esa-sceva/satcom-chunk-collection huggingface dataset:
# Spatiotemporal Prediction and Proactive Control Method for Excavation-Induced Wall Deflection
## 2 Methodology  
This section first introduces the base model of the proposed intelligent control method, specifically the spatiotemporal graph attention (ST-GAT) model, and briefly explains the principles of this model. Following that, a proportional-integral-differentiation (PID) controller is introduced to control and optimize the performance of the servo axial force support system. Finally, the flowchart of the proposed ST-GAT-based PID control method for wall deflections is presented.
# Secure Data Distribution Architecture in IoT Using MQTT
## 2 Problem Statement and Related Works
The clients are less secure because they are exposed to several brokers. 3. Inefficient utilization of network resources, because when multiple clients within the same broker are interested in the same resource each client establishes a separate connection with the broker producing the resource they want. In a fully peer-to-peer architecture, the brokers dynamically organize themselves in a topology according to a distributed algorithm running on all of them [15]. Subsequently, data discovery and dissemination are performed in a cooperative manner, where brokers route other brokers messages from source to destination. Their peer-to-peer nature is a good indication of how well they scale; however, it is still heavily influenced by their final topology which also affects other aspects such as fault tolerance and security [16]. Another interesting aspect of these architectures is that the clients only need to connect to one broker to get resources produced across all brokers in the network. This overcomes most of the limitations of the previous type. However, there is still an issue of latency, where data might have to propagate through several brokers to reach its final destination. The architecture is illustrated in Figure 2. [PERSON] et al. [5] presented another architecture that aims to improve scalability by organizing the brokers in a spanning tree using the spanning tree protocol. Table 1 summarizes related works and compares them to our proposed architecture in terms of autonomy, client involvement and topology. Autonomy refers to the brokers ability to choose other brokers to communicate with. Client involvement refers to whether the clients are aware of the existence of multiple brokers. The topology refers to the brokers' arrangement and whether that can change during the runtime. Our proposed architecture improves upon existing architectures by allowing the brokers to be fully autonomous while hiding the complexity of obtaining data from multiple brokers from the clients. This leads to the broker's communicating in an ad hoc fashion to form a topology that best serves their clients at any given time. \begin{table}
\begin{tabular}{p{113.8 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Reference** & **Autonomy** & **Client Involvement** & **Topology** \\ \hline
[8; 9; 10] & No autonomy & No involvement & Fixed \\
[11; 12; 13; 14] & Full autonomy & Involved & Dynamic \\
[5] & No autonomy & No involvement & Dynamic \\ Proposed architecture & Full autonomy & No involvement & Dynamic \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Related work summary and comparison with the proposed solution. Figure 2: Illustration of fully peer-to-peer architectures.
## 2 Materials and Methods
### Description of Optimization and Algorithm
#### 2.2.1 Description of Optimization Problem
The last condition, (20), is the power constraint on user devices and the base station. In order to fully mobilize the resources in the network, the transmission power of each resource should be less than the respective power limit. Thus, the uplink transmission is less than \(P_{ul}\) and the downlink transmission is less than \(P_{dl}\). This optimization problem involves discrete variables, including allocation status and the number of bytes, and continuous variables such as transmission power. If the model tries to solve this problem by means of conventional algorithms for optimization, because of the frequent network change between adjacent time slots, the algorithm must iterate again after every change, which will result in more extensive computation operations, larger delays and worse optimization performance. This will undoubtedly lead to a waste of MEC resources. Therefore, low iterations and a low complexity resource allocation algorithm will greatly improve the ability of a MEC network to quickly match multi-dimensional resources and multiple kinds of tasks.
## 4 pLEO Time Scale Realizations
### Principle of IEM Formations  
In the innovative time scale concept for AltPNT, each onboard orbital platform \(i\) (where \(i=1,\ldots,N\)) is designed to incorporate a clock ensemble for pLEOT, moving beyond reliance on a single precision clock. As depicted in Figure 9, to ensure accurate time output from this clock ensemble, an oven-controlled quartz oscillator (OCXO) is meticulously calibrated using a micro-phase stepper (MPS) on every platform. This strategic setup iscrucial because, with all platforms interconnected, just one connection to GPS or a single ground station is sufficient to accurately calculate the offset of pLEOT relative to GPST or UTC. This approach not only enhances precision but also optimizes connectivity and reliability across the system.  
In relation to the realization of the IEM, an anchor platform continuously collects clock-to-clock difference measurement data. These data are processed using a clock-ensemble Kalman filter for each ensemble member clock. The output of this filter, as indicated in Equation (69), provides the estimate of the \(i\)-th clock state. This estimate is expressed as the difference between the physical clock state \(x_{i}(t_{k})\) and the IEM of \(x_{0}(t_{k})\), along with the estimation noise \(e_{i}(t_{k})\). Additionally, the measurement of the MPS of the steerable OCXO, denoted as \(x_{i,OCXO}(t_{k})\), is also important. This measurement describes the local difference between the two clock states \(x_{i}(t_{k})\) and \(x_{i,OCXO}(t_{k})\)  
\[z_{i,OCXO}(t_{k})=x_{i,OCXO}(t_{k})-x_{i}(t_{k})+v_{i}(t_{k})\,. \tag{72}\]  
Next, the estimate for the free-running clock, \(\hat{x}_{i}(t_{k})\), is combined with the measurement, \(z_{i,OCXO}(t_{k})\), thus resulting in the difference measurement between the OCXO and the IEM, disturbed by the estimation and measurement errors  
\[\hat{x}_{i}(t_{k})+z_{i,OCXO}(t_{k})=x_{i,OCXO}(t_{k})-x_{0}(t_{k})-e_{i}(t_{k })+v_{i}(t_{k})\,. \tag{73}\]
## 1 Introduction
[PERSON] and [PERSON] [15] explored effects of residual stress patterns on fretting-corrosion-fatigue behaviors of steel wires in seawater and found that the fretting behavior is influenced by residual stresses at points of contact varying around the circumference of the wire. [PERSON] et al. [16] discussed the effects of fretting wear depth and contact load on stress distributions and crack initiation characteristics on fretting contact surfaces of fretted wires and found that increasing wear depth and contact load induces distinct stress distributions and accelerates crack initiation on fretting surfaces. [PERSON] et al. [17] analyzed effects of contact pressure, relative humidity and lubricants on the fretting wear behavior in thin steel roping wires. [PERSON] et al. [18] dealt with fatigue damage evolution in steel cables used in tires when subjected to cyclic bending loadings and simulated bent cable and its matrix with and without broken wires in order to evaluate the impact of the first breakage on the stress distribution within the cable. Generally speaking, previous efforts mainly focus on failure mechanisms of steel wires during cyclic tensile loading with fretting contact in different environmental media, wear mechanisms and fatigue crack propagations of steel wires during tension-torsion cyclic loading in different environmental media, and effects of surface condition, residual stress, material loss at the cross-section, contact pressure, lubricants and additional bending on fretting damage behaviors of steel wires. However, during tension-torsion cyclic loading of steel wires with fretting contact in distinct environmental media, quantitative demonstrations of wear rate and dissipation energy of steel wires have not been reported yet. The objective of this study is to quantitatively demonstrate the wear rate and dissipation energy during tension-torsion cyclic loading of steel wires with fretting contact in distinct environmental media. Effects of environmental media (air, deionized water, solutions) and tension-torsion cyclic loading parameters (loading cycles, contact load, torsion angle and crossing angle) on hysteresis loops, dissipation energy, wear scar size and wear coefficient were presented, respectively.
## 2 Design, Modeling, and STOP Simulation of an Optical Laser Head for LAAM
### Thermal Effect on Optical Performance with Sensitivity Analysis
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material Properties** & **Fused Silica [(29)]** & **Stainless Steel 316 [(30)]** & **Copper [(30)]** \\ \hline \multirow{4}{*}{Thermal conductivity (W/mK)} & 1.38 at 293–375 K & & \\  & 1.47 at 375–475 K & & \\  & 1.55 at 475–575 K & & \\  & 1.67 at 575–675 K & & \\ \hline \multirow{4}{*}{Heat capacity (J/kgK)} & 1.84 at 675–800 K & & \\  & 772 at 293–375 K & & \\  & 964 at 375–775 K & 500 & 384 \\  & 1052 at 775–1175 K & & \\ \hline \multirow{4}{*}{Thermal expansion coefficient (K\({}^{-1}\))} & 0.51 \(\times\) 10\({}^{-6}\) at 293–375 K & & \\  & 0.58 \(\times\) 10\({}^{-6}\) at 375–475 K & & \\  & 0.59 \(\times\) 10\({}^{-6}\) at 475–575 K & & \\  & 0.54 \(\times\) 10\({}^{-6}\) at 575–800 K & & \\ \hline Density (kg/m\({}^{3}\)) & 2203 & 7990 & 8960 \\ \hline Young’s modulus (Pa) & 73.1 \(\times\) 10\({}^{9}\) & 193 \(\times\) 10\({}^{9}\) & 120 \(\times\) 10\({}^{9}\) \\ \hline Poisson ratio & 0.17 & 0.26 & 0.34 \\ \hline \multirow{2}{*}{Refractive index} & 1.45 \(-\) i(\(T-T_{0}\)) \(\times\) 10\({}^{-5}\) & & \\  & \multicolumn{3}{c}{\(T_{0}\) = 293 K} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Material properties used in the simulation. Figure 5: Temperature distributions by 1.634 kW in (**a**) housing structure and (**b**) lenses, and deformation distributions in (**c**) housing structure and (**d**) lenses. Figure 6: (**a**) Temperature and (**b**) displacement distributions of the focusing lens irradiated by laser power at 1.634 kW. Figure 8 shows the RMS spot radius and optical intensity under 1.634 kW and 3.364 kW laser power levels as a function of the focal plane positions. From the plot, it is shown that the designed RMS spot radius at the same focal plane position is directly proportional to a rise in the laser power level. However, the optical intensity has no such linear relationship with the increasing laser power level as it is also inversely related to the squared spot radius. Figure 9 plots the RMS spot radius and optical intensity as a function of laser power at the original focal plane position (\(F_{z}=-0.1\) mm). It is seen that the RMS spot radius has similar changes. However, the optical intensity may reach a peak at 2.0-kW laser power between 1.634 kW and 3.364 kW. Figure 10 shows the optical intensity distributions irradiated by 1.634 kW and 3.364 kW laser power, respectively, with the focal plane location at 0.1 mm. It is seen that different laser power levels generate different power density distributions. Figure 7: RMS spot radius (**a,c**) and optical intensity (**b,d**) by the STOP analysis (**top**) and ray tracing simulations (**bottom**). Figure 8: RMS spot radius \(R_{rms}\) and optical intensity \(I\) as the function of focal plane position \(F_{z}\). Figure 10: Power density distributions (**a**) 1.634 kW and (**b**) 3.364 kW in the focal plane position of \(F_{z}=0.1\)mm. Figure 9: RMS spot radius \(R_{rms}\) and power density distribution \(I\) as the function of laser power \(P\).
# IoT-Enabled Vehicle Speed Monitoring System
## 4 Testing
### Android Module Testing  
The Test-Driven Development (TDD) paradigm is used to develop Android applications, because the Flutter framework is used for application development, which ships with a large number of tools for unit integration and system testing. Like the website module, usability, safety, security, and interoperability are the main attributes of the Android application. To enhance the usability of the application, Android material design was implemented in the front-end and validated with user testing results.  
This module was also tested using UBST, where usage probabilities were assigned based on mutual consultation and expert opinion. Table 3 shows the usage probabilities of the application's features.  
Effective testing was performed to confirm the agreed-upon requirements in the Android application. Moreover, extensive black- and white-box testing of the crucial functionalities was performed.  
Comparison between Existing System (Speed Gun) and the Proposed System (IoT-Based Intelligent System  
1. Because a speed gun can only identify a vehicle's speed when it is in front of a camera, a large number of such speed guns are required to detect the speed at various places. The suggested system is used to continuously monitor the vehicle's speed since it is based on the idea of monitoring the average speed between two places. The device can identify the speed even if the vehicle is not always visible to the camera.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Functionality** & **Usage Probability** \\ \hline Admin/User Login & 20\% \\ API Requests & 20\% \\ Camera Details & 15\% \\ Gross Speed & 20\% \\ Admin/User Profile & 15\% \\ Notifications/Log Outs & 10\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Website feature usage probabilities.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Functionality** & **Usage Probability** \\ \hline User Signup/Login & 15\% \\ User Panel & 5\% \\ In-app Notifications & 10\% \\ API Requests & 15\% \\ Camera Information & 20\% \\ Vehicle Information & 20\% \\ Location Information & 15\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: The application’s feature usage probabilities.  
2. The current system is unable to monitor the vehicle's speed on several road lines at the same time, but the suggested system is capable of doing so.
3. When comparing costs, the provided system is ten times less expensive than the current system. The suggested method requires a basic low-cost camera and a Raspberry Pi, whereas the speed gun is expensive and inaccurate.
4. The existing system is a very manual and archatic system, whereas the proposed system is an automatic and up-to-date system, in which all processes are completed automatically with minimal human intervention.
5. The proposed model addresses two critical issues in Pakistan's disorganized traffic environment: (1) need of continuous power supply and (2) irregular traffic trajectories that make speed monitoring more challenging.
## 3 Method
### Compositions of the Neural Network
#### 3.3.4 Dynamic Encoder  
The dynamic encoder is used for the further extraction of dynamic features. At every step, its inputs comprise the embedding form of the current state and the encoding form of the task that is selected at the previous step. The states of the AOS have the following characteristics: first, it is dynamically changing; second, the current state is affected by the previous one; third, the state sequence is time related. Hence, the dynamic encoder is composed of an LSTM cell (\(L_{d}\)), a concatenating operator, and a fully connected network (\(W_{c}\)). At step \(n\), \(de_{n}\) converts to a feature vector \(dc_{n}\) through the LSTM cell at first. Then, the concatenation of \(dc_{n}\) and \(sc_{i_{n}}\) is transformed to \(c_{n}\) by a fully connected network. This process is formulated as follows:  
\[dc_{n} = L_{d}(de_{n}), \tag{28}\] \[c_{n} = W_{c}\big{(}\text{Concat}(sc_{i_{n}},dc_{n}))\big{)}, \tag{29}\]  
where \(L_{d}\) is the learnable parameter set of the LSTM cell, and \(W_{c}\) is the learnable parameter set of the fully connected network.
# Improving 2-5 Qubit Quantum Phase Estimation Circuits Using Machine Learning
## 3 Results
### Traditional Phase Estimation  
The performance of the traditional, highest-peak estimator is shown in Figure 9 for all four datasets and the 21 levels of noise present in each dataset. The 2-qubit dataset is shown in blue and has notably worse performance than the other datasets. This is due to the fact that the error of this method goes as \(2^{-n}\), where \(n\) is the number of qubits. Thus, the jump in error from 2 to 3 qubits is larger than the jump from 3 to 4 and from 4 to 5. The similarity in error for the \(3/4/5\) qubit datasets is due to the dominant contribution of phases near 0 and 1, where phases in the interval \(\left(2^{-n-1},1\right)\) will generate a prediction of 0 using the traditional method. This is because, as was covered in Section 2.3.1, the highest peak of the distribution occurs at the integer closest to \(2^{n}\theta\), though this is periodic. For \(\theta\in\left(2^{-n-1},1\right)\), this integer is 0 and not \(2^{n}-1\), causing a peak at 0 and a large contribution to MSE. In fact, the \(3/4/5\)-qubit datasets all have similar performance near MSE = 0.02. The traditional method exhibits mostly level performance across various noise levels, which is desirable. However, this method had the poorest performance of all algorithms tested, and on the 2-qubit dataset it performed worse than the trivial mean-predicting model.
# Integrating Root Morphology Based on Whole-Pullout Test of Model Roots: A Case Study
## 2 Materials and Methods
### Experimental Design  
#### 2.2.1 Levels of Experimental Factors  
This experiment considered three morphological features: taproot length, number of lateral roots, and branching angle of the lateral root. These three morphological features and the unit weight of soil around the root were considered the four main influencing factors in this study. The unit weight of soil around the root was determined at different compaction frequencies every 10 cm during the quartz sand filling process. According to reference [31], the typical depth of plant root systems ranges from 0 to 60 cm. In the depth range of 0 to 20 cm below the quartz sand surface, the root-soil interface friction resistance was minimal. From depths of 20 to 60 cm, the root-soil interface friction resistance was significant, and it decreased again from depths of 60 to 80 cm. References [32; 33] suggested that the branching angle of the lateral root generally ranges from 15\({}^{\circ}\) to 60\({}^{\circ}\). To simplify the analysis, each factor was considered at four levels: taproot lengths of 40 cm, 50 cm, 60 cm, and 70 cm; 1, 2, 3, and 4 lateral roots; soil compaction frequencies of 10, 20, 30, and 40; and lateral root branching angles of 15\({}^{\circ}\), 30\({}^{\circ}\), 45\({}^{\circ}\), and 60\({}^{\circ}\). Conducting 256 experiments using a method wherein the variables are controlled would be cost-intensive, laborious, and challenging for quick and effective analyses, therefore, representative combinations were selected based on orthogonality from the aforementioned experimental plan. The rational design of an orthogonal experiment allowed for understanding of the sensitivity and significance of the plant root morphology and the unit weight of soil around the root on the overall vertical pullout resistance of the root system. An \(L_{16}(4^{5})\) orthogonal table was employed, with one column left blank for the error. Factors A, B, C, and D are the compaction frequency, the number of lateral roots, the taproot length, and the branching angle of the lateral root, respectively. The levels of each factor in the orthogonal experiment are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Factor Levels**} & **A** & **B** & **C** & **D** \\  & **(Compaction** & **(Number of Lateral** & **C** & **(Branching Angle of the** \\  & **Frequency/freq** & **Roots/pcs)** & **(Taproot Length/cm)** & **(Branching Angle of the** \\  & & & & ** Lateral Roots/deg)** \\ \hline
1 & 10 & 1 & 40 & 15 \\
2 & 20 & 2 & 50 & 30 \\
3 & 30 & 3 & 60 & 45 \\
4 & 40 & 4 & 70 & 60 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Levels of experimental factors.  
Figure 1: Materials and apparatus setup: (**a**) model root physical diagram; (**b**) experimental setup and schematic diagram of the model root.
# Noise-Tolerant Data Reconstruction Based on Convolutional Autoencoder for Wireless Sensor Network +
## 2 Related Work
### External Events  
As mentioned previously, external events mainly affect WSNs when sending packets from sensor nodes to the base station. This section reviews previous studies related to recovering data lost through external events.  
In [14], the authors employed an approach based on matrix factorization (MF) using any weights to recover the missing time series data. Although this method can be used with numerous attributes, it is extremely complex. A distributed data prediction model was proposed by [12]. The model predicted upcoming measurements and recovered the missing sensor readings that resulted from diffusion faults and sleep schedules. In [15], an approach was proposed to predict missing readings from a sensor node by utilizing matrix completion techniques. The proposed method considers the correlation between sensor nodes to address the randomly missing data. The disadvantage of these algorithms is that they are relatively straightforward and have limited success in discovering the relationship between the data and sensors to recover the missing data.  
In the study [16], a method for reconstructing missing data was introduced. This method utilized a multiple linear regression (MLR) model that incorporated spatiotemporal correlation. The results demonstrated the effectiveness of the model in terms of the mean absolute error (MAE), mean squared error (MSE), and data reliability. Subsequently, a clustering strategy was employed to compute the internode correlation. Another study [13] proposed a novel approach using deep learning techniques to recover lost measurement  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Paper** & **Year** & **Type** & **Dataset** & **Purpose** & **Method** \\ \hline
[14] & 2020 & External events & Real data & Reconstructing missing data & Algorithm \\
[12] & 2020 & External events & Real data and noise & Reconstructing missing data & Algorithm \\
[15] & 2021 & External events & Simulated data & Reconstructing missing data & Algorithm \\
[16] & 2021 & External events & Real data & Reconstructing missing data & Machine learning \\
[13] & 2021 & External events & Real data and noise & Reconstructing lost data & Deep learning \\
[17] & 2018 & Internal events & Real data & Reconstructing data & Algorithm \\
[18] & 2021 & Internal events & Real data & Reconstructing data & Bidirectional recurrent \\
[19] & 2022 & Internal events & Real data & Reconstructing data & Neural network \\
[20] & 2021 & Internal events & Real data and noise & Denoising data & Algorithm \\
[21] & 2022 & Internal events & Real data and noise & Reconstructing data & Convolutional neural network \\ \hline \hline \end{tabular}
\end{table}
Table 1: Related work.  
data. The recovered signal was highly consistent with the original signal in both the time and frequency domains.