esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Optimization of RISC-V Processors Based on Branch Prediction and Division Data Dependency
## 1 Introduction
Therefore, effectively evaluating the predictability (i.e., real-time performance) of hardware processors from a hardware perspective, as well as quantifying the degree of predictability (i.e., the quality of real-time performance), becomes a central focus of this paper. The contributions of the paper are as follows:  
* A parameter is proposed for evaluating the real-time performance and the quality of real-time performance of processors from a hardware perspective, along with the formula for calculating this parameter. This formula not only indicates whether the processor can meet the system's real-time requirements but also reflects the quality of real-time performance when these requirements are met. * An embedded RISC-V processor is improved and optimized for real-time performance. * An improvement in real-time performance is verified, as is the degree of improvement in the quality of real-time performance before and after the processor optimization. Additionally, we use experimental data to validate the reasonableness of the real-time performance parameter. The paper is organized as follows. Section 2 briefly introduces the current methods for evaluating system real-time performance, such as the WCET method, and discusses its limitations. We also present the real-time performance evaluation method proposed in this paper and provide an explanation. Section 3 introduces the RISC-V processor platform used in this paper and details two specific methods for the real-time optimization of the platform, including the detailed content of module optimizations. Section 4 outlines the phased experiments and results for the optimization of the branch prediction module and the division module. It also describes the comprehensive real-time testing of the complete processor, including the CoreMark test and division test experiments and their results. The section provides an analysis of the overall improvement in the processor's real-time performance. Section 5 summarizes the WCET analysis method and the H\({}_{\text{rtp}}\) (Hardware Real-Time Parameter) analysis method, providing experimental conclusions and outlining plans for future work.
## 3 Global Equations and Parameters
### Impedance Computation  
The presence of possible defects in the weld zone lead to a change in the physical characteristics, which results in the variation of the coil impedance. Several methods exist for impedance calculation; the difference lies in the choice of the state variable which has a direct relationship with the solution resulting from the numerical model and the configuration of the device to be studied. In this application the impedance \(Z\) is calculated from the MVP (the real and imaginary parts) as follows [35; 36]:  
\[Re(Z)=-\frac{N^{2}}{\int S^{2}}\omega\iint\limits_{S}\ 2\pi r\ Im(A)\ ds \tag{7}\]  
\[Im(Z)=-\frac{N^{2}}{\int S^{2}}\omega\iint\limits_{S}\ 2\pi r\ Re(A)\ ds \tag{8}\]  
with: \(N\)--coils number, \(S\)--surface of inductor coil, \(r\)--inductor radius.
# Toward Sustainable Mars Exploration: A Perspective on Collaborative Intelligent Systems
## 3 Energy Management  
Space exploration systems deployed millions of kilometers away from Earth need a reliable source of energy to sustain their operation. While electrochemical cells might suffice for powering short-term deployments, long-term missions demand alternative sources of energy to provide periodic energy top-ups to the system. Often, long-term space missions rely on energy generated from sources such as solar or nuclear, in combination with rechargeable electrochemical cells [12; 13]. These sources are not always readily available; they are rather limited and unpredictable in many scenarios. Consequently, there is an increasing demand for reliable sources of energy and advanced energy management systems in space missions.
# Combining Keyframes and Image Classification for Violent Behavior Recognition
## 3 Proposed Method
### Keyframe Extraction  
While deep-learning methods can accurately extract keyframes, they require high-performance hardware and reduce the system's processing speed. The characteristics of violent video frames are apparent; thus, traditional methods can be used to extract keyframes. The available traditional methods have roughly equivalent probabilities of extracting frames containing violence, with all of them able to reach or exceed a success rate of 98.90%. In practical applications, the frame difference method can better control the number of extracted keyframes. Therefore, we propose to use this method in our approach, as it can reduce the number of false-positive (non-violent) frames extracted by controlling the length of the window. For example, some nonviolent behaviors (such as hugging, shaking hands, etc.) may briefly appear similar to violent behaviors, and an appropriate window length can reduce the chances of these being flagged as violent. Moreover, because violent events are uncertain in the real world, keyframes can also be extracted at fixed intervals.
# Special Issue on Transportation Big Data and Its Applications
###### Abstract
They found that the XGBoost model had a better fitting degree compared to traditional linear and nonlinear models. The model identified the relative importance of different variables and revealed the nonlinear and threshold effects of built-environment variables on ridership. The study also explored the moderating impact of station location on the relationship between the built environment and ridership. [PERSON] et al. (contribution 11) proposed a modular battery swapping mode for electric taxis and developed a data-driven approach to configure and operate modular battery swapping stations (BSSs). Their study showed that a BSS with modular battery swapping can save on investment costs and better respond to time-of-use pricing compared to traditional battery swapping modes. Utilizing the ride-hailing order data from Chengdu, [PERSON] et al. (contribution 12) employed the Nugget-Sill Ratio (NSR) method and the Optimal Parameter-Based Geographical Detector (OPGD) model to determine the optimal grid scale. Furthermore, they integrated the \"5D\" built-environment determinants to construct a model assessing the impact factors influencing ride-hailing during morning and evening peak hours. [PERSON] et al. (contribution 13) reported the development of a dynamic graph convolutional network model (Res-DGCN) for crowd flow prediction in urban areas. The model incorporates a spatial-temporal attention module and a conditional convolution module to capture spatial and temporal dependencies in the crowd flow data. The model was trained using the Huber loss function, which improved its robustness to outliers. The experimental results showed that the RS-DGCN model outperformed the baseline models in terms of the mean absolute error and the root mean square error, demonstrating its effectiveness in crowd flow prediction tasks. [PERSON] et al. (contribution 14) developed a driving behavior risk measurement model that calculates the risk of different driving behaviors based on four indicators: lateral stability, longitudinal stability, car-following risk, and lane-changing risk. The model used weights to combine these indicators and provided a comprehensive risk evaluation of a driver's behavior. The study found that most drivers' behavior fell within a normal distribution, with the majority exhibiting a risk measurement between 0.1 and 0.3. [PERSON] et al. (contribution 15) presented a traffic accident risk prediction model based on an LSTM algorithm. The model was able to accurately forecast the accident risk by analyzing the regional risk index and capturing long-term dependencies in the data. [PERSON] et al. (contribution 16) reviewed the intersection between ITS sensing and edge computing applications. They discussed the recent advances in ITS sensing and identified key challenges in this field. The authors also highlighted the potential benefits of integrating edge computing with ITS sensing and proposed future research directions.
## 4 Results and Analysis  
### Multi-Dimensional Transport and Settlement Laws of Proponents  
The fracturing fluid studied in this article is slippery water, mainly composed of clean water, crosslinking agent, corrosion inhibitor, breaker, surfactant, and drag reducer in a certain proportion. In practical engineering, the transport and settlement of proppants are affected by multiple types of engineering parameters. The first is the construction operation parameters. During on-site construction, the construction displacement, time, sand ratio, and total operation time are often determined based on the technical personnels' references to the fracturing pump injection program table, combined with construction experience. In terms of construction displacement, it is necessary to have bidirectional interference with the initial velocity of particle groups, phased changes in the length of sand embankments (initial sand dune/length unchanged \(\rightarrow\) establishment stage/accumulation dominated \(\rightarrow\) extension stage/height unchanged), dynamic accumulation in the direction of crack height (linear growth, slow growth, and stable maintenance), and changes in the accumulation angle at the entrance. The second is the interference between particles and fluid properties, mainly considering the particle size, viscosity, and density of fracturing fluid and proppant. The particle sizes of proppants are mostly dominated by 20/40 mesh or 30/60 mesh. The density of the proppant is directly related to the settling speed, and using a low-density proppant system can promote the increase in effective proppant seam length. Viscosity is an important characterization of the suspended sand performance of fracturing fluids and is particularly crucial for the placement of proppants. With the continuous exploration and production of unconventional oil and gas, low viscosity fracturing fluids are increasingly  
Figure 1: Force analysis of single-particle proppant.  
being valued. Figure 2 describes the multidimensional factors that affect the transport and settlement patterns of proppants.
# Phase Alignment of an Array Optical Telescope System Using Balanced Detection
## 3 Simulation Experiment Process
### Signal-to-Noise Ratio  
In practice, the spectral ratio of the coupler inaccuracy, the shot noise and thermal noise are existed in the system. Additionally, two diodes of the balance detector are not exactly the same in the reality [29; 30]. From Equations (7) and (8), the SNR of this communication system was derived in Equation (12).  
\[SNR=\frac{\left(\frac{\epsilon}{\hbar\upsilon}\right)^{2}(S_{1}\eta_{1}+S_{2} \eta_{2})^{2}A^{4}\varepsilon(1-\varepsilon)R}{\frac{\epsilon^{2}A^{2}}{ \hbar\upsilon}[S_{1}\eta_{1}(1-\varepsilon)-S_{2}\eta_{2}\varepsilon]\Delta fR+ Var(n_{ex1}-n_{ex2})+4k_{b}T\Delta fR} \tag{12}\]  
where \(S\) is the photosensitive area of a diode; \(\eta\) represents the quantum efficiency of a diode; \(\varepsilon\) is the spectral ratio of the coupler; and \(\Delta f\) is the measured bandwidth. Thermal noise is \(P_{T}\), which is equal to \(4k_{b}T\Delta fR\), and \(k_{b}=1.38\times 10^{-23}\mathrm{J/K}\), which is the Boltzmann's constant. Shot noise is expressed as \(2 eI_{DC}\Delta fR+Var(n_{ex1}-n_{ex2})\), and \(I_{DC}=\frac{\epsilon}{2\hbar\upsilon}\big{[}S_{1}\eta_{1}(1-\varepsilon)A^{ 2}-S_{2}\eta_{2}\varepsilon A^{2}\big{]}\), \(n_{ex}\) is excess noise.  
Quantum efficiency has a significant impact on the SNR. SNR can be simplified into Equation (13) under the condition of the excess noise, and thermal noise can be ignored. The value of \(\varepsilon\) is 0.5. \(S_{1
## 4 Results and Discussion
### Evaluation of Experimental Results
#### 4.4.1 Evaluation of Distinguishing Ability  
The distinguishing ability of the proposed S-ECNN model was compared with that of its variants. The confusion matrix of binary classification was used to analyze the comparing results, as shown in Figure 10. Each column of the matrix represents the instances in a  
Figure 8: Visualization of alarm vectors in the three-dimensional space.  
Figure 9: The accuracy curves and loss curves for the training and validation set of the proposed method.  
predicted label while each row represents the instances in an actual label. The effectiveness was further quantified by accuracy and the following widely used indicators [35]:  
\[Recall=\frac{TP}{TP+FN}, \tag{15}\]  
\[Precision=\frac{TP}{TP+FP}, \tag{16}\]  
\[Specificity=\frac{TN}{TN+FP}, \tag{17}\]  
\[F1-score=\frac{2\cdot Recall\cdot Precision}{Recall+Precision}. \tag{18}\]  
The comparing results of the proposed S-ECNN model and its variants are listed in Table 5. The results indicate that the proposed S-ECNN model is more effective than the S-ECNN-rand model and the S-ECNN-static model because all the indicators of the S-ECNN model are the highest. Specifically, our concern is whether the model can accurately identify the same fault category and whether the model can effectively distinguish different fault categories. The model is not directly used for prediction. In this section, _recall_ is calculated as the number of correct identifications of the same fault category divided by the number of given same-fault sample pairs, indicating the ability to identify the same fault category. _Specificity_ is calculated as the number of correct identifications of the different fault categories divided by the number of given different-fault sample pairs, indicating the ability to distinguish different alarm sequences. The S-ECNN achieves a _recall_ of 89.5% and a _specificity_ of 86.3%, both of which are satisfied. Owing to the imbalanced data, _precision_ is not good. However, in this section, _precision_ is calculated as the number of correct identifications of the same fault category divided by the number of sample pairs predicted as the same fault category, indicating the ability for prediction. Therefore, compared with _precision_, we pay more attention to _recall_. Additionally, _F1-score_ is also given considering imbalanced classification. Compared with the S-ECNN-rand model, the _F1-score_ of the S-ECNN model increases by 12.9%. Therefore, the collaborative use of the labeled and the unlabeled alarm sequences can effectively improve the distinguishing ability.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & _Accuracy_ & _Recall_ & _Precision_ & _Specificity_ & _F1-Score_ \\ \hline S-ECNN-rand & 78.7\% & 84.2\% & 38.1\% & 77.8\% & 52.5\% \\ S-ECNN-static & 79.4\% & 73.7\% & 37.8\% & 80.3\% & 50.0\% \\ S-ECNN & 86.8\% & 89.5\% & 51.5\% & 86.3\% & 65.4\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: The comparing indicators for distinguishing different alarm sequences.  
Figure 10: The confusion matrices for distinguishing ability evaluation.
# CoAR: Congestion-Aware Routing Protocol for Low Power and Lossy Networks for IoT Applications
## 4 Proposed Congestion-Aware RPL
### Congestion Detection
When the 50% of the queue (24 packets) is full the node begins the queue measurement. First, it checks the congestion degree, in this example the queue filling ratio is double that means when a single packet will exit from queue two packets will enter in the queue. Thus, \(CD(i)=\
icefrac{{1}}{{2}}\) and the remaining queue size is 24 packets. The congestion threshold can be calculated according to Equation (21) i.e., \(A_{\tau}=\
icefrac{{1}}{{2}}\times 24=12\) packets. Now, with this ratio node can hold 12 packets in the next time interval and total packets in the queue will be 36. Thus, in this example, the threshold (\(A_{\tau}\)) is set to 75%. When the queue occupancy exceeds the threshold, a node triggers the parent change procedure to alleviate the congestion problem. By using the adaptive threshold-based congestion detection mechanism the proposed algorithm is able to face the congestion situations successfully.
## 2 Experimental Setup and Proposed Method
### Economic Indicators  
#### 2.3.1 Levelized Cost of Energy  
LCOE is a metric used to measure the lifetime cost of energy generation by a given system. The LCOE considers system-related costs, including installation, operation, maintenance, and fuel. Therefore, it is used to quickly compare the economic value and viability of systems before investing. The LCOE is calculated by dividing the present value of all costs associated with producing 1 kWh of electricity by a given system by the total number of kilowatt hours produced over the life of the system. One of the important considerations when calculating the LCOE is the determination of the discount rate. The discount rate depends on the cost of capital and the assessment of financial risk, including the balance between debt financing and equity financing. Generally, LCOEs are calculated over 20-to-40-year lifetimes and are provided in currency units per kilowatt hour. It is important to note that the LCOE, used for investment planning and consistent comparison of different electricity generation methods, as a single figure does not provide a complete picture of everything that influences the investment decision [59]. The LCOE is calculated by Equation (9).  
\[LCOE=\frac{\sum_{t=1}^{n}\frac{I+M_{t}+F_{t}}{(1+r)^{t}}}{\sum_{t=1}^{n}\frac{E_{ t}}{(1+r)^{t}}}, \tag{9}\]  
where \(I_{t}\) is the investment expenditures in year t (including financing), \(M_{t}\) is the operations and maintenance expenditures in year t, \(F_{t}\) is the fuel expenditures in year t, \(E_{t}\) is the electricity generation in year \(t\), \(r\) is the discount rate, and \(n\) is the life of the system.
## 6 Experimental Results and Discussion
### Influence of Fusion of Different Measuring Points
#### 6.5.2 Influence of the Fusion of Different Measuring Points  
In this section, the focus was on estimating the influence of different measurement points. From the above subsection, it can be known that when the number of measurement points is 7, the model has the best diagnostic accuracy. Therefore, the number of measuring points was fixed at 7 and channel 8, channel 6, channel 4, channel 3, and channel 1 measuring points were deleted to respectively train and test. Next, the accuracy of these cases was compared and the situation with the highest accuracy was found.  
As shown in Figure 17, when the key phase signal of the channel 8 was deleted, the model performance was the best. However, when the model with the other channel was deleted, the performance decreased. This phenomenon indicates that only the signal of channels 1 to 7 contains characteristic information handy for fault diagnosis. Combined with the conclusion of the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number of Measuring Points** & **4** & **5** & **6** & **7** & **8** \\ \hline Deleted Channel & 3,5,7,8 & 3,6,8 & 3,8 & 8 & Null \\ Training Parameters & 396,796 & 492,028 & 587,260 & 682,492 & 777,724 \\ Training Time & 139.8 & 170.3 & 203.3 & 246.8 & 303.4 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Number of training parameters and training time for different measuring points.  
previous section, it can be known that reasonable selection and deletion of measuring point signals can not only reduce the training time but also improve the accuracy of the experiment.
## 4 Results and Discussion  
The performance of the category detection model is compared to that of other approaches in this section. Meanwhile, the outcomes of fashion category prediction using YOLOv4 and YOLOv3 are compared and studied. The following indicators are used to assess the efficacy of neural network models: For binary classification issues, samples can be classified as true positive (TP), false positive (FP), true negative (TN), or false negative (FN) based on the model's combined ground truth and predicted class. The detailed equations can be seen in (12) and (13).  
\[Precision\ (P)=\frac{TP}{TP+FP} \tag{12}\]  
\[Recall\ (R)=\frac{TP}{TP+FN} \tag{13}\]
## 3 Experimental Methodology  
### Rock Specimens and Sample Preparation  
Siltstone samples from the Eidsvold basin were collected from Queensland, Australia. Based on digital microscope observations, this particular type of siltstone is almost homogeneous. According to X-ray diffraction (XRD) analysis, it consists of 43% quartz and 40% kaolinite, and according to mercury intrusion porosimetry (MIP) tests, its porosity is about 20%. The collected siltstone blocks were first cored to 54 mm diameter samples and then cut to 90 mm lengths. The two end surfaces of the samples were carefully ground to produce parallel surfaces, using the diamond grinding machine available in the Monash University Deep Earth Energy Research Laboratory (DEERL). In order to inject the fracturing fluid and to simulate the wellbore, sample centres were drilled up to mid-depth using a 4-mm drill bit. The drilling was carried out in the Civil Engineering Workshop at Monash University, to make homogeneous holes in all the samples, and the hole dimensions were 45 mm deep and 4 mm in diameter. During the drilling process, lubricant and a very slow drilling speed were used to prevent damage to the samples, which also prevented the initiation of micro-fractures. According to [PERSON] et al. [23] fracture initiation in a homogeneous and smooth borehole wall is quite difficult and therefore the axial notch created by the drill bit at the centre of the sample is kept as it is to promote the fracture initiation. After preparing the drill hole, compressed air was used to clean the hole by removing dust and small particles and the five prepared samples were then washed and stored in an oven at 40 \({}^{\circ}\)C for four days to remove moisture. One sample was used for intact permeability testing and the other four for the fracturing tests and fracture permeability tests.
# A Lightweight Reconstruction Model via a Neural Network for a Video Super-Resolution Model
## 3 Results
### Datasets
#### 3.1.2 Vid 4  
We chose the Vid 4 [3] dataset for evaluating our model. Vid 4 is a widely recognized video super-resolution test set consisting of four scenes with unique motion and occlusion characteristics. The four video sequences in this dataset are 'calendar' (41 frames, 576 \(\times\) 720 resolution), 'city' (34 frames, 576 \(\times\) 704 resolution), 'foliage' (49 frames, 480 \(\times\) 720 resolution), and 'walk' (47 frames, 480 \(\times\) 720 resolution).
###### Abstract  
Integrated circuits suffer severe deterioration due to single-event upsets (SEUs) in irradiated environments. Spin-transfer torque magnetic random-access memory (STT-MRAM) appears to be a promising candidate for next-generation memory as it shows promising properties, such as non-volatility, speed, and unlimited endurance. One of the important merits of STT-MRAM is its radiation hardness, thanks to its core component, a magnetic tunnel junction (MTJ), being capable of good function in an irradiated environment. This property makes MRAM attractive for space and nuclear technology applications. In this paper, a novel radiation-hardened triple modular redundancy (TMR) design for anti-radiation reinforcement is proposed based on the utilization of STT-MTJ devices. Simulation results demonstrate the radiation-hardened performance of the design. This shows improvements in the design's robustness against ionizing radiation.  
single-event upset (SEU); triple modular redundancy (TMR); magnetic tunnel junction (MTJ); ionizing radiation; radiation hardening +
Footnote †: journal: Applied sciences