esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Longitudinal Speed Estimator for Four-Wheel Slip in Snowy Conditions
## 2 Materials and Methods
### Longitudinal Speed Estimator of Four-Wheel Drive Slipping Vehicle on Split Roads
#### 2.1.4 Local Vehicle Speed Estimator and Confidence Slip Ratio Calculator  
As mentioned above, the first-layer vehicle speed estimate objective is a relatively-accurate estimate of vehicle speed. Furthermore, there are estimates of longitudinal vehicle speed based on wheel encoder speeds and the acceleration sensor in the first layer. They estimate accurate longitudinal vehicle speeds in certain situations but are inaccurate in other situations. In the case of low wheel slip and correct tire diameter calibration, the wheel encoder speed estimate is accurate. In the case of mild acceleration and large wheel slip, the estimate based on vehicle acceleration is more accurate than the wheel encoder speed estimate. The basic concept of an estimator strategy is to optimize the confidence level between each vehicle speed estimate based on wheel encoder speeds and the vehicle acceleration sensor. It is also the reason that the estimated vehicle speed based on wheel speed and the acceleration sensor are in the first layer.  
Moreover, it is difficult to calculate longitudinal vehicle speed based on certain wheel encoder speeds for large slips, which is a disadvantage of estimating vehicle speed based on wheel encoder speed. Although it is not sufficiently accurate, it is beneficial for use as a reference value to compare with the estimated longitudinal vehicle speed based on vehicle acceleration, and then apply corrections based on the confidence level.  
The local vehicle speed estimator is introduced before the second layer to balance the two longitudinal speed estimates using a weighting coefficient matrix, to provide the most accurate speed possible. Then, slip rate is calculated with the help of the confidence slip ratio calculator, which provides input variables to the final fuzzy algorithm. The estimated vehicle speed (\(V_{E,l}\)) is given by  
\[v_{E,l}=W_{local}\cdot V_{E,w}+(1-W_{local})\cdot V_{E,a}\]  
A specific confidence factor value is selected based on an experiment to obtain the relatively-accurate vehicle speed estimate \(v_{E,l}\). The purpose of \(v_{E,l}\) is to re-calculate a higher-confidence slip rate for the four wheels, \(\lambda_{ij}^{*}\). This factor is a more accurate four-wheel slip rate to balance the confidence between the estimated vehicle speed calculated based on wheel encoder speeds and vehicle acceleration. \(W_{local}\) is weight factor defined in \"local vehicle speed estimator\".
# Internet of Things and Distributed Computing Systems in Business Models
## 4 Theoretical Framework
### IoT and CDS Impact on Business Models
#### 4.4.8 Enhanced Security and Compliance  
IoT and Distributed Computing Systems enhance security and compliance by providing on-time checking and automated response capabilities. IoT machines can detect and report security threats, such as unauthorized access or anomalies in operational data, enabling businesses to respond promptly to potential risks [52]. For instance, IoT sensors in a data center can monitor for unusual temperature fluctuations or unauthorized entry, triggering alerts and initiating corrective actions. Distributed systems support the implementation of advanced security protocols (e.g., access control) to ensure confidential information [80; 81]. In addition, distributed systems can ensure compliance with regulatory requirements by providing accurate and timely data on various aspects of operations. For instance, in the pharmaceutical industry [82], IoT devices can check the conditions under which drugs are stored and transported, while distributed systems analyze this data to ensure compliance with regulations and standards.
## 2 Experimental Approach
### Noise Reduction  
AE signals may be generated from a number of possible sources including background noise, micro-crack generation, or plastic deformation. In order to reduce uncertainties and determine the AE signals corresponding to crack growth, applying noise reduction techniques on the captured data was required. Various de-noising techniques have been proposed to filter AE signals due to extraneous events during crack growth [10; 21; 22].  
To filter out noise from AE signals associated with fatigue crack propagation, the recorded AE data was filtered using the acquisition system's band pass filter (200 kHz-3 MHz). In addition to the band pass filter that restricts the captured signals based on frequency, an amplitude threshold was determined and filtered out low-amplitude background noise. It should be noted that the lack of significant AE activity in the initial stages of fatigue loading makes it more difficult to distinguish background noise from crack-related acoustic events. For each set of materials, a dummy specimen was tested beforehand under the same conditions as the main experiments to determine the background noise. The dummy specimen was simply the first sample from the test specimen batch and had the same geometry and characteristics. The dummy specimen was installed in the testing machine, and the background noise was captured as a cyclic load was applied for a few cycles. Based on this test, the AE detection threshold was set to 45 dB for Al7075-T6 samples to eliminate the background noises. This value was found to be 35 dB for the Ti-6-4 samples.  
It has also been observed that AE events occurring during the loading portion of a cycle are related to crack growth [6; 17; 21]. Therefore, the AE data taken only during the loading portion of each cycle were used for data analysis, while AE events during the unloading portion of the cycle were ignored. In addition, many researchers have assumed that only events occurring close to the maximum or peak load are associated directly with crack growth [12; 21; 22]. So, the filtered AE events were separated for different percentages of the applied load range, and it was determined that the AE counts occurring within the top 20% of peak load showed the closest correlation with crack propagation rates [23].
# Power Optimization in Multi-Tier Heterogeneous Networks Using Genetic Algorithm
## 4 System Model
### Successive Interference Cancellation Technique  
Successive interference cancellation (SIC) is a well-studied technique for canceling interference in wireless networks with room for enhancement. It works by recreating the interfering signals and then removing them from the received combined signal, thereby enhancing the desired signal's signal-to-interference ratio (SIR). The SIC receiver technique shown in Figure 2. starts by decoding the strongest interfering signal while considering other signals as noise. It then regenerates the analog signal from the decoded information and subtracts it from the received combined signal. This results in the desired signal being free from the strongest interfering signal. The receiver then moves on to decoding, regenerating, and canceling the second-strongest interfering signal from the remaining signal, repeating this process until the desired signal can be successfully decoded.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Description** \\ \hline \(\varphi_{R}\) & PPP of relay nodes \\ \(\varphi_{CU}\) & PPP of cellular users \\ \(\varphi_{D}\) & PPP of D2D users \\ \(\gamma\) & path loss exponent \\ \(h,f,g\) & fading factor \\ \(\rho_{AP}\) & transmission power of access point \\ \(\rho_{R}\) & transmission power of relay node \\ \(\rho_{CU}\) & transmission power of cellular user \\ \(x_{AP}\) & transmit signal of access point \\ \(x_{R}\) & transmit signal from relay station \\ \(\lambda_{R}\) & density of relay nodes \\ \(\lambda_{CU}\) & density of cellular user \\ \(\lambda_{D}\) & density of D2D users \\ \(x_{CU}\) & transmit signal at cellular user \\ \(H_{AP_{i},R_{i}}\) & channel between access point to relay node \\ \(H_{R_{j},R_{i}}\) & channel between relay node to relay node \\ \(H_{R_{j},CU_{i}}\) & channel between relay node to cellular user \\ \(H_{CU_{j},R_{i}}\) & channel between cellular user to relay node \\ \(H_{CU_{i},CU_{i}}\) & channel between cellular to cellular user \\ \(H_{D_{i},CU_{j}}\) & channel between device-to-device to cellular user \\ \(y_{R}\) & received signal \\ \(P_{A,R}\) & path loss between access point and relay node \\ \(P_{R,R}\) & path loss between relay nodes \\ \(n_{R}\) & received noise \\ \(P_{R,U}\) & path loss between relay nodes and cellular user \\ \(P_{U,U}\) & path loss between cellular users \\ \(z_{D_{i}}\) & D2D user signal \\ \(SIR_{T}\) & SIR threshold \\ \(I_{CU_{i}}^{(k)}\) & total interference at the cellular user \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notations and their descriptions.
## 2 Materials, Mixture Design, and Experimental Methods
### SEM Analyses  
To investigate the microstructural effects induced by environmental exposure, scanning electron microscopy (SEM) analyses were conducted on the cementitious matrix (cement paste) of the mixtures. Two types of paste samples were prepared: a control paste composed entirely of 100% OPC and a blend containing 15 vol% of CS (85% OPC-15% CS). After 180 days of conditioning in either water or a 5% magnesium sulfate (MgSO4). SEM enables detailed observation of the morphology, distribution, and surface characteristics of hydration products, as well as the identification of degradation features and secondary phases associated with sulfate attack. The analysis focused on detecting microstructural differences attributable to the type of binder and the exposure conditions.
## 3 Control System Design  
### Problem Formulation  
The method of hierarchical control was adopted to design the coordinated control strategy of the vehicle trajectory-tracking and stability, which is divided into the upper controller and lower controller. In the upper-level control, the control objective is to track the ideal vehicle driving state, and to make the lateral deviation and heading deviation in the trajectory tracking approach zero. The upper controller calculates the required vehicle front wheel steering angle and yaw moment according to the vehicle control target, and inputs it into the lower controller. For distributed-drive intelligent vehicles, four in-wheel motors are used for direct drive, which has a higher degree of freedom of maneuvering and torque-response capability, which is conducive to dynamic vehicle control in the process of trajectory tracking. The intelligent vehicle driven by four in-wheel motors is an overdrive system. Therefore, in the lower-level controller, an optimized distribution method of the four wheel and tire forces is designed to achieve the optimization of the power performance of the redundant drive system and meet the requirements of the upper-level controller. The control requirement of the controller realizes the coordinated control of trajectory tracking and vehicle stability. The overall hierarchical coordinated control strategy of unmanned vehicles is shown in Figure 4.  
Figure 4: Structure of the hierarchical control.
## 10 Discussion
A comparison has been made regarding the advantages and disadvantages of both approaches in Table 7.  
Although centralized databases can be simpler to set up, they may lack some of the security and transparency features that blockchain offers. We can say that the _population coverage (missed population)_ issue can be improved in either approach through the centralized infrastructure of thumbprint collection devices, but, again, the decentralization, privacy, security, trust, and transparency of the system are today's public needs. _Ethnic and racial discrimination_ issues in censuses can only be resolved with a blockchain-based system. Blockchain does introduce complexity but provides enhanced security, transparency, and decentralization. Overall, our proposed blockchain-based census system offers robustness, scalability, and improvements in the system as well as promotes transparency and trust in the general public so that they can participate in the census process confidently. The same results cannot be accomplished with any sort of centralized system. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Approach** & **Advantages** & **Disadvantages** \\ \hline \multirow{8}{*}{**Census**} & Simplicity: Centralized databases are typically & Single Point of Failure: A centralized system has a single point of failure. If the central database is \\  & easier to set up and manage compared to & \\  & distributed systems. They require less complex & compromised, all the data \\  & infrastructure. & are at risk. \\ \cline{2-3}  & Performance: In certain scenarios, centralized & Data Integrity: With a single database, ensuring \\  & databases may offer better performance for reading & data integrity can be challenging. Any corruption \\  & and writing operations since all the data are stored & or tampering could have widespread effects. \\ \cline{2-3}  & Control: Centralized systems provide a single & Security Concerns: Centralized databases are more \\  & point of control and authority, making it easier for & susceptible to targeted attacks. Once breached, an \\  & administrators to manage & attacker can potentially gain access to all sensitive \\  & access and permissions. & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Advantages and disadvantages of centralized vs. blockchain-based approaches.
## 5 Results and Discussion
### Carbonation  
One of the main reasons for the destruction of concrete, including reinforced concrete, is carbonation. It is a group of physico-chemical changes in concrete under the influence of long-term exposure to carbon dioxide. During this process, the carbon dioxide in the air reacts with the products of cement hydration. The calcium hydroxide contained in the concrete structure reacts with CO\({}_{2}\) in the presence of moisture, resulting in the formation of calcium carbonate and water. The reaction can be written:  
\[\text{Ca(OH)}_{2}+\text{CO}_{2}\rightarrow\text{CaCO}_{3}+\text{H}_{2}\text{O}, \tag{1}\]  
In the case of steel reinforced concrete, the carbonation process leads to the inertialization of the concrete. Around the reinforcement, due to lowering the pH level, the passivation layer on the steel is destroyed, which leads to corrosion. Increasing the volume of the reinforcement corrosion products causes stresses in the concrete cover, and consequently leads to cracking and the exposure of the reinforcement [77].  
The speed of carbonation advancement depends primarily on the operating conditions of a given element or concrete structure. High humidity and increased CO\({}_{2}\) concentration accelerate the carbonation process. A positive effect is the sealing of the microstructure of the cement matrix. The formed calcium carbonate fills the pores in the matrix and the released water supports the hydration of the non-hydrated cement grains. Therefore, the subsurface concrete layer increases its hardness.  
Elements of road surfaces and tunnels constitute a particularly exposed group of structures to the carbonation process. The surface of concrete elements in these facilities is cyclically affected not only by water or de-icing salt solutions, but also by carbon dioxide with a concentration higher than in the natural environment (CO\({}_{2}\) > 0.03%). Concrete technology requirements include increasing the content of the dust particles in the concrete mix, which is achieved by introducing one of the mineral additives, including fly ash [78; 79; 80].  
The measured depth of carbonation was 1-2 mm, which was undoubtedly influenced by the maturation of the samples in water. A lower depth of carbonation was found in the case of concrete with the addition of fly ash from sewage sludge, which can be explained by its low permeability and, thus, limited range of carbonation. However, the microstructure of concrete with cement, which has an addition, does not protect against carbonation, the speed of which is higher than in the case of concrete made of Portland cement [78; 80]. Importantly, the rate of carbonation changes over time; therefore, the research that has already started needs to be continued over time, along with the development of a model for this process of creating a specific concrete.
## 3 VLC for Underwater Communication
### Key Components and System Design of UVLC
#### 3.3.3 System Architecture  
The basic UVLC design is shown in Figure 4. On the transmitter side, data are generated from the source and fed into the modulator. Data modulate onto the carrier wave at the modulator. A light source converts an electrical signal into an optical signal and is transmitted through the water channel. On the receiver end, the photodetector converts the optical signal back into an electrical signal. The electrical signal is fed to a signal processor where the signal is amplified and filtered as it is suitable for demodulation.  
Table 1 represents a comprehensive comparison of several system architectures including the transmitter and its wavelength, optical power at the transmitter end, receiver and its sensitivity level, modulation scheme, and data rate.  
Figure 4: Basic UVLC design.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline
**Year** & **Wavelength** & **Optical Power** & **Receiver** & **Modulation Scheme** & **Distance (m)** & **Receiver Sensitivity** & **Data Rate** & **Reference** \\ \hline
2019 & 520 nm LD & 7.25 mW & APD & OOK and Nonlinear & 100 & \(-\)30.4 dBm/ & 100 Mbps/500 Mbps & [28] \\  & & & & Equalization & & \(-\)24 dBm & 100 Mbps/500 Mbps & [28] \\ \hline
2020 & 450 nm LD & 2.4 mW & MPPC & OOK & 100/50 & NA & 8.39 Mbps/ & [29] \\  & & & & & & & 16.78 Mbps & \\ \hline
2020 & 450 nm LD & 10 mW &
\begin{tabular}{c} Silicon \\ Photomultiplier \\ (SiPM) \\ \end{tabular} & OOK and Decision & 40 & \(-\)43 dBm & 1 Gbps & [30] \\  & & & & Equalization & & & \\ \hline
2020 & 450 nm GaN LD & 285.5 mW & PIN PD & Probabilistic Shaping & 1.2 & NA & 120.09 Gbps & [31] \\  & & & & bit loading & & & \\ \hline
2020 & 450 nm Micro-LED & \(<\)20 mW & PIN PD & QAM-OFDM & 1.5 & NA & 4.92 Gbps & [32] \\  & & & & & & \\ \hline
2020 & 450 nm Micro-LED & \(<\)20 mW & PIN PD & QAM-OFDM & 3 & NA & 3.22 Gbps & [32] \\  & & & & & & \\ \hline
2020 & 450 nm Micro-LED & \(<\)20 mW & PIN PD & QAM-OFDM & 4.5 & NA & 3.4 Gbps & [32] \\  & & & & & & \\ \hline
2020 & 520 nm LD & NA & APD & 32-QAM & 56 & NA & 3.31 Gbps & [33] \\ \hline
2021 & 450 nm LD & 16.18 mW & APD &
\begin{tabular}{c} Discrete Fourier \\ Transform Spread \\ Discrete Multi-Tone \\ \end{tabular} & 50 & \(-\)11.2 dBm & 5 Gbps & [34] \\ \hline
2021 & 450 nm LD & 293.1 mW & PMT & PAM4 and Trellis & 150 & \(-\)29.8 dBm & 500 Mbps & [35] \\ \hline
2021 & 450 nm LD & NA & APD & DFT-S DMT & 50 & NA & 5 Gbps & [34] \\ \hline  & & &
\begin{tabular}{c} Diversity Reception \\ and Nonlinear \\ Decision Feedback \\ Equalization \\ \end{tabular} & 55 & \(-\)49.23 dBm/ & 1 Gbps/2 Gbps & [36] \\ \end{tabular}
\end{table}
Table 1: Comparison of transmitter and receiver of UWOC systems in recent works.
# Data Conversion Strategies for Effective Aviation Technical Support as a Service
## 3 Results
### Life Cycle Cost-Based Decision-Making Model  
The research question focuses on determining which system architecture, centralized or decentralized, is more cost-effective over its life cycle, considering initial investment, operational costs, and their dynamics over time. Below is a generalized mathematical model to address this question. The main notations are presented in the Table 1.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Notations** & **Description** \\ \hline \(U_{1}\) & Centralized architecture \\ \(U_{2}\) & Decentralized architecture \\ \(t\) & Time in years \\ \(S\) & Scalability factor which represents the system’s workload or capacity (e.g., number of users, data size, or transactions) \\ \(C_{init}\) & The fixed cost incurred during system implementation \\ \(C_{op}(t,S)\) & Time-dependent costs for operating the system, influenced by scalability \\ \(\gamma\) & Scalability cost coefficient for centralized systems \\ \(\beta\) & Growth rate of operational costs in decentralized systems \\ \(C_{init}^{(1)}\), \(C_{init}^{(2)}\) & Initial costs for centralized and decentralized architectures, respectively \\ \(\epsilon_{1},\epsilon_{2}\) & Base operational costs for centralized and decentralized systems, respectively \\ \(C_{op}^{(1)}\), \(C_{op}^{(2)}\) & Operational costs for centralized and decentralized architectures, respectively \\ \(k\) & Impact of scalability on reliability in decentralized systems \\ \(T_{max}\) & Maximum allowable response time \\ \(T_{1},T_{2}^{base}\) & Base response times for centralized and decentralized systems \\ \(\rho\) & Response time degradation rate in decentralized systems \\ \(\mu_{1},\mu_{2}^{base}\) & Base complexity factors for centralized and decentralized systems \\ \(\lambda\) & Complexity growth rate for decentralized systems \\ \(\varepsilon_{1},\epsilon_{2}\) & Environmental impact factors for centralized and decentralized systems \\ \(\psi\) & Time-dependent environmental impact factor for centralized systems \\ \(\phi_{max},\chi_{max}\) & Maximum values for scalability and sustainability metrics used for normalization \\ \(B_{1},B_{2}\) & Time-independent constants for security represent the fixed security scores \\  & assigned to the centralized and decentralized architectures, respectively \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main notations of the model.