esa-sceva/satcom-chunk-collection huggingface dataset:
# Understanding Consumer Acceptance for Blockchain-Based Digital Payment Systems in Bhutan
## 3 Theoretical Framework and Hypothesis Development
### Social Influence  
Social influence (SI) is defined as the effects of peer pressure, social norms, and social expectations on the use of technology [53; 92]. In collectivist cultures such as Bhutan, where shared decision-making values are dominant, social pressure shapes individual action. Suggestions, recommendations, and incentives from trusted individuals, organizations, or community leaders can provide an ideal apropos setting for the adoption of new technologies. Social endorsement by influencers or early adopters significantly fosters trust and increases technology adoption by mitigating risks and uncertainty [97; 107]. In Bhutan's case, the support of well-regarded village elders and official representatives can prove crucial to accelerating the adoption of blockchain-based payments. Recommendations from early adopters who have experienced success with blockchain can also build trust within local communities [32; 108]. This is particularly true in Bhutanese society, where decisions are often based on shared values and the voices of others we trust. Social influence can be enhanced through awareness campaigns, community events, and specific rewards, including discounts or reward points for paying using a blockchain-based platform [109]. Such practices can shift the perception that blockchain is a highly complex system and will be accepted by its peers and community networks [110]. Therefore, we propose the following hypothesis:  
**H3**: _Social influence positively affects consumer acceptance of blockchain adoption for digital payments in Bhutan._
# Design and Research of Photonic Reservoir Computing for Optical Channel Equalization
## 3 Results and Discussion
### Optimization of Input and Output Port Counts  
The equalization capability of different input and output port quantities was analyzed based on a 48-node layout. The input and output ports are distributed along the four sides of the rectangle, named in clockwise order, with a maximum of 14 ports each, as shown in Figure 5.  
When discussing the input port counts, the number of output ports is fixed at 14, while the number of input ports is varied from 2 to 14. The same approach is used when discussing the number of output ports. For a fair comparison, the insertion loss of the input layer's beam splitter is set to 0. The distorted signal is a 25 Gb/s OOK and 50 Gb/s PAM4 signal transmitted through a 20 km fiber.  
The equalization results for varying numbers of input nodes are shown in Figure 6a. For both OOK and PAM4 modulation formats of the distorted signals, increasing the number of input nodes does not significantly change the final equalization outcome. When the number of input nodes increases from 2 to 14, the BER of the equalized signal fluctuates around the mean value. This is because the distorted input signal is equally divided by a beam splitter into multipath signals with the same power. Each of the split optical signals carries the same information into the reservoir, and regardless of the number of splits, the optical signal power entering the reservoir remains constant. This result demonstrates that the PhRC performance is insensitive to changes in the number of inputs.  
The equalization results for different numbers of output ports are shown in Figure 6b. Under both OOK and PAM4 modulation formats, from 2 to 14 output ports, the equalization capability of the PhRC generally exhibits an upward trend as the number of ports increases.  
Figure 5: Input and output positions of the rectangular 48-node reservoir layout.  
Figure 6: The equalization effect of 48-node PhRC with different numbers of (**a**) input ports and (**b**) output ports.  
However, at certain specific numbers of output ports, fewer ports may achieve a better BER after equalization. For instance, during OOK signal equalization, the BER at three output ports is significantly lower than the data points surrounding it. This phenomenon arises because some nodes output redundant information, and the inclusion of such redundant information can lead to slight instability in the final weight training process.
## 4 Results
### Service Layer
#### 4.3.2 Service Functional Requirements of an Underground Utility Tunnel  
As shown in Table 6, the required functions of an underground utility tunnel digital twin system were deduced according to the scenario when using an underground utility tunnel digital twin system based on the scenario of the situation room and site workers. For system classification, digital twin functions can be distinguished into DB management and data transmission, underground utility tunnel management, safety management, abnormal signal transmission, and simulation. It was classified based on the minimum required functions of the system according to the requirements of underground utility tunnel managers and scenarios; the minimum required functions can be applied when building an underground utility tunnel digital twin system.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Category** & **Description** \\ \hline \multirow{4}{*}{Database management} & Collect and store databases \\ \cline{2-3}  & Convert and transmit databases \\ \cline{2-3}  & Statistical analysis of databases \\ \hline \hline \end{tabular}
\end{table}
Table 6: Functional requirements of an underground utility tunnel digital twin.  
Figure 8: Service flow of a digital twin space.  
The underground utility tunnel manager, accommodation facility manager, and disaster response system manager, which are the recipients of the abnormal situation message and digital twin model, should constantly identify the operational status of an underground tunnel, abnormal situations, and disasters based on abnormal situation message and decision-making support information provided in stages, and immediately execute response measures such as promptly giving commands and reporting in cases of emergency. Specifically, event messages of abnormal situations and the digital twin model must be presented in an easy-to-comprehend manner and be readily controllable by information recipients. An underground utility tunnel is an underground facility having a length of tens of km in general, and therefore it is extremely challenging to respond to disasters. Therefore, an underground utility tunnel digital twin platform should realistically reflect 3D spatial information to facilitate exploring an underground utility tunnel in three dimensions and support AR-based mobile patrol and inspection, as well as the disaster simulation of fires, flooding, and earthquakes reflecting the actual structure and sizes of a complex underground utility tunnel, and should report the operational status of accommodation, electric, and communications facilities at the time of disaster.
# Minimum-Cost Offloading for Collaborative Task Execution of MEC-Assisted Platooning
## 3 System Model
### Task Execution Model
#### 3.3.3 Platoon Data Transmission  
If the task \(k\) is executed by the platoon members, the data needs to be offloaded to the destination vehicle terminal before it is executed. The data transmission time for offloading the task \(k\) from vehicle \(v_{1}\) to vehicle \(v_{2}\) is computed by  
\[t_{v_{1},v_{2}}^{k}=\frac{d_{k}}{R_{v_{1},v_{2}}^{k}} \tag{9}\]where \(R^{k}_{v_{1},v_{2}}\) is the data transmission rate from vehicle \(v_{1}\) to vehicle \(v_{2}\). We have  
\[R^{k}_{v_{1},v_{2}}=B_{1}\log_{2}\left(1+\frac{\xi_{v}\mathrm{PL}_{v}(l_{v_{1},v _{2}})}{\mathrm{N}_{0}}\right) \tag{10}\]  
where \(\xi_{v}\) is the signal transmission power of the vehicle, \(\mathrm{N}_{0}\) is the noise power and \(B_{1}\) is the bandwidth for V2V communication. If \(v_{1}=v_{2}\), \(t^{k}_{v_{1},v_{2}}=0\).
## 4 The Field-Deployed Seven-Core MCF Cable
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Core-to-Core Distance (\(\mu\)m)** & **Mode Field** & **Dispersion** & **Attenuation** & **Cut-Off** & **Polarization** & **Inter-Core** & **Bending Loss** \\
**Croeffunct** & **Diameter (\(\mu\)m)** & **Coefficient** & **Coefficient** (ps/nm/km)** & **Coefficient** (dB/km)** & **Wavelength** & **Dispersion** & **Crossstalk** & **with R = 30 mm** \\ \hline
42 & 9.0 & \(\leq\)22 & \(\leq\)0.22 & \(\leq\)1300 & \(\leq\)0.08 & \(\leq\)\(-\)50 & \(<\)0.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Measured key characteristics of the 7-core fiber after cabling. Figure 3: (**a**) Route of the deployed MCF cable and the cross-section of the 7-core fiber. (**b**) Photograph of overhead-installed MCF cable. (**c**) The structure of the 17.69 km MCF cable composed of 11 segments of short MCF cables, and the schematic diagram of the 70.76 km 7-core fiber link. (**d**) Photograph of a fusion-splice point within the fiber cable. (**e**) Photograph of testing the sensing ability of the ICS transmission system. ICS OTN transmission system. The structure of the two 70.76 km CDM links is depicted in Figure 1. We measure the span loss and the total inter-core crosstalk of the two CDM links, and the results are shown in Tables 2 and 3, respectively. The total inter-core crosstalk is defined as the sum of crosstalk from all the other fiber cores to the core under test. We find that the span loss and the inter-core crosstalk are no more than 25.7 dB and \(-42.5\) dB, respectively, for each fiber core channel. And the span loss is obviously larger the that of existing SMF cable of an equivalent length, which is primarily due to the higher splicing losses inherent in MCF compared to conventional SMF. It can also be found that span losses are slightly larger than those in our previous work [21]. This is mainly because the FIFO devices and their SM pigtails were squeezed before the field trial, resulting in a deterioration in their performance.
## 3 Results and Discussions
### Thermogravimetric Analysis (TG/DTA)
These losses were accompanied by an endothermic effect at 60 \({}^{\circ}\)C and one a slightly exothermic effect at 217 \({}^{\circ}\)C. As in the other cases, these losses can be attributed to both the release of CO, CO\({}_{2}\) and moisture and to the fact that some of the functional groups on the GO surface interacted with ZnO. In the third step, between 230-380 \({}^{\circ}\)C, there is a weight loss of 2.21%, accompanied by an endothermic effect at 277 \({}^{\circ}\)C. The loss of mass in this step may be due to pyrolysis of oxygen-containing functional groups. In the fourth step, between 380-600 \({}^{\circ}\)C, the mass loss was 0.84%, with a slight exothermic maximum at 550 \({}^{\circ}\)C, which may be due to decomposition of the GO skeleton. In the case of GO-ZnO-APTES powder, the total mass losses are lower (3.60%) as compared to those recorded for GO-ZnO powder, which confirms the presence of the silane deposited on the surface of GO-Zn powder. As in the previous case, the weight  
Figure 5: TG (**a**) and DTA (**b**) curves of GO, GO-Ag, GO-Ag-APTES, GO-ZnO and GO-ZnO-APTES GO. loss occurred in four steps. In the first two steps, between 25-140 \({}^{\circ}\)C and 140-220 \({}^{\circ}\)C, the mass losses have close values, 0.36% and 0.39%, lower than in the case of GO-ZnO powder. These losses were accompanied by an exothermic maximum, in the second step, at 217 \({}^{\circ}\)C. In the third step, between 220-400 \({}^{\circ}\)C there is a weight loss of 2.19%, accompanied by an endothermic effect at 275 \({}^{\circ}\)C and a small exothermic effect at 350 \({}^{\circ}\)C. The mass loss in this step may be due to pyrolysis of oxygen-containing functional groups and to decomposition of nitrogenous fragments from APTES, respectively [51]. In the fourth step, between 400-600 \({}^{\circ}\)C, the mass loss was 0.64% and may be due to the removal of the GO skeleton and to the removal of oxyethyl groups from APTES [51]. The difference of approximately 0.9% between the total mass losses of GO-ZnO and GO-ZnO-APTES confirms the previous assumption regarding the interaction between the GO-Zn and the silane.
# Multi-Modal Spatio-Temporal Knowledge Graph of Ship Management
## 3 Method
### Multi-Modal Spatio-Temporal Knowledge Graph Storage  
The spatio-temporal knowledge graph constructed in this study is stored using Neo4j, a high-performance NoSQL graph database designed for storing structured data on a network rather than in traditional tables. Neo4j exhibits remarkable scalability, enabling the efficient processing of billions of nodes, relationships, and attributes on a single machine. It can also be horizontally scaled across multiple machines to facilitate parallel processing. By leveraging the node-based storage model and establishing relationships between nodes, we are able to construct intricate nested and interconnected unstructured data structures. This approach effectively caters to the storage requirements of multi-level nested spatio-temporal scene data models.
## 4 Experiments
### Quantitative Evaluation
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Data Set** & \multicolumn{5}{c}{**SNR = 0.5**} & \multicolumn{5}{c}{**SNR = 1.5**} \\ \hline
**MethodMetric** & \(\varepsilon\) & \(F\) & \(E\) & \(S\) & \(\varepsilon\) & \(F\) & \(E\) & \(S\) \\ \hline PUB-SalNet-B**20** & 0.0984 & 0.6347 & 0.6964 & 0.6428 & 0.0793 & 0.7239 & 0.7447 & 0.7124 \\ PUB-SalNet-B**40** & 0.0961 & 0.6396 & 0.6945 & 0.6443 & 0.0766 & 0.7318 & 0.7320 & 0.7107 \\ PUB-SalNet-B**60** & 0.0945 & 0.6437 & 0.6710 & 0.6358 & 0.0774 & 0.7218 & 0.7294 & 0.7032 \\ PUB-SalNet-B**80** & 0.0943 & 0.6543 & **0.7221** & **0.6598** & 0.0783 & 0.7327 & 0.7340 & 0.7065 \\ PUB-SalNet-B**100** & **0.0914** & **0.6573** & 0.7036 & 0.6494 & **0.0762** & **0.7426** & **0.7522** & **0.7209** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The quantitative comparison of parameter sensitivity analysis under four metrics. \(\varepsilon\), \(F\), \(E\) and \(S\) are the same as Table 1. For PUB-SalNet-BX, **X** stands for the initial number of classes to be decreased, as is in Function _Init()_ parameter in Algorithm 1.
###### Abstract  
Temperature is an important factor that affects corrosion potential in rebars. The temperature effect must be removed from the corrosion potential for precise measurement of corrosion rates. To separate the temperature effect from the corrosion potential, in this study rebar specimens were not embedded in concrete but, instead, were placed in an uncontrolled air environment. Gaussian process regression (GPR) was applied to the temperature and the non-corrosion potential data in order to remove the temperature effect from the corrosion potential. The results indicated that the corrosion potential was affected by the temperature. Furthermore, the GPR models of all the experimental cases showed high coefficients of determination (R\({}^{2}\) > 0.90) and low root mean square errors (RMSE < 0.08), meaning that these models had high reliability. The fitted GPR models were used to successfully remove the temperature effect from the corrosion potential. This demonstrates that the GPR method can be appropriately used to assess the temperature effect on rebar corrosion.  
corrosion potential; temperature effect; rebar; Gaussian process regression +
Footnote †: journal: Applied Sciences
# Fast Performance Evaluation for Switched-Capacitor Converter Based on Convolutional Neural Networks
## 4 Efficiency Prediction Based on Convolutional Neural Network
### Convolutional Neural Network Construction  
A convolutional neural network (CNN) is a supervised deep learning algorithm that differs from typical neural networks in using convolution as an activation function. It is superior at dealing with matrix information or image processing, applied here to learning the underlying relationship between the adjacent matrices of SCC topologies and the conversion efficiency \(\eta\). Figure 8 shows the structure of the applied CNN model, which was composed of a feature extraction part and a regression analysis part [21]. The former consisted of convolution layers, while the later consisted of fully connected hidden layers in which a non-linear activation function was employed in the neurons for nonlinear regression. Finally, the prediction efficiency was realized through the output layer. The loss function was defined as the mean absolute percentage error (MAPE), calculated as follows:  
\[\text{MAPE}=\frac{\sum\frac{|A-F|}{A}\times 100}{N} \tag{10}\]  
The hyperparameters of the CNN model were adjusted by observing the convergence in the loss function of the training set. Finally, the Rectified Linear Unit (ReLU) was defined as the activation function of the hidden layer, and Sigmoid was selected as the activation function of the output layer. The number of epochs was set to as 50 and the Adam optimizer was selected. The details of the the CNN model structure are shown in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Input Voltage \(V_{in}\) & 48 V \\ Output Voltage \(V_{out}\) & 12 V \\ Switching frequency \(f_{saw}\) & 1 MHz \\ Rated output power \(P_{out}\) & 120 W \\ ESR of capacitor \(R_{est}\) & 2 m\(\Omega\) \\ \hline \end{tabular}
\end{table}
Table 2: Simulation Parameters.  
Figure 8: Scheme of applied CNN.  
Figure 7: (a) Model for parallel simulation in Matlab and (b) the dataset for the CNN model.
# Theoretical Study of the Wear of a Reduced-Diameter Wheel for Freight Wagons, Based on Its Diameter
###### Abstract
[4; 5; 6; 7] focus on polygonal wheel wear, which is a relevant problem in high-speed railway lines, but they do not include RCF and abrasive-adhesive wear, which cause significant trouble in rail freight transportation. Ref. [8] correlates RCF and abrasive-adhesive wear through analytical models and proves the results experimentally, although it does not give insight into wheel life, not even for ordinary-diameter wheels. Ref. [9] is very specific as it focuses on the wheel wear caused under different braking modes, without covering the wear appearing when the vehicle is not braking. Refs. [10; 11] are very specific as they focus on the effect of wheel diameter difference, i.e., when the wheels in a wheelset do not have the same diameter, and wheel life is not studied (only numerical simulations and experiments for 200,000 km). Refs. [12; 13] focus on the optimization of the reprofiling cycles for wheels, although their diameter is not varied and the strategy is drawn up only for ordinary-diameter ones. Ref. [14] develops a wheel-rail contact model which considers many structural elements of the vehicle and the infrastructure; however, it does not consider reduced-diameter wheels either. To end with, Ref. [15] reviews as many numerical models for rail-wheel contact as possible, while Ref. [16] compares different wear indicators for quantifying wheel wear in rail freight operations; even so, the former does not discuss numerical models for reduced-diameter wheels, and the latter does not apply the wear indicators to them. The primary contribution of this work is addressing the wear issue of reduced-diameter wheels, a topic that has been scarcely explored due to the unique characteristics of these wheels. This study offers an in-depth analysis of the kinematics and dynamics of wheels, wheelsets, and bogies, providing valuable insights into why wheel lifespan varies with diameter and the nature of this dependency. Unlike previous research, this study proposes several realistic scenarios based on rail motorways and varies wheel diameter during the analysis process. By maintaining consistent or equivalent parameters throughout, this study enables meaningful comparisons across different scenarios.
# Voltage Problems on Farms with Agricultural Biogas Plants--A Case Study
## 2 Materials and Methods
\(\mathrm{P_{st}}=1\) is the conventional threshold for the annoyance of light flicker;
- the long-term flicker index \(\mathrm{P_{lt}}\) (an index indicating the annoyance of flickering light over a long period, of the order of a few hours). In most cases, the index  
Figure 1: Diagram of the tested agricultural biogas plant. 1—livestock facility, 2—slurry tank, 3—digester with biogas tank, 4—cogeneration unit, 5—digestate tank, 6—agricultural field. should not exceed 1. The value of the index is determined from successive values of Pst, according to the relation: \[\text{P}_{\text{lt}}=\sqrt{\frac{\sum_{i=1}^{12}\text{P}_{\text{st}_{\text{l}}}^{3 }}{12}}\] (1) in which Pst (i = 1, 2, 3,... 12) are successive values of the short-term flicker indices Pst. * **Voltage asymmetry--**unequal voltage values and/or unequal angles between successive phase voltages. The asymmetry of the system of supply voltages results, among other things, in the appearance of symmetrical components of the opposite order. The parameter describing this condition is the voltage asymmetry factor \(\alpha_{\text{U}\%}\) (opposite voltage asymmetry) [36; 44]: \[\alpha_{\text{U}\%}=\left|\frac{\text{U}_{2}}{\text{U}_{1}}\right|\cdot 100\%\] (2) where: U1, U2--the composite values of the symmetrical components of the consensual and opposite order of the voltage. In most cases, the voltage asymmetry factor should not exceed 2%. * **The distortion of the voltage waveform**, defined by the total harmonic distortion factor (THDU) [45]: \[\text{THD}_{\text{U}}=\frac{\sqrt{\sum_{\text{h}=2}^{\infty}\text{U}_{\text{h} }^{2}}}{\text{U}_{1}}\cdot 100\%\] (3) where: U1--rms value of voltage for the first harmonic, Uh--rms value of voltage for the h-th harmonic, h--order of harmonic. In most cases, the voltage distortion coefficient THDU in low-voltage networks must not exceed 8%. At the agricultural biogas plant under study, electricity was the primary end product, and its production was maintained at the highest possible level. The tests were carried out using a SONEL PQM-701 portable power quality parameter analyser, performing measurements in accordance with Class A of the EN 61000-4-30 standard [46]. The recorder has a legalisation certificate issued by the Research and Calibration Laboratory in Swidnica. The recording of electricity parameters was carried out with 1-minute averaging and recording of the measurement results. The measurement of voltage/current was carried out with an accuracy of \(\pm 0.1\%\) of the rated voltage/current, and the measurement of voltage unbalance with an accuracy of \(\pm 0.15\%\) (absolute error). The maximum error of the harmonic measurement was \(\pm 0.05\%\) Un for voltage and \(\pm 0.15\%\) In for current.
## 3 System Overview
### Problem Description  
In this paper, we consider that \(n\) concurrent video sessions in the network share the same bottleneck link. Each video session consists of a global sender (video server) and a separate receiver (video player). The receiver has an independent ABR algorithm for determining its bitrate.  
The player \(i\in N(N=\{1,\dots,n\})\) starts at a random point in time \(t_{i}\) and requests a random video in the video dataset, where each video is independently encoded at the same set of bitrate levels \(R\). When downloading video, the player \(i\) selects the bitrate level \(r_{i}(k_{i})\in R\) for the \(k_{i}\)_-th_ video block through its own ABR algorithm.