esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Recurrent Neural Network-Based Hybrid Modeling Method for Digital Twin of Boiler System
### The Construction of DTM Framework for Boiler System
#### 3.2.2 The DTM Framework with Basic Information  
The purpose of the DTM established in this paper is to reflect the dynamic processes of the physical boiler system in real time accurately, as shown in Figure 3. The physical boiler system includes pulverizing system, furnace and drum. Initially, the pulverized coils in coal mills are sent into the furnace by the primary air and mixed with the secondary air for combustion. Then, the water in the drum is heated to saturated vapor through the radiation energy generated by fuel combustion in furnace. Finally, the saturated vapor flowing out  
Figure 1: The design process of digital twin model for boiler system.  
of the drum is heated into super-heated vapor in the super heater. The inputs of each device in Figure 3 include not only the outputs of the front device, but also the outputs of other devices.  
Figure 3: The DTM framework for boiler system.  
Figure 2: Three hybrid modeling methods. (**a**) Hybrid modeling method with parameter identification only. (**b**) Hybrid modeling method with data-driven identification only. (**c**) Hybrid modeling method with both parameter and data-driven identification.  
In DTM of boiler system, the dynamic model corresponding to each device is established. The usage mode of DTM is continuous because the dynamic processes of real physical system need to be reflected synchronously. To identify the suitable technologies for the DTM development, we need to propose the requirements for DTM. The first issue to address is that of the way to achieve iterative updating of DTM parameters using input and output data while ensuring the interpretability of DTM. The solution proposed in this paper is to establish the mechanism model of the boiler system and describe it through neural network structure. In this way, the parameters in the mechanism model can be updated iteratively through the backpropagation algorithm, while the interpretability will not deteriorate. Since the outputs of the previous time need to be used, it is a better way to describe the mechanism model through recurrent neural network (RNN). The second requirement for DTM is to ensure the high fidelity after adopting the simplified mechanism model. Then, the LSTM-based error-compensation model is formed to reduce the predictive residual of the mechanism model.
# IBA myQA SRS Detector for CyberKnife Robotic Radiosurgery Quality Assurance
## 2 Materials and Methods
### Patient Plan QA Verification  
Patient treatments clinically delivered with CyberKnife(r) were retrospectively checked by administering the same beam geometry and MUs to the myQA SRS(r) phantom. All patients gave written informed consent, which included anonymized data usage for research purposes.  
As in our standard of practice with pre-treatment QA performed with other detectors, treatment plans were rigidly registered to the myQA(r) SRS phantom so that patient planning target volume (PTV) and array center-of-mass overlapped. When necessary, further translational adjustments were manually applied in order to satisfy fiducial marker pattern requirements with respect to the CyberKnife image guidance system (at least 3 fiducials contemporarily visible on the imaging X-ray projections). The planar dose (Ray Tracing algorithm) corresponding to the CMOS device was then exported from the TPS and compared to myQA SRS reading. As suggested by AAPM Task Group No. 218 recommendations [14], a dose calibration verification was performed before each measurement session to factor in the daily variation of linac output.  
The agreement between measured and TPS dose distributions was evaluated using myQA(r) Platform software to calculate the Gamma Index with a local dose-difference of 3% and a distance-to-agreement of 1 mm criteria [15].  
Plans for various intracranial target sizes (PTV volumes ranging from a few to several cubic centimeters) and locations were investigated to evaluate the detector's performance on a more comprehensive sample of treatment doses (from 5 to 75 Gy prescription dose) and beam ballistics.  
Patient Plan QA Verification after Beam Pruning  
Taking into account the device's angular response, beams above a threshold angle were removed, and the resulting QA plan was also calculated and delivered. The same Gamma Index criteria as above were employed for evaluation purposes.
## 3 Methodology
### Performance Evaluation  
The performance metrics used in this research played crucial roles in evaluating the effectiveness and accuracy of the classifiers. Several metrics were employed, including accuracy, recall, precision, F1-score, Cohen's kappa \((\kappa)\), and AUC-ROC. These metrics provided valuable insights into different aspects of the classifiers' performance. The evaluation was based on the confusion matrix shown in Table 2, which depicts the classification results in terms of true positives \((TP)\), true negatives \((TN)\), false positives \((FP)\), and false negatives \((FN)\). \(TP\) represents the instances that were correctly predicted as the positive class, while \(TN\) represents instances that were correctly predicted as the negative class. \(FP\) are instances that were incorrectly predicted as the positive class and \(FN\) are instances that were incorrectly predicted as the negative class.  
\[\text{Accuracy}=\frac{TN+TP}{TN+FP+TP+FN}\times 100\% \tag{1}\]  
\[\text{Precision}=\frac{TP}{TP+FP}\times 100\% \tag{2}\]  
\[\text{Recall}=\frac{TP}{TP+FN}\times 100\% \tag{3}\]  
\[\kappa=2\times\frac{(TP\cdot TN-FP\cdot FN)}{(TP+FP)\cdot(FP+TN)+(TP+FN)\cdot( FN+TN)} \tag{4}\]  
\[\text{F1-score}=\frac{2(Precision\times Recall)}{Precision+Recall} \tag{5}\]  
AUC-ROC: The area under the ROC curve (AUC-ROC) is a metric that quantifies the performance of a classification model across all possible classification thresholds. It represents the trade-off between the true positive rate and the false positive rate as the classification threshold varies from 0 to 1. An AUC-ROC value of 1 indicates a flawless model, whereas an AUC-ROC value of 0.5 indicates a subpar model [46].  
PRC: A precision-recall curve (PRC) is a graphical representation that depicts the relationships between precision and recall for different classification thresholds in a binary classification problem. It illustrates how changes in the classification threshold impact the trade-off between precision and recall [47].  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Predicted Positive** & **Predicted Negative** \\ \hline
**Actual Positive** & True Positive (TP) & False Negative (FN) \\
**Actual Negative** & False Positive (FP) & True Negative (TN) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The confusion matrix.
###### Abstract  
Matrix factorization based methods have widely been used in data representation. Among them, Non-negative Matrix Factorization (NMF) is a promising technique owing to its psychological and physiological interpretation of spontaneously occurring data. On one hand, although traditional Laplacian regularization can enhance the performance of NMF, it still suffers from the problem of its weak extrapolating ability. On the other hand, standard NMF disregards the discriminative information hidden in the data and cannot guarantee the sparsity of the factor matrices. In this paper, a novel algorithm called \(\ell_{2,1}\) norm and Hessian Regularized Non-negative Matrix Factorization with Discriminability (\(\ell_{2,1}\)HNMFD), is developed to overcome the aforementioned problems. In \(\ell_{2,1}\)HNMFD, Hessian regularization is introduced in the framework of NMF to capture the intrinsic manifold structure of the data. \(\ell_{2,1}\) norm constraints and approximation orthogonal constraints are added to assure the group sparsity of encoding matrix and characterize the discriminative information of the data simultaneously. To solve the objective function, an efficient optimization scheme is developed to settle it. Our experimental results on five benchmark data sets have demonstrated that \(\ell_{2,1}\)HNMFD can learn better data representation and provide better clustering results.  
Keywords:non-negative matrix factorization; Hessian regularization; Discriminability; clustering +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Data Processing  
First, the position of the total station was computed using the free position method and auxiliary points determined by the Trimble R2 GNSS receiver. All coordinates of other points (GCPs, control points) were calculated using the polar method. The data acquired with Leica P40 were processed in the Leica Cyclone software ver. 2023.0.1. The individual scans were automatically georeferenced to the black-and-white GCPs. The average registration error was 1.8 mm.  
The Zeb Horizon data were first processed in the local coordinate system in the manufacturer-provided software GeoSLAM Connect ver. 2.3.0. To be able to compare the cloud with the reference one, it was transformed into the Czech coordinate system using square high-reflection targets with georeferenced centers serving as GCPs. These targets were used, for example, in [33], where the transformation method is described in detail. Figure 6 shows the scanned target and its color coding based on the intensity. In principle, the points representing the targets were filtered from the cloud, their centers were determined as the centers of gravity, and these coordinates (georeferenced previously with the total station) were used for spatial transformation (translation in x,y,z-axes, rotation about the same axes). Out of the ten horizontal targets (1001-1010), seven that were sufficiently captured by the SLAM scanner were used for the transformation.
## 3 High-Dimensional Multi-Objective Sparrow Search Algorithm
### Time Complexity Analysis  
Assume that \(M\) is the goal number for the high-dimensional multi-objective optimization problem, \(N\) is the sparrow population size, and \(D\) is the decision number. The time complexity is discussed in two parts: the initial pertains to the temporal complexity of the adaptive reference point method, while the other is that of the sparrow optimization algorithm. The event complexity of the original NSGA-III algorithm is \(O(N^{2}\log^{M-2}N)\) or \(O(N^{2}M)\), and the improved adaptive reference point strategy adds comparison operation at two stages, so the time complexity of the entropy difference \(\Delta t^{t}\) between the two generations is calculated as \(O(N\log N)\); in the reference point screening stage, the time complexity is \(O(N\log N)\); therefore, in the worst case, the time complexity of the improved adaptive reference point strategy is \(\max\Bigl{\{}O(N^{2}\log^{M-2}N),\ O(N^{2}M),\ O(N\log N)\Bigr{\}}\). The event complexity of the initial sparrow search algorithm is \(O(DN)\), and the time complexity of the polynomial variation operation is \(O(DN^{2})\). Considering the time complexity of the two parts, the overall time complexity of the algorithm is \(\max\Bigl{\{}O(N^{2}\log^{M-2}N),\ O(N^{2}M),\ O(N\log N),\ O(DN^{2}),\ O(DN) \Bigr{\}}\).  
```
Input: initialized population \(P_{0}\); population size \(N\); target number \(M\); decision variable dimension \(D\); current evolutionary algebra \(t\); population's maximum evolution algebra \(\max T\), constrained boundary \(ub\) and \(lb\); warning sparrow number \(SD=0.2N\); warning value \(R_{2}\). Output: Pareto optimal frontier. (a) Initialized population \(P_{0}\) and initial reference point \(Z_{0}\); (b) According to Formulas (7) and (8), calculate the threshold, initialized reference point \(Z_{0}\), and the total number of individuals associated with the reference point Zsum = 0; (c) For \(t=1\): \(\max T\); (d) Sort the population by non-domination, select the elite individual leader, and select the best individual and the worst individual from the results; (e) Calculate \(\Delta e\) and judge the population scale factor \(\gamma\); (f) For \(i=1:\gamma N\) (g) Update the discovered's location in the sparrow population according to Formula (9); (h) End for (i) For \(i=\gamma N:N\) (j) Add Cauchy variation operator and update the follower's position of sparrow population according to Formula (11); (k) End for (l) For \(i=1:SD\) (m) Update sparrow population location according to Formula (3); (n) End for (o) Form a population \(P_{t}\) with the size of \(N\) (p) Calculate the entropy \(E_{r}\) of the population \(P_{t}\) according to Formula (5) and \(\Delta e\); (q) If \(\Delta e^{t}<\mu\) (r) Delete \(Z_{t}-N\) reference points with the least number of associated individuals from Zsum; (s) End if (t) \(t=t+1\); (u) End for (v) Output the optimal solution.
```  
**Algorithm 1**MoOISSA  
Figure 1: MaOISSA Algorithm flow chart.
# Experimental Study on the Effect of Wind Speed on Hail Impacts
## 3 Experimental Analysis and Results
### Formatting of Mathematical Components  
To further investigate the coupling mechanisms between wind speed and hailstone velocity, this study selected 3.2 cm hailstones as the test subject and analyzed the patterns of the variation in the impact forces under two wind speeds (\(V_{w}\) = 0 m/s, \(V_{w}\) = 13 m/s) and varying hail velocities, as shown in Figures 7 and 8.  
\begin{table}
\begin{tabular}{c c c} \hline
**Wind Velocity (m/s)** & **Hail Diameter (cm)** & **Peak Force (N)** \\ \hline
0 & 2.4/2.8/3.2 & 110.9/139.7/159.2 \\
13 & 2.4/2.8/3.2 & 126.0/173.6/190.3 \\ \hline \end{tabular}
\end{table}
Table 4: The effect of wind speed on the peak impact force of hailstones.  
Figure 6: Peak force curves for different hail diameters.  
From the figures, it is evident that hail velocity significantly influences the impact duration: higher hail velocities result in shorter impact durations, faster attainment of peak impact forces, and greater peak force magnitudes. The effect of wind speed on hail impact forces is primarily reflected in the peak impact force. As illustrated, higher wind speeds lead to larger peak impact forces, with increases exceeding 15% in magnitude. These results unequivocally demonstrate that wind speed is a critical factor influencing hail impact forces and cannot be overlooked in structural risk assessments.
## 4 Conclusions  
This study mainly focuses on rural residential buildings and establishes a typical building energy efficiency retrofit model based on field investigations. Through theoretical simulations, the thermal comfort and energy performance of the rural buildings with improved insulation are analyzed. There are three main conclusions obtained from the simulations in the article.  
1. Setting the building model parameters to control the original building and the specification building are at the same indoor air temperature. The APMV of the original building is always lower than the standard building, indicating that the indoor temperature is not representative of the thermal comfort of the room.
2. Adding a building envelope can reduce the cumulative heat load while enhancing indoor thermal comfort. Compared with the original building with the same thermal comfort value, the energy saving rate after adding envelope insulation is 67%.
3. By adjusting the thickness of the insulation layer, the paper obtains the relationship between the heat transfer coefficient and the indoor APMV value. Additionally, according to this equation, the heat transfer coefficient of the envelope should not be higher than 0.5 W/(m\({}^{2}\cdot\)K) when the room is in comfort.  
The meteorological parameters in this paper are based on a typical meteorological year rather than a specific year; the indoor temperature is based on a survey of thermal expectation temperatures from previous literature; and the practices of the envelope are based on the actual data, so this study has certain references for the retrofitting of rural buildings in north China.  
[PERSON] conceptualized the study to evaluate rural building retrofits with thermal comfort, developed the research method, supervised the co-authors and edited the manuscript. [PERSON] did the formal analysis, investigation and wrote the original draft. [PERSON] did data curation, validation and visualization work. [PERSON] did the funding acquisition, visualization and review-editing work. [PERSON] provided building information resources, validated the paper and supervised the research team. All authors have read and agreed to the published version of the manuscript.  
This research is supported by Pyramid Talent Training Project of Beijing University of Civil Engineering and Architecture (No.JDJQ20200303), The Fundamental Research Funds for Beijing University of Civil Engineering and Architecture(No.X19010,X18202) and National Natural Science Foundation of China(No. 51708013).  
Not applicable.  
Figure 8: Relationship between indoor thermal comfort and heat transfer coefficient.
# Performance of Narrow Band Wide Area Networks with Gateway Diversity
## 3 System Model
### Interference Model  
In this section, the interference model is presented. Packets can potentially collide due to an un-coordinated ALOHA-based channel access scheme [14]. To simplify the EN operation, the UL and the DL share the same RF channel (902 MHz) and use the same spreading factor. The chirp symbols in the DL transmissions are IQ inverted, thereby providing the orthogonality with respect to UL transmissions. For such a scenario, as the results in Figures 5 and 6 show, the worst-case SIR threshold that is required to achieve a BER target of \(10^{-4}\) is \(\mathrm{SIR_{th}}=0\) dB within end-nodes in the UL direction and \(\mathrm{SIR_{th}}=-20.7\) dB in between the UL and DL traffic thanks to IQ inversion. These thresholds correspond when the LoRa modulation is used with an SF = 11 and with a 500 kHz bandwidth. During the analysis for the system PER, any collisions occurring with a SIR \(<\)\(\mathrm{SIR_{th}}\) are assumed to cause a packet loss. This is illustrated in Figure 7. The WAN operates based on the LoRa modulation with either SF-11 at \(\sim\)2 kbps PHY bitrate assuming a code rate of 4/5, or with SF-8 at \(\sim\)16 kbps PHY bitrate.
# AWMC: Abnormal-Weather Monitoring and Curation Service Based on Dynamic Graph Embedding
## 2 Related Work
Lastly, for each tool's site technology, most tools drew from external libraries to support visualization in their website. In addition, 80% of them use JavaScript-based open web maps on their website. We compare the AWMC system with existing weather systems regarding the four aspects of function, method, feature extraction, and database. The functions of existing weather systems are focused on weather forecasting and monitoring. However, compared with existing systems, the AWMC system not only monitors changes in weather data, but also provides a function for detecting abnormal weather, giving people a better understanding of weather conditions. In addition, existing weather forecasting systems do not extract features from weather data, which may lead to a decrease in the accuracy of forecasts. In the AWMC system, we extract relational features between weather time series. More details of the comparison are shown in Table 1.
## 6 Fast Terminal Sliding Mode Control with ESO
### Design of ESO  
The ESO estimates system states and the total disturbance. The electro-hydraulic servo loading system is a fifth-order system, and the third-order and above are regarded as part of the total disturbance. The system state equation is  
\[\left\{\begin{array}{l}\dot{x}_{1}=x_{2}\\ \dot{x}_{2}=f+bu\\ y=x_{1}\end{array}\right. \tag{7}\]  
where \(x_{2}\) is the system output differential value, \(f\) is the system total disturbance, including external disturbances (road surface reaction forces), unmodeled dynamics (valve nonlinearity), parameter uncertainties (changes in the elastic modulus of oil), and system coupling effects. It is the sum of all deviations between the actual system and the ideal mathematical model. The total disturbance \(f\) disrupts the ideal mathematical model of the system, causing deviation terms in the state equations. \(b\) is the compensation factor, \(u\) is the system input control quantity, \(x_{1}\) is the system output.  
Assuming that the derivative of \(f\) exists and is bounded, it is regarded as the third state variable by the ESO:  
\[\left\{\begin{array}{l}\dot{x}_{1}=x_{2}\\ \dot{x}_{2}=x_{3}+bu\\ \dot{x}_{3}=\dot{f}\\ y=x_{1}\end{array}\right. \tag{8}\]  
In this paper, a nonlinear ESO is used, which is constructed as follows [33]:  
\[\left\{\begin{array}{l}\varepsilon=z_{1}-y\\ \dot{z}_{1}=z_{2}-\beta_{1}\varepsilon\\ \dot{z}_{2}=z_{3}-\beta_{2}fal(\varepsilon,\alpha_{1},\delta)+bu\\ \dot{z}_{3}=-\beta_{3}fal(\varepsilon,\alpha_{2},\delta)\end{array}\right. \tag{9}\]  
where \(z_{1}\) is the observed value of the system output \(x_{1}\), \(z_{2}\) is the observed value of the system output differential value \(x_{2}\), \(z_{3}\) is the observed value of the system total disturbance; \(\beta_{1},\beta_{2},\beta_{3},\delta,\alpha_{1},\alpha_{2}\) are adjustable parameters for ESO, where generally, \(\alpha_{1}\) is taken as 0.5, and \(\alpha_{2}\) is taken as 0.25. \(fal(\cdot)\) is defined as  
\[fal(\varepsilon,\alpha,\delta)=\left\{\begin{array}{l}|\varepsilon|^{\rm{* }}{\rm sgn}(\varepsilon),\ \ |\varepsilon|>\delta\\ \frac{\varepsilon}{\delta^{1-\alpha}},\ \ \ \ \ \ |\varepsilon|\leq\delta\end{array}\right. \tag{10}\]  
The ESO is discretized to obtain  
\[\left\{\begin{array}{l}\varepsilon=z_{1}(k)-y(k)\\ z_{1}(k+1)=z_{1}(k)+h[z_{2}(k)-\beta_{1}\varepsilon]\\ z_{2}(k+1)=z_{2}(k)+h[z_{3}(k)-\beta_{2}fal(\varepsilon,\alpha_{1},\delta) +bu(k)]\\ z_{3}(k+1)=z_{3}(k)-h\beta_{3}fal(\varepsilon,\alpha_{2},\delta)\end{array}\right. \tag{11}\]  
where \(h\) is the sampling period.
# Path Planning for Yarn Changing Robots Based on NRBO and Dynamic Obstacle Avoidance Strategy
## 2 Path Planning Problem and Modeling in the Knitting Workshop
### Environment Modeling of the Workshop  
In the field of path planning, the Occupancy Grid Map is a widely adopted map representation technique [12]. It is popular due to its precise environmental modeling capabilities, real-time updating features, adaptability to different environment scales, and seamless integration with the Robot Operating System (ROS). Using such a map, the robot can perform path planning and obstacle avoidance more effectively, ensuring the successful execution of path planning tasks. This paper employs a three-dimensional grid map for modeling, where the entire grid is composed of tightly arranged cubic cells. Each grid cell records its occupancy state, classified into two categories: occupied and free. The occupied state signifies that the grid cell is obstructed by an object or obstacle, preventing the robot from passing through. In contrast, the free state indicates that the cell is clear, allowing the robot to navigate through it.  
This study uses the bobbin replacement process illustrated in Figure 3 as a model for environmental construction. To minimize the computational burden of subsequent algorithms while maintaining the effectiveness of path planning, the cylindrical shape of the yarn bobbins and the irregular shapes of the preparation and yarn racks are simplified as much as possible. The simplified three-dimensional grid map of the knitting production workshop is shown in Figure 4. Two cylinders represent the positions of the yarn bobbins, while eight cuboids represent the yarn racks in the occupied state, marking areas where the robot cannot traverse. Small cuboids signify dynamic obstacles on the robot's path. To ensure obstacles do not block the planned path, some cuboids are displayed using a transparent perspective.  
Figure 4: Simplified 3D grid map of the knitting workshop.