esa-sceva/satcom-chunk-collection huggingface dataset:
# The Evolution and Optimization Strategies of a PBFT Consensus Algorithm for Consortium Blockchains
## 3 An Introduction to Mainstream Consensus Algorithms
### The Mainstream Consensus Algorithms of Private Blockchains
#### 3.2.1 Paxos
In terms of performance optimization, efforts have been made to reduce message complexity (such as in three-message consensus), introduce random backoff mechanisms, or relax quorum constraints. These strategies help lower synchronization overhead and improve the system's tolerance to network dynamics and adversarial environments. For scenario adaptation, improvements have been achieved through modular design (such as hierarchical state machine replication), hybrid node role support, or cross-protocol performance comparison, which extend the applicability of Paxos in wide-area networks, educational settings, and heterogeneous networks. Overall, these advancements highlight a common evolutionary trajectory for Paxos: while maintaining basic fault tolerance, the protocol balances the demands of theoretical correctness, real-time efficiency, and scenario specificity. This is accomplished through dynamic adjustments in leader mechanisms, message propagation strategies, or validation rules, thus driving the consensus algorithm toward a greater generalization and robustness. However, the practical optimization of Paxos is often challenging for large-scale distributed systems due to its complex implementation and high message transmission overhead. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author** & **Year** & **Improvement Method** & **Core Mechanism** & **Limitations** \\ \hline [PERSON] [41] & 2020 & Paxos and Raft & \begin{tabular}{c} Compares the fault tolerance mechanisms, \\ message complexity, and leader election \\ strategies of Paxos and Raft. \\ \end{tabular} & Provides theoretical analysis but does not introduce new optimizations or solutions. \\ \hline [PERSON] [42] & 2020 & \begin{tabular}{c} Structured Sequence \\ Paxos Lecture \\ \end{tabular} & \
\begin{tabular}{c} Modularly decomposes the principles of \\ Paxos, reducing the learning threshold with \\ code examples. \\ \end{tabular} & Focuses on education rather than improving \\ \hline [PERSON] [43] & 2020 & Heterogeneous Paxos &
\begin{tabular}{c} Achieves consensus with three message \\ rounds, supporting hybrid node roles and \\ network models. \\ \end{tabular} & Increased complexity due to role differentiation \\ \hline \hline \end{tabular}
\end{table} 
Table 5: A comparison of Paxos improvements.
# Analysis of Hydration-Mechanical-Durability Properties of Metakaolin Blended Concrete
## 3 Property Evaluations of MK Blended Concrete
### Results and Discussion
#### 3.2.5 Discussion about Reaction Degree and Efficiency Factor  
The efficiency factor is a widely used indicator for evaluating the properties of concrete containing supplementary cementitious materials (SCMs). Although the efficiency factor is convenient, it has some limitations. For concrete with various proportions of mix, at various curing ages, the efficiency factors are different. The efficiency factors for mechanical properties and durability are also different. Therefore, it is difficult to evaluate the chemical aspects, such as the calcium hydroxide content, by using the efficiency factor in the SCMs blended concrete. Comparatively, in this study, we did not use an efficiency factor. Instead, we used the reaction degree of the SCMs to estimate the properties evolution of MK blended concrete. The coefficients for the hydration model (Section 3.2), strength model (Section 3.3), and chloride penetrability model (Section 3.4) were constants. When the SCM replacement levels, water to binder ratios, and curing ages were changed, these coefficients did not vary. The compositions of reaction products, such as calcium hydroxide content, could be evaluated using the reaction degrees of binders (Section 3.2). Summarily, regarding a multiple properties evaluation of blended concrete, such as the chemical-mechanical-durability properties, the reaction degree was much more effective than the efficiency factor given that the reaction degree can serve as a link between the macro properties and micro compositions of concrete.
# Accuracy Analysis of GNSS Hourly Ultra-Rapid Orbit and Clock Products from SHAO AC of iGMAS
## 2 Data, Methods, and Strategy
The stations, which had less data or showed bad quality, would be rejected. The receiver type of the station could be updated if it was found changed. The initial orbit and clock were calculated from qualified navigation files. The orbit with five SRPs, satellite and receiver clock, station coordinate, ERP, ambiguity, ISB/IFB between systems, and ZTD, were estimated by four threads, which were running parallel. The lease square (LSQ) adjustment procedure was iterated if the residuals were accessed beyond the requirement of the threshold value. The satellites or stations with larger residuals were excluded by a quality check procedure for controlling the quality of the products. The hourly products of orbit and clock for GPS/GLONASS/Galileo/BDS were composited together after quality checks. It should be mentioned that the 6-hourly orbit and clock products were generated routinely in the iGMAS AC at SHAO, while the 1-hourly products were running by post-processing batch mode in this study.
# Enhancing Reactive Ad Hoc Routing Protocols with Trust
## 5 Experiments and Results
### Dependence of the TARA Method Performance on the Destination Only Flag  
Figure 14 shows how the setting of the _destination only_ flag influences construction of paths. Blue nodes have \(PDR=1\) and red nodes have \(PDR<1\). The size of the red nodes grows with decreasing of their \(PDR\) value. The value of \(PDR\) is written next to the node ID. Paths in Figure 14 were constructed with the default configuration and Exponential Delay function with \(BASE=8\) and \(MD=512\). Grey links are not used. Links are thicker when  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Flag**} & \multicolumn{2}{c}{**Parameters**} & \multicolumn{3}{c}{**Results**} \\  & _BASE_ & _MD_ & \(PDR_{\text{avg}}\) & \(\delta_{PDR_{\text{avg}}}\) & \(RDD_{\text{avg}}\) & \(\delta_{RDD_{\text{avg}}}\) \\ \hline \multirow{6}{*}{DFT} & 4 & 8 & 0.55 & 55\% & 10.38 & 69\% \\  & 4 & 32 & 0.59 & 69\% & 19.26 & 214\% \\  & 8 & 16 & 0.57 & 62\% & 12.29 & 100\% \\  & 16 & 16 & 0.56 & 59\% & 11.26 & 84\% \\  & 16 & 32 & 0.59 & 66\% & 14.94 & 144\% \\  & 32 & 8 & 0.52 & 47\% & 8.71 & 42\% \\ \hline \multirow{6}{*}{DFF} & 4 & 32 & 0.58 & 71\% & 3.56 & 151\% \\  & 8 & 16 & 0.56 & 66\% & 2.35 & 66\% \\ \cline{1-1}  & 8 & 64 & 0.58 & 73\% & 4.73 & 233\% \\ \cline{1-1}  & 16 & 16 & 0.55 & 63\% & 2.18 & 53\% \\ \cline{1-1}  & 32 & 8 & 0.49 & 46\% & 1.78 & 25\% \\ \cline{1-1}  & 1024 & 8 & 0.45 & 32\% & 1.58 & 11\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Exponential Delay function parameter combinations for the best trade-off.  
Figure 12: Dependence of \(PDR_{\text{avg}}\) on the \(\beta\) parameter compared to the reference experiment (with no delay).  
Figure 13: Dependence of \(PDR_{\text{avg}}\) on the network size (Linear Delay: \(THR=1\)).  
they are used in more paths. DFF topology has longer paths and fewer branches because paths are built on existing ones (see Section 2.3.1).  
Performance metrics for Constant, Linear, and Exponential Delay functions with DFT vs. DFF are depicted on Figure 6 vs. Figure 7, Figure 8 vs. Figure 9, and Figure 10 vs. Figure 11, respectively. DFT allows to reach greater values of \(PDR_{avg}\) for the price of longer \(RDD_{avg}\). \(RDD_{avg}\) values of DFT are by one order greater than the corresponding results of DFF (see Table 8, compare DFT and DFF sections).
# Calibration of Camera and Flash LiDAR System with a Triangular Pyramid Target
## 3 Method
### Problem Formulation
#### 3.1.1 Intrinsic Parameters Calibration Problem Formulation  
As mentioned above, we apply flash LiDAR to complete calibration of the system because of its low cost and high resolution in both the vertical and horizontal directions. Besides, flash LiDAR has a big advantage in terms of price and stability. The LiDAR emits a beam of diverging laser initiatively, and the light returns to LiDAR through optical lens on a light-sensitive chip. This process follows time of flight (TOF) theory and calculates how long it takes for the light to hit an object and reflect back to the LiDAR. Hence, the intrinsic parameters of the camera and flash LiDAR both can be demonstrated by the pinhole model, as illustrated in Figure 1. The mere difference is that the pixel value of camera refers to color information, while the pixel value of LiDAR represents distance. The basic assumption here is that the lens distortion is dismissed.  
Arbitrary point \(P\) on the object is captured by camera or LiDAR. The projection of \(P\) is point \(P^{\prime}\) on the image plane. According to triangle similarity, we can obtain the coordinate relationship of projection:  
\[\left\{\begin{array}{l}\dfrac{u-u_{0}}{x}=\dfrac{f/d_{x}}{y}\\ \dfrac{v-v_{0}}{y}=\dfrac{f\tilde{f}d_{y}}{z}\end{array}\right., \tag{1}\]  
where \(P=[x,y,z]^{T}\) denotes the coordinate of point \(P\) in the camera coordinate system (or LiDAR coordinate system), and \(P^{\prime}=[u,v]^{T}\) is noted as the projection of point \(P\) in pixel coordinate system. \([u_{0},\ v_{0}]^{T}\) represents the coordinate of the center point \(O^{\prime}\) on the image. \(d_{x}\) and \(d_{y}\) refer to the camera sensor pixel size. \(f\) represents the effective focal length.  
We can obtain the relationship of coordinate between the camera coordinate system (or LiDAR coordinate system) and pixel coordinate system by combining Equation (1):  
\[z\left[\begin{array}{c}u\\ v\\ 1\end{array}\right]=\left[\begin{array}{ccc}f_{x}&0&u_{0}\\ 0&f_{y}&v_{0}\\ 0&0&1\end{array}\right]\left[\begin{array}{c}x\\ y\\ z\end{array}\right]=\mathbf{M}\left[\begin{array}{c}x\\ y\\ z\end{array}\right], \tag{2}\]  
where \(f_{x}=f/d_{x}\), \(f_{y}=f/d_{y}\). The intrinsic parameters of the camera and flash LiDAR are \(f_{x}\), \(f_{y}\), \(u_{0}\), and \(v_{0}\), and matrix \(\mathbf{M}\) is defined as the intrinsic matrix.  
Figure 1: Intrinsic parameters calibration model of camera and flash light detection and ranging (LiDAR). Arbitrary point P of object is projected onto flash LiDAR (**left**) and camera (**right**).  
Our method improves on the method of [13], in terms of the acquisition of intrinsic parameter of the camera described in Section 3.2.1, and the intrinsic parameters of flash LiDAR are calibrated in advance by the manufacturer.
## 2 Related Work
### Deep Learning Visual Model Architecture
#### 2.4.1 Convolutional Neural Network (CNN)
The pooling layer is placed after the convolutional layer and is used to reduce the size of the feature map, lower the model complexity, and increase the translation invariance of the model. It is used for downsampling the feature map. Its main functions include the following: (a) Size reduction: The pooling operation aggregates (takes the maximum or average value of) the local regions of the feature map and compresses the information within the regions, thus reducing the size of the feature map. This helps to reduce the number of model parameters and the amount of computation, and improves the computational efficiency of the model. (b) Translation invariance: The pooling operation aggregates local regions, making the feature map have certain invariance under transformations such as translation and rotation. This helps the model learn more robust feature representations and improves the generalization ability of the model for input data. Common pooling operationsinclude max-pooling and average-pooling. Max-pooling selects the maximum value within the local region as the aggregation result, while average-pooling selects the average value within the local region as the aggregation result. 3. Activation function. Its main role is to convert the input of neurons into output and introduce non-linearity, enabling the neural network to learn and represent complex non-linear relationships. If the activation function is missing in the model, the output of the network will be limited to the category of linear functions. This means that the complexity of the network will be greatly restricted, making it difficult for the network to effectively learn and simulate more complex and diverse data types, such as images, videos, and voices. The activation function allows the neural network to learn and map the complex non-linear functional relationships in reality, greatly enhancing the expressive ability of CNNs, and thus enabling more advanced learning and reasoning tasks. Commonly used activation functions include Sigmoid, Tanh, Relu, etc. 4. Fully connected layer. Also known as the dense-connected layer, it is located in the last few layers of the CNN and is used to map the features extracted by the convolutional layer into the final output results. In classification tasks, the fully connected layer is usually used to convert the feature map into a class probability distribution or class labels. Each neuron in the fully connected layer is connected to all neurons in the previous layer, and each neuron has weight and bias parameters, which are used to adjust the weights and biases of the input features. The fully connected layer performs linear combinations and non-linear transformations on the features extracted by the convolutional layer, thus obtaining higher-level abstract feature representations for better distinguishing different classes. Therefore, the Softmax function can be used in the last layer for classification to ensure that the sum of the probabilities of all classes in the obtained output results is 1.
## 2 Data and Methods  
### Project Description  
In this research, the study area is the Aboozar oilfield located 74 km west of Kharg Island. Figure 1 shows the location of the Aboozar oilfield along with Kharg Island and other adjacent oilfields. For a better illustration, the Aboozar oilfield and Kharg Island have been shown in a yellow and green color, respectively. As shown in this figure, Aboozar oilfield is situated between the Nowrouz and Soroosh oilfields.  
In 1959, the first exploration wellbore was drilled in the Aboozar oilfield. Further exploratory works were pursued until 1975. Subsequently, the oil production phase commenced in November 1976. The oilfield was initially operated by the Iran Pan American Company (IPAC), and it was then transferred to the Iranian Offshore Oil Company (IOOC) in 1979. Up to now, ten platforms with more than 140 vertical, deviated, and horizontal wellbores have been drilled in the oilfield. Those wellbores are connected to three main production platforms: AA, AB, and AC. Presently, a total of 90 wellbores are operating while the rest are inactive due to different technical issues. Based on the exploratory activities, it is estimated that the Aboozar oilfield contains 4 billion barrels of crude oil. The current production rate of the oilfield is around 200,000 barrelsper day. The oil extracted from the platforms is transferred to Kharg Island through a 24-inch-diameter pipeline. It is noteworthy that in the Aboozar oilfield, more than one hundred people are currently working. Moreover, the depth of seawater in the area is nearly 40 m.  
The main reservoir in this oilfield is the Asmari formation situated under the Gachsaran anhydride caprocks. Figure 2 shows the simplified stratigraphy petroleum systems and tectonics offshore of the Persian Gulf. As shown in this figure, the Asmari formation is considered as the first oil-bearing formation in both the South Gulf and East Gulf sections. The East Gulf refers to the Iranian waters offshore of the Persian Gulf. Based on this figure, the Asmari formation contains Oligocene- and Miocene-aged carbonate and limestone rocks [52; 53].
# Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review
## 5 Earthquakes
### Reviewed Use Cases for Earthquakes EW
The proposed architecture consists of: (i) a sensor gateway based on 3G/Wi-Fi access point, (ii) a p-wave detector connected to the gateway, and (iii) wireless sensor nodes with WoR-Receivers organized hierarchically. The P-wave detector triggers the more reliable and accurate WSN system through the WoR devices when a P-wave is detected. The study shows a 229 ms delay to the wake-up command, but the proposed scheme has a shorter wake-up delay and is more energy efficient (with power consumption 350 \(\upmu\)A) than IEEE 802.15.4 (which has power consumption in beacon mode from 1.43 mA to 2.51 mA, depending on parameters set).
# Vulnerability of Satellite Quantum Key Distribution to Disruption from Ground-Based Lasers
## 4 Discussion
### Countermeasures
#### 4.2.1 Receiver Gating  
Improved synchronization between the satellite and ground station will allow the QKD receiver to gate its detector more accurately, reducing the fraction of photons from the mobile laser terminal that will cause disruption. This would require the average power from the laser terminal to be increased, or better knowledge of the QKD receiver gating to be obtained.  
This development is highly likely to occur in the near term as improved detector gating will be necessary to reduce the impact of background light and allow the QKD receiver to operate during the day or in light polluted areas. Great improvements in daytime operation of satellite laser ranging stations have already been achieved using careful gating techniques [18].
# Using Machine Learning Models and Actual Transaction Data for Predicting Real Estate Prices
## 2 The Literature Review of Real Estate Price Predictions
In addition, weights based on value zones provided by experts of the register center and weights generated by the self-organizing map (SOM) were used by the ensemble learning system. The numerical results revealed that the ensemble learning system with weights generated by SOM outperform the ensemble learning systems with weights provided by experts. Furthermore, the ensemble systems can reach more accurate forecasting results than the other single forecasting models. [PERSON] et al. [17] employed time series models with or without the information content of 10 or 120 additional quarterly macroeconomic series to forecast the U.S. real house price index. Their study concluded that the utilization of fundamental economic variables could increase the forecasting accuracy and especially be effective for the 10 fundamental economic variables in the dynamic stochastic general equilibrium model. [PERSON] et al. [18] developed a grading fuzzy logic model to forecast house selling prices. Many housing attributes, such as public transportations systems and environmental factors, served as inputs of the fuzzy logic system. The numerical results indicated that the proposed fuzzy logic system captures the patterns of house selling prices and lead to satisfied forecasting accuracy. The collection of actual transaction data of real estate has been performed since August 2012 by the Ministry of the Interior of Taiwan. Thus, the motivation of this study is to examine the performance of machine learning models with actual transaction data in forecasting real estate prices.
## 1 Introduction
Table 1 shows an overview of these methods. In this paper, a novel framework based on CNN is proposed to improve background subtraction. In the proposed method, the lower convolution layers are used to extract the general features of a video. These general features usually have multiple channels, and, in each channel, there is a lot of redundant information. A few of these channels contain the main information of the image, thus these main channels are applied to background subtraction, which not only reduces the amount of data processing, but also removes the redundant interference in the scene. Using a traditional background subtraction method to process the main features that are extracted by CNN further improves the performance of the background subtraction method. In comparison to the existing deep neural network methods of background subtraction, there are three advantages of the proposed method. First, the proposed method is a general unsupervised method, and there is no need to train models based on the historical data of specific scenarios. Second, the proposed method requires only a few calculations. Third, only one of the lower layers of the pretrained CNN is used to extract the general features of videos. The remainder of this paper is organized as follows. Section 2 presents a detailed account of the proposed method. Section 3 describes the profound experiments on a dataset of surveillance \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Categories** & **Methods, Authors** & **Features** & **Learning Types** & **Scene** \\ \hline \multirow{8}{*}{traditional methods} & GMM & & & \\  & KDE & & & \\  & ViBe & & & \\  & PBAS & hand-crafted & unsupervised & universal \\  & LOBSTER & & & \\  & SuBSENSE & & & \\  & RPCA & & & \\ \hline \multirow{8}{*}{CNN-based} & ConvNet [17] & & & \\  & [PERSON] [19] & & & \\  & [PERSON] [21] & & & \\  & [PERSON] [27] & & & \\  & FgSegNet [28] & learned & supervised & specific \\  & [PERSON] [29] & & & \\  & [PERSON] [31] & & & \\  & [PERSON] [32] & & & \\  & [PERSON] [33] & & & \\ \hline \multirow{2}{*}{Fully CNNs} & [PERSON] [23; 35] & & & \\  & [PERSON] [24] & & & \\ \cline{1-1}  & [PERSON] [26] & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: An overview of background subtraction methods. videos that verified the validity and feasibility of the proposed method. Finally, Section 4 provides some conclusions.
# Holographic Mixed Reality System for Air Traffic Control and Management
###### Abstract  
Based on a long-term prediction by the International Civil Aviation Organization indicating steady increases in air traffic demand throughout the world, the workloads of air traffic controllers are expected to continuously increase. Air traffic control and management (ATC/M) includes the processing of various unstructured composite data along with the real-time visualization of aircraft data. To prepare for future air traffic, research and development intended to effectively present various complex navigation data to air traffic controllers is necessary. This paper presents a mixed reality-based air traffic control system for the improvement of and support for air traffic controllers' workflow using mixed reality technology that is effective for the delivery of information such as complex navigation data. The existing control systems involve difficulties in information access and interpretation. Therefore, taking notice of the necessity for the integration of air traffic control systems, this study presents the mixed reality (MR) system, which is a new approach, that enables the control of air traffic in interactive environments. This system is provided in a form usable in actual operational environments with a head-mounted see-through display installed with a controller to enable more structured work support. In addition, since this system can be controlled first-hand by air traffic controllers, it provides a new experience through improved work efficiency and productivity.  
mixed reality; interaction mechanisms; air traffic management +
Footnote †: journal: Applied Sciences
# Performance Analysis of the Korean Positioning System Using Observation Simulation
## 4 Performance Analysis for KPS
### Precise Point Positioning (PPP)
In this study, the combined KPS+GPS improved the position accuracy in both SPP and KPP modes compared to KPS and GPS. As a result, it is noted that KPS with GPS can contribute to the improvement of the positioning accuracy. Figure 10: PPP positioning accuracy. (**a-c**) for KPS-only; (**d-f**) for GPS-only; (**g-i**) for combined KPS+GPS. E, N, and U indicate the east, north, and up components. In each sub-figure, the ground-track of KPS IGSO orbit is the analemma. The red crosses denote the ground-track of three KPS GEO satellites.