendation
## 4 Experiments
### Ablation Study (RQ2)  
To explore the effects of different data augmentation and pre-text tasks on the model performance, we show the results of four model variants in Figure 2. Based on the results, we have the following observations:  
* In the strategies we designed, the temporal-based approaches (i.e., TEP and TCL) worked better on the Foursquare and Gowalla datasets, and the spatial-aware approaches (i.e., SEP and SCL) performed better on the Meituan dataset. This may indicate that the spatial factor has a greater influence on the Meituan dataset compared with other datasets.
* Although both consider spatio-temporal information, the data augmentation-based approach outperformed the pre-text-task-based approach. This may be due to the direct modification of the graph structure using the self-supervised method of data augmentation, which allows the node representation of the GNN output to make better use of spatio-temporal prior knowledge.
## 2 Literature Review and Hypothesis Development
### Theory of Planned Behavior
#### 2.1.2 Subjective Norms  
Subjective norm refers to the perceived social consequences of \"performing\" or \"refraining from performing\" [32]. Individuals frequently imitate others and adhere to various social norms. These practices are a society's 'guideline' norms or behavioral desires that govern what is considered acceptable and desirable [57]. Earlier research established the perceived norm as a strong predictor of plans to acquire energy-efficient appliances [58; 59]. The latest evidence in South Korea, depending on survey questions to evaluate Korean customers' buying intention toward EEAs, discovered that SN had a favorable effect on EEHA [9]. Furthermore, [PERSON] et al. [60] noted that this impact also applies to EEA purchase intentions. Additional research from around Asia [61; 62] has backed up these outcomes. Furthermore, [PERSON] et al. [1] discovered a non-significant connection between SN and purchase intent of energy-saving items in the case of Pakistani consumers' inclination to buy energy-saving products. Nevertheless, [PERSON] et al. [5] identified that SN does not have a substantially positive connection with EEA buying intention, and the absence of a link implies that customers are not easily affected by other people's judgments. As a result, the following hypotheses are proposed:  
**Hypothesis 2**.: _Subjective norm is significantly and positively related to the attitude of residents towards the purchase of EEHA._  
**Hypothesis 3**.: _Subjective norm is significantly and positively related to the intention of residents to purchase EEHA._
## 3 Results
### Data and Statistical Tests  
#### 3.1.1 Statistical Tests for Latent Variables  
This study focused on the impact of driving style and distraction state on the utility of LDW modes. Since the latent variables including driver's gender, age, driving years, and average annual miles driven may cause the interference, statistical tests were conducted to test whether there exists a significant difference between each variable and LDD.  
1. Mann-Whitney U test  
By testing the normality between LDD and gender, it was found that it did not conform to normal distribution. Since LDD and gender were numerical and binary variables, respectively, the Mann-Whitney U test was used to determine whether there was a significant difference in LDD across genders, without the assumption of normal distributions. As shown in Table 2, the significant level \(\alpha\) suggested that the driver's gender did not affect the LDD (\(p=0.668>0.05\)).  
2. Pearson Correlation analysis  
LDD, age, driving years, and average annual miles driven were numerical variables, thus a Pearson correlation analysis was used to test the difference between LDD and other variables, as shown in Table 3. It can be seen that the driver's age, driving years, and average annual miles driven did not have a significant effect on the LDD, so the effect of these factors on the LDD could be excluded.  
3.1.2 Statistical Test for Survival Analysis  
The survival analysis model adopted in this study was explained in Section 2.2. In order to determine whether a parametric, semi-parametric, or non-parametric model needed be used, an early statistical test was carried out. Since the parametric model requires the survival time variable to satisfy any non-negative parametric distribution, this study tested the goodness-of-fit of several distributions, including the Gamma distribution, the Weibull distribution, and the Log-Normal distribution, for the duration variable, LDD. The results are shown in Figure 5. It can be seen that LDD for three warning situations did not fit any of the distributions. Therefore, the non-parametric K-M model and the semi-parametric COX model, which do not require a certain distribution assumption, were suitable.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multicolumn{5}{c}{**Test Statistic**} \\ \cline{2-6}  & **Mann-Whitney U** & **Wilcoxon W** & **Z** & **\(\alpha\)** \\ \hline LDD & 113,285.5 & 223,031.5 & \(-0.429\) & 0.668 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mann-Whitney U test between gender and LDD.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multirow{2}{*}{**LDD**} & \multicolumn{3}{c}{**Statistic**} \\ \cline{3-5}  & & **LDD** & **Age** & \
\begin{tabular}{c} **Driving** \\ **Years** \\ \end{tabular} &
\begin{tabular}{c} **Average Annual** \\ **Miles Driven** \\ \end{tabular} \\ \hline \multirow{2}{*}{LDD} & Pearson correlation & 1.000 & \(-0.001\) & \(-0.21\) & 0.007 \\ \cline{2-5}  & Significance & - & 0.968 & 0.511 & 0.833 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Correlation analysis between LDD and other personal variables.
## 5 Urban Eco-Efficiency Response  
### Relationship between Urban Eco-Efficiency and Urban Spatial Structure Response  
To reveal the trend of eco-efficiency with urban spatial structure in Shandong Province, the relationship between urban eco-efficiency and spatial structure was investigated using linear regression (least squares). Figure 8 shows the regression curve of urban compactness and urban eco-efficiency. From the figure, it can be seen that with the increase in urban compactness, the eco-efficiency shows a trend of a small decrease followed by a large increase, then basically stays stable, and then there is a large decrease. The reason behind this may be that in the initial stage of urban development, resources are concentrated in large cities. With the increase in agglomeration, the environment becomes more crowded and polluted, and the urban development industry is mainly concentrated in industrial industry, with high energy consumption, high pollution, and a large degree of damage to the urban ecological environment. With the expansion of city scale and rapid economic and social development, the main industries complete their transformation, residents and industries spread to the surrounding areas, and the development of new urban centers and urban agglomerations tends to be polycentric; large cities with a concentration of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Year**} & **Provincial** & **Provincial** & \multirow{2}{*}{**Jiaodong–Lunan**} \\  & **Capital–Jiaodong** & **Capital–Lunan** & \\ \hline
2014 & 0.35841 & 0.4473 & 0.30512 \\
2016 & 0.40277 & 0.47654 & 0.32027 \\
2018 & 0.42999 & 0.51236 & 0.34065 \\
2020 & 0.41947 & 0.44733 & 0.3524 \\
2021 & 0.43452 & 0.49811 & 0.34696 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The contribution of inter-regional differences (Gnb) in ecological efficiency in Shandong Province.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Year**} & **Inter-Subgroup** & **Intra-Subgroup** & **Hypervariable** \\ \hline
2014 & 51.3584 & 31.097 & 17.5446 \\
2016 & 51.4455 & 31.3112 & 17.2433 \\
2018 & 51.4914 & 31.5413 & 16.9673 \\
2020 & 45.86 & 31.9151 & 22.2249 \\
2021 & 52.2395 & 30.3727 & 17.3878 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The contribution of super-efficiency density (Gt) in ecological efficiency in Shandong Province.  
residents, economy, and technology can share and recycle public resources; share fixed costs, such as water conservancy infrastructure; and even protect the environment through technological advances brought about by corporate competition. However, as large cities attract excessive population concentrations, overcrowded urban environments lead to massive duplication of construction and excessive maintenance costs for public resources, and the low costs associated with economic development lead to wasteful use of resources in urban environments.
# An Estimation Method of River Dry Runoff Alteration after Upper New Reservoirs Storage
## 3 Results and Discussion
### Runoff Prediction
By utilizing this ratio, we can estimate the future 95% assurance runoff after the construction of dams in the Jinsha River using the following equation:  
\[Q^{1}_{P=95\%}=Q^{0}_{P=95\%}+\Delta Q^{1}_{driest\ Month}\times(\Delta Q^{0}_ {P=95\%}/\Delta Q^{0}_{driest\ Month}) \tag{6}\]  
where, \(Q^{1}_{P=95\%}\) is the predicted assurance runoff of 95%, \(Q^{0}_{P=95\%}\) is the practical assurance runoff of 95%, and \(\Delta Q^{0}_{P=95\%}\) is the practical assurance runoff increment of 95%. Thus, the predict value of the assurance runoff of 95% was 2174 m\({}^{3}\)/s, which is obtained by  
\[Q^{1}_{P=95\%}\ =1749+654\times 0.65=2174\ \rm{m}^{3}/\rm{s} \tag{7}\]  
Recently, there has been little relevant reporting on runoff estimation after the construction of reservoir clusters. The method developed in this paper provides a reasonable and straightforward tool for researchers, which can help prevent future engineering invalidation and minimize resource costs. However, this method requires careful consideration of the characteristics of the studied river reach and the management of reservoir clusters during its application. It is crucial to determine the disparities between theoretical and actual runoff based on available measured data rather than simply generalizing these findings to all river channels.