esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Review of Empirical Models for Extinction and Backscattering
#### 2.3.4 Empirical Models for Dust  
The literature search yielded no automotive-specific lidar study regarding the impact of dust on the laser beam intensity. However, dust storms can have a substantial influence on optical communication systems that are based on laser systems. For this technical application, numerous studies during sand storms were performed [28; 29]. The common measure for dust is the total suspended particle mass [\(\mathrm{\SIUnitSymbolMicro m}\)3] (TSP). The work in [30] presented several models, which put the TSP in relation to the visibility during a sand storm. In Figure 7, the models are represented with data from [31; 32; 33], showing a good correlation between unrelated reference sand storm models. Depending on the source of the sand and the mineral composition, the particle distributions can vary a lot, which was also discussed by [34], who showed that the sand consists mostly of Quartz and that the particle distribution can have several local maxima. This is in contrast to the other weather phenomena, where the particle distribution follows a log-normal distribution function with only a single maximum.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Coefficient** & **Author** & **Snow Type** & **Equation** \\ \hline \multirow{4}{*}{\(\alpha\)} & [26] & Dry & \((5.42\cdot 10^{-5}\lambda_{\mathrm{nm}}+5.5)\ R^{1.38}\ \ [\mathrm{db/km}]\) \\  & [26] & Wet & \((1.02\cdot 10^{-4}\lambda_{\mathrm{nm}}+3.79)\ R^{0.72}\ \ [\mathrm{db/km}]\) \\  & [27] & Dry & \(\alpha=17.30R\ \ [\mathrm{db/km}]\) \\  & [27] & Wet & \(\alpha=1.39R\ \ [\mathrm{db/km}]\) \\ \hline \end{tabular}
\begin{tabular}{c c c} \hline \(\beta\) a & n/a & n/a & n/a \\ \hline \end{tabular}
\end{table}
Table 3: Comparison of different coefficient models for snow.  
Figure 6: Comparison of the extinction coefficients during snowfall.
# High-Precision Roller Supported by Active Magnetic Bearings
## 3 Design of Magnetic Bearing and Roller System  
Figure 4 shows the configuration of the precision roller system with magnetic bearings. The radial magnetic bearings applied to the precision roller for printing must be able to support the printing force as well as the mass of the roller, because the mass of the roller acts as a radial load. The mass of the roller is 30 kg and the maximum printing force applied to the roller during printing was set to 300 N. Therefore, the magnetic bearings at must be capable of generating a force of at least 600 N. Based on this, the radial magnetic bearings were designed to generate a force each exceeding 300 N. In the case of the axial force, the thrust generated between the core and rotor magnet in the motor and the axial component of the printing force caused by the vibration and misalignment occurring during the rotation of the roller act as axial disturbances. Since these forces are not large, the required force of the thrust magnetic bearing was set to 100 N. The roller, rotor of the motor, roller printing section, and shaft core were designed to be integrated as one part for ease of control and complete levitation. The design parameters of the radial and axial magnetic bearings were selected based on the magnetic circuit and the magnetic force equation, and they were examined using Maxwell, a finite element electromagnetic field analysis software program [20; 21]. The dimensions of the magnetic bearings were selected based on the roller used for the test, but these dimensions may need to be increased depending on the specific application and roller used because larger bearings will be required to support heavier industrial rollers. This process is omitted from this paper for brevity. The air gap of \(\pm\)0.2 mm in which the roller can move was designed to implement the automatic alignment function of the roller. This movement range is sufficient for most production processes where the adjustment is fairly small, but the current or the magnetic bearing size might need to be increased to generate the same magnetic force for larger movement ranges. The specifications of the precision roller and magnetic bearings proposed in this study are listed in Table 1.  
Figure 3: Basic concept of automatic alignment of roller.  
The prototypes of the magnetic bearings and precision roller equipment for printing were fabricated according to the design parameters shown previously. Figure 5a,b shows the fabricated radial and thrust magnetic bearings, and Figure 5c presents the precision roller. For roller rotation, a direct drive motor (TBMS-12913, Kollmorgen, Radford, VA, United States) was used.
## 3 Advancements in Imaging and Augmented Reality (AR)
### Three-Dimensional (3D) Virtual Models for Surgical Planning  
Traditional two-dimensional imaging modalities, such as CT and MRI, while fundamental for diagnosis and staging, have limitations in spatial perception, and do not adequately represent the dynamic complexity of intraoperative anatomy. To address this, patient-specific three-dimensional virtual models (3 DVMs) have been developed and integrated into robotic workflows. These 3 DVMs are reconstructed from high-resolution cross-sectional images and allow the surgeon to interact with an anatomically faithful representation of the organ, tumor, and surrounding structures (Figure 2). In the context of robot-assisted partial nephrectomy (RAPN), 3 DVMs enable a detailed evaluation of tumor size, location, endophyticity, and its relationship to vessels and calves. Studies have shown that the preoperative use of 3 DVMs improves an understanding of vascular anatomy, which in turn facilitates selective or super-selective arterial clamping, a critical strategy for minimizing warm ischemia and preserving renal function [11]. A recent study by [PERSON] et al. introduced perfusion-region 3 DVMs (PR-3 DVMs), which use Voronoi-based mathematical algorithms to delineate renal perfusion territories and guide tailored clamping strategies [12]. Their prospective analysis of 103 RAPN cases demonstrated a high concordance between planned and executed vascular management, with significantly better functional outcomes in terms of renal scintigraphy parameters when selective or super-selective clamping was employed.
# Positioning Information Privacy in Intelligent Transportation Systems:
###### Abstract  
Today, the Intelligent Transportation Systems (ITS) are already in deep integration phase all over the world. One of the most significant enablers for ITS are vehicle positioning and tracking techniques. Worldwide integration of ITS employing Dedicated Short Range Communications (DSRC) and European standard for vehicular communication, known as ETSI ITS-G5, brings a variety of options to improve the positioning in areas where GPS connectivity is lacking precision. Utilization of the ready infrastructure, next-generation cellular 5G networks, and surrounding electronic devices together with conventional positioning techniques could become the solution to improve the overall ITS operation in vehicle-to-everything (V2X) communication scenario. Nonetheless, effective and secure communication protocols between the vehicle and roadside units should be both analyzed and improved in terms of potential attacks on the transmitted positioning-related data. In particular, said information might be misused or stolen at the infrastructure side conventionally assumed to be trusted. In this paper, we first survey different methods of vehicle positioning, which is followed by an overview of potential attacks on ITS systems. Next, we propose potential improvements allowing mutual authentication between the vehicle and infrastructure aiming at improving positioning data privacy. Finally, we propose a vision on the development and standardization aspects of such systems.  
Intelligent Transportation Systems; positioning; data privacy; authentication; GDPR Article
# Understanding Consumer Acceptance for Blockchain-Based Digital Payment Systems in Bhutan
## 3 Theoretical Framework and Hypothesis Development
### Effort Expectancy  
The level of effort expectancy (EE), the degree of technology's perceived ease of use, determines adoption [53; 93]. It describes how much users think using a system will not be challenging. When dealing with technology as complex as blockchain, there is a need for easy-to-use interfaces, efficient system designs and support systems to drive adoption. Usability was found to be one of the largest drivers of technology adoption [102; 103]. Any system that cuts down on the amount of mental effort required and makes tasks easier  
Figure 1: Theoretical model.  
would be embraced more widely [104]. Successful adoption of blockchain-based digital payments is contingent upon reducing perceived technical complexity through ease of use [105], localized functionality suited to Bhutanese cultural and linguistic conditions, and well-established training materials. Making the blockchain easier to use, for example, by using transactions and wallets, can make blockchain accessible to users of varying digital skills. The effort expectancy also involves addressing infrastructural and accessibility problems [106]. These efforts are particularly important in rural Bhutan, where digital resources and expertise may be lacking. If it simplifies the learning curve and makes the user experience seamless, blockchain will prove to be a promising and practical digital payment alternative in Bhutan's varied demographics. Therefore, we propose the following hypothesis:  
**H2**: _Effort expectation positively affects consumer acceptance of blockchain adoption for digital payments in Bhutan._
# Micro-Satellite Systems Design, Integration, and Flight
## 4 Attitude Control System (ACS) Development
### Attitude Control
#### 4.3.1 Passive Techniques  
The CubeSat had limited power, so it used passive methods to control its attitude. The CubeSat's passive ACS worked by adjusting the mass distribution. Mass distribution had two benefits: it kept the CubeSat balanced on each axis to prevent tilting or shaking, and it made the \(z\) axis the most dominant principal axis (requirement 2.0 in Table 5). Lead weights were arranged in a square shape in the XY plane. They were in the light sail compartment opposite the electronics. The lead was placed there to make the CubeSat weight even along the \(z\) axis. Steel weights were also added, pointing along the \(z\) axis direction. They went around the edge of the CubeSat, up and down the inside of each of the side-facing panels. Having both the lead and steel weights on the outside of the CubeSat made the principal moment of the inertia axis line up with the geometric \(z\) axis. Figure 31 shows how the mass was placed.
###### Abstract  
Mobile users can access vital real-time services through wireless paradigms like software-defined network (SDN) topologies and the Internet of Things. Point-of-contact-based infrastructures and dynamic user densities increase resource access and service-sharing concurrency. Thus, controlling power consumption and network and device congestion becomes a major issue for SDN-based IoT applications. This paper uses the Controlled Service Scheduling Scheme (CS3) to address the challenge of simultaneous scheduling and power allocation. The suggested approach uses deep recurrent learning and probabilistic balancing for power allocation and service distribution during user-centric concurrent sharing intervals. The SDN control plane decides how much power to use for service delivery while forecasting user service demands directs the scheduling interval allocation. Power management is under the control plane of the SDN, whereas service distribution is under the data plane. Power-to-service requirements are evaluated probabilistically, and updates for both aircraft are obtained via the deep learning model. This allocation serves as the basis for training the learning model to alleviate power deficits across succeeding intervals. The simulation experiments are modeled using the Contiki Cooja simulator, where 200 mobile users are placed. The proposed plan delivers a 14.9% high-service distribution for various users, 18.29% less delay, 13.34% less failure, 5.54% less downtime, and 18.68% less power consumption.  
Internet of Things; software-defined networks; real-time services; concurrency; power allocation; deep learning; service scheduling
## 3 Simulation Results and Discussion
### Optimization of the Combination of the Gate Oxide and Back Oxide Materials  
The dependence of lattice temperature and DIBL on the number of combinations that can be seen from Figures 6 and 7 for a gate length of 20 nm is shown in Figure 9. It is clear from Figure 9 that the SHE and DIBL effects compete for the combination of the oxide materials. As already discussed above, the electrical and thermal properties of the back oxide material, namely dielectric constant and thermal conductivity, are mainly responsible for the SHE and DIBL effect in the considered 2D MoS\({}_{2}\) MOSFETs.  
Achieving a small DIBL effect requires a low dielectric constant back oxide material, while reducing the gate temperature in the transistor channel requires a high thermal conductivity back oxide material. We have considered two popular and commonly used back oxide materials: SiO\({}_{2}\) and HfO\({}_{2}\). When the back oxide material is SiO\({}_{2}\), it has a small dielectric constant, resulting in a decrease in DIBL; at the same time, it also has low thermal conductivity, which leads to high lattice temperature in the transistor channel. Conversely, as a back oxide material, HfO\({}_{2}\) material has a high dielectric constant, resulting in a high DIBL effect, while also exhibiting a relatively high thermal conductivity, resulting in a reduction in the lattice temperature in the transistor channel.  
From our results, it is possible to define the range of electrical and thermal parameters of oxide materials that allow to achieve optimal immunity to SHE as well as the DIBL effect. From Figure 7, it can be concluded that using the BOX material with a relative dielectric  
Figure 8: Equivalent circuit describing the capacitive coupling between the channel and the device electrodes in MOSFET.  
Figure 9: DIBL effect and SHE dependence of combinations of gate oxide and back oxide materials in MOSFET.  
constant in the range between 3.9 and 25 and a thermal conductivity between 1.4 and 2.3 W/Km is a perfect choice to achieve (or for achieving) optimal immunity to degradation effects, such as SHE and DIBL effects in 2D MoS2-based MOSFETs. The gate oxide material has practically no significant influence on SHE as well as on the DIBL effect.
# Empirical Performance Models of MAC Protocols for Cooperative Platooning Applications
## 4 Empirical Models
### Number of Lanes and Transmit Power of Followers
\begin{table}
\begin{tabular}{l l c c c c c c} \hline \hline
**Parameter** & **Protocol** & \multicolumn{3}{c}{**Collisions Metric**} & \multicolumn{3}{c}{**Busy Time Ratio Metric**} \\ \hline \multirow{4}{*}{Heterogeneous} & \multicolumn{3}{c}{Exponential Model} & \multicolumn{3}{c}{Log Linear Model} \\  & & \(\alpha\) & \(\beta\) & MSE & \(\beta_{0}\) & \(\beta_{1}\) & MSE \\ \cline{2-7}  & RA-TDMAp & 0.138 & 0.082 & 7.043 & \(-0.520\) & 0.168 & 2.106\(\times 10^{-5}\) \\ \cline{2-7}  & PLEXE-slotted & 0.023 & 0.120 & 17.792 & \(-1.112\) & 0.363 & \(5.223\times 10^{-5}\) \\  & CSMA/CA & 0.816 & 0.074 & 84.502 & \(-1.179\) & 0.379 & \(7.765\times 10^{-5}\) \\ \hline \multirow{4}{*}{Lanes} & \multicolumn{3}{c}{Gaussian Function Model} & \multicolumn{3}{c}{Log Linear Model} \\  & & \(\alpha\) & \(\beta\) & \(\gamma\) & MSE & \(\beta_{0}\) & \(\beta_{1}\) & MSE \\ \cline{2-7}  & RA-TDMAp & 4.806\(\times 10^{3}\) & 73.502 & 32.26 & 1.688 & \(-0.391\) & 0.139 & \(2.916\times 10^{-5}\) \\  & PLEXE-slotted & \(1.126\times 10^{4}\) & 76.66 & 33.74 & 17.195 & \(-0.758\) & 0.283 & \(1.540\times 10^{-4}\) \\  & CSMA/CA & \(1.331\times 10^{4}\) & 79.57 & 34.11 & 37.398 & \(-0.726\) & \(0.275\) & \(1.196\times 10^{-4}\) \\ \hline \multirow{4}{*}{Tx Power} & \multicolumn{3}{c}{Gaussian Function Model} & \multicolumn{3}{c}{Log Linear Model} \\  & & \(\alpha\) & \(\beta\) & \(\gamma\) & MSE & \(\beta_{0}\) & \(\beta_{1}\) & MSE \\ \cline{2-7}  & RA-TDMAp & \(5.300\times 10^{3}\) & 80.66 & 35.0 & 4.421 & \(-0.562\) & 0.180 & \(1.580\times 10^{-5}\) \\  & PLEXE-slotted & \(1.530\times 10^{4}\) & 85.51 & 44.85 & 51.175 & \(-0.945\) & 0.330 & \(1.385\times 10^{-4}\) \\  & CSMA/CA & \(1.523\times 10^{4}\) & 84.17 & 41.84 & 96.195 & \(-0.934\) & 0.326 & \(2.714\times 10^{-5}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Description of fitted models. Figure 10: Network metrics for RF neighbours varying number of lanes. Figure 11: Network metrics for RF neighbours varying the power followers transmit.
## 3 Results
### Best Suited Model Selection for Maize Grain Yield Prediction
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Feature Set** & **Model** & **MAE** & **MSE** & **RMSE** & \(R^{2}\) \\ \hline All Features & Decision Tree & 0.987037 & 4.735048 & 2.176017 & 0.324520 \\ Permutation Importance & Decision Tree & 0.996296 & 4.731600 & 2.175224 & 0.325012 \\ Correlation Coefficient & Passive Aggressive & 1.620354 & 4.476983 & 2.115888 & 0.361334 \\ LASSO Coefficients & Decision Tree & 0.940370 & 4.000685 & 2.000171 & 0.429281 \\ SHAP Values & Decision Tree & 0.748519 & 3.459833 & 1.860063 & 0.506436 \\ SHAP Values & XGBoost & 0.763426 & 2.624211 & 1.619942 & 0.625642 \\ Correlation Coefficient & ElasticNet & 1.237896 & 2.548584 & 1.596428 & 0.636431 \\ LASSO Coefficients & ElasticNet & 1.213348 & 2.533981 & 1.591848 & 0.638514 \\ RFE with CV & Decision Tree & 0.878889 & 2.498648 & 1.580711 & 0.643554 \\ LASSO Coefficients & Gradient Boosting & 0.841662 & 2.399104 & 1.548904 & 0.657755 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Top 10 worst performances of fourteen ML model across six distinct methods of feature engineering.
## 3 Results and Discussion
### Comparison of Real-Time Sampling Devices
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Monitoring Site, Date, and** **Environmental Condition**} & \multirow{2}{*}{**Sampling Device**} & \multirow{2}{*}{**RE\%**} & \multicolumn{2}{c}{**PM\({}_{2.5}\) Daily Concentrations (\(\mu\)g/m3)**} \\ \cline{3-5}  & & & **AVERAGE** & **MIN** & **MAX** \\ \hline \multirow{5}{*}{Indoor quiet 12 March 2018} & AirVisual Pro & 13.0 & 3.8 & 1.0 & 6.0 \\  & KORA & 1.4 & 3.6 & 1.9 & 9.7 \\  & ELPI+ & 1.0 & 4.9 & 1.7 & 15.6 \\  & AirVisual Pro & 7.6 & 6.6 & 3.0 & 12.0 \\  & KORA & 0.9 & 5.9 & 3.7 & 9.5 \\  & ELPI+ & 1.0 & 9.0 & 0.00004 & 26.2 \\  & AirVisual Pro & 5.5 & 9.1 & 5.0 & 15.0 \\  & KORA & 0.6 & 8.0 & 4.3 & 12.2 \\  & ELPI+ & 1.0 & 11.7 & 0.1 & 21.7 \\  & AirVisual Pro & 2.6 & 19.4 & 7.0 & 46.0 \\  & KORA & 0.4 & 14.1 & 6.5 & 26.1 \\  & ELPI+ & 1.0 & 19.8 & 0.0003 & 43.5 \\  & AirVisual Pro & 5.8 & 8.6 & 3.0 & 20.0 \\  & KORA & 0.6 & 7.9 & 3.8 & 19.5 \\  & ELPI+ & 1.0 & 10 & 0.3 & 43 \\  & AirVisual Pro & 11.0 & 4.5 & 1.0 & 11.0 \\  & KORA & 1.1 & 4.6 & 0.7 & 9.9 \\  & ELPI+ & 1.0 & 5.3 & 0.0003 & 14.6 \\  & AirVisual Pro & 3.1 & 16.3 & 4.0 & 29.0 \\  & KORA & 0.4 & 13.4 & 5.8 & 21.7 \\  & ELPI+ & 1.0 & 19.8 & 0.0003 & 51.3 \\  & AirVisual Pro & 3.3 & 15.2 & 9.0 & 29.0 \\  & KORA & 0.4 & 12.5 & 8.6 & 24.1 \\  & ELPI+ & 1.0 & 18.9 & 0.0003 & 49.0 \\  & AirVisualPro & 2.1 & 23.3 & 11.0 & 43.0 \\  & KORA & 0.3 & 19.7 & 10.2 & 37.3 \\  & ELPI+ & 1.0 & 26.8 & 0.0003 & 48.0 \\  & AirVisual Pro & 4.6 & 10.8 & 2.0 & 16.0 \\  & KORA & 0.6 & 8.7 & 6.3 & 15.2 \\  & ELPI+ & 1.0 & 13.4 & 0.007 & 22.8 \\  & AirVisual Pro & 1.1 & 45.2 & 13.0 & 197.6 \\ Outdoors 26 March 2018 & KORA & 0.1 & 38.5 & 32.8 & 81.9 \\  & ELPI+ & 1.0 & 54.4 & 38.8 & 454.4 \\  & AirVisual Pro & 1.1 & 47.3 & 34.0 & 78.3 \\  & KORA & 0.1 & 39.0 & 29.8 & 64.4 \\  & ELPI+ & 1.0 & 51.1 & 35.5 & 301.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: PM\({}_{2.5}\) daily minimum, maximum, and average concentrations measured by real-time sampling devices. Minimum and maximum concentrations, and the relative percent error (RE%) are also reported. For average PM\({}_{2.5}\) concentrations lower than 45 \(\mathrm{\SIUnitSymbolMicro g/m^{3}}\), the difference between ELIPI+ and KORA/AirVisual Pro concentrations suggested a good agreement between the two monitoring systems and a good representativeness of optical sensors to indirectly evaluate PM\({}_{2.5}\) concentrations well beyond the yearly limit value for the protection of human health of 20 \(\mathrm{\SIUnitSymbolMicro g/m^{3}}\)[1]. The Bland-Altman plot that compared KORA and AirVisual Pro data showed a good alignment for all the concentrations range, evidencing that they are based on the same physical principle (optical particle detection).
## 4 Evaluation of the Proposed Algorithm
### The Number of Alive Nodes  
In the first scenario, the base station location is at (0,0); Figure 3 shows that the genetic and LEACH (traditional) algorithms lose almost 70% of the nodes after 500 rounds and 800 rounds, respectively, whereas the COY algorithm at 1100 rounds and (AO, and HHO) algorithms lose the same amount at 1250 rounds. In this case, the proposed AO algorithm has the best loss rate at 1250 rounds.  
In the second scenario, the BS location is at (50,50); Figure 4 shows that the genetics and LEACH (traditional) algorithms lose 70% of the nodes after 500 rounds and 1700 rounds respectively, whereas the COY algorithm at 1800 rounds and (AO and HHO) algorithms lose the same amount at 2000 rounds. In this case, the AO algorithm has the best loss rate at 2000 rounds.  
In the third scenario, the BS location is at (100,100); Figure 5 shows that the genetic and LEACH (traditional) algorithms lose 70% of the nodes after approximately 500 rounds, whereas COY and AO algorithms at 1200 rounds and the HHO algorithm lose the same  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Description** & **Parameters** & **Value** \\ \hline Network Area & Dx \(\times\) Dy & 100 \(\times\) 100 \\ Total number of Sensor Nodes & Num & 100 \\ Total number of cluster heads & m & 10 \\ Max. number of iterations & max.iter & 4000 \\ Communication range & c\_r & 20 \\ Sensor node energy & energy & 2 \\ The length of data bits & k & 4000 \\ Sink node coordinate & Sensor\_x, Sensor\_y & 0, 0 \\ The energy consumed by the transmitter and the receiver & Eelec & 50 \(\times\) power (10,\(-\)9) \\ The energy consumed in the transmitter amplifier & Eamp & 100 \(\times\) power(10,\(-\)12) \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of simulation parameters for the proposed algorithm.  
Figure 3: Number of alive nodes in case of scenario 1: BS at (0,0).  
amount at approximately 1250 rounds. In this case, the proposed AO algorithm has a normal loss rate at 1200 rounds.