esa-sceva/satcom-chunk-collection huggingface dataset:
# A Simplified Matrix Formulation for Sensitivity Analysis of Hidden Markov Models
## 3 Sensitivity Analysis of Filtering Probability
### Observation Parameter Variation
#### 3.3.1 Observation Matrix Decomposition
```
0:\(A,O,\Gamma,\mathbf{e}\), and \(\theta_{o}\)
0:\(F^{1},\cdots,F^{t}\)
1:\(\tilde{O}\leftarrow\mathcal{O}\)
2:\(\tilde{O}_{r,s}\gets 0\)
3:\(\tilde{O}_{r,s}\leftarrow\tilde{O}_{r,s}\cdot/(1-o_{r,s})\)
4:\(\tilde{O}\gets O-\mathcal{O}\)
5:\(\tilde{O}_{r,s}\leftarrow\tilde{O}_{r,s}\cdot/o_{r,s}\)
6:\(\tilde{O}^{1}\gets diag(\tilde{O}_{,\mathbf{e}^{1}})\)
7:\(\tilde{O}^{1}\gets diag(\tilde{O}_{,\mathbf{e}^{1}})\)
8:\(Ftmp_{1}\leftarrow[\tilde{O}^{1}*\Gamma^{\prime},zeros(n,1)]\)
9:\(Ftmp_{2}\leftarrow[zeros(n,1),\tilde{O}^{1}*\Gamma^{\prime}]\)
10:\(F^{1}\gets Ftmp_{1}+Ftmp_{2}\)
11:for\(k=2\) to \(t\)do
12:\(\tilde{O}^{k}\gets diag(\tilde{O}_{,\mathbf{e}^{k}})\)
13:\(\tilde{O}^{k}\gets diag(\tilde{O}_{,\mathbf{e}^{k}})\)
14:\(Ftmp_{1}\leftarrow[\tilde{O}^{k}*A^{\prime}*F^{k-1},zeros(n,1)]\)
15:\(Ftmp_{2}\leftarrow[zeros(n,1),\tilde{O}^{k}*A^{\prime}*F^{k-1}]\)
16:\(F^{k}\gets Ftmp_{1}+Ftmp_{2}\)
17:Return\(F^{1},\cdots,F^{t}\)
```  
**Algorithm 3** This computes the coefficients of the forward (filtering) probability sensitivity function \(p(x_{v}^{t},\mathbf{y}_{\mathbf{e}}^{1:t})(\theta_{o})\) with \(\theta_{o}=o_{r,s}\) in forward matrices \(F^{k}\) for \(k=1,\cdots,t\) using SMF.
## 3 Test Results and Analysis
### Ductility and Energy Dissipation
The accumulated energy dissipation of each specimen is shown in Table 6 and Figure 12. Compared to specimen CFST-N, specimens CFST-HS through CFST-MC, with different structures, exhibit significantly improved energy dissipation capability. When the specimens reach their peak loads, the accumulated energy dissipation \(\mathrm{E_{P}}\) improves by 44.2%, 51.5%, 59.8%, 96.8%, and 95.3%. The specimen energy dissipation \(\mathrm{E_{t}}\) during the entire process improves by 3.1%, 6.8%, 13.7%, 28.4%, and 45.4%. The results show that the structure has a higher impact on \(\mathrm{E_{P}}\) than \(\mathrm{E_{t}}\). Specim CFST-SR and specimen CFST-DT have the same level of steel consumption. However, the \(\mathrm{E_{P}}\) and \(\mathrm{E_{t}}\) of specimen CFST-DT are 37% and 14.7% higher than those of specimen CFST-SR. The reason is that the embedded circular steel tube increases the contact area between the steel tube and concrete. Therefore, the structure dissipates more absorbed energy via the bonding slip effect. Specim CFST-MC has the highest section steel ratio and the highest level of accumulated energy dissipation.
## 4 Case Study
The SHyFTA model of Figure 7b has been coded using the SHyFTOO library. The Monte Carlo simulation process has been set for running 100,000 iterations. This simulation campaign aims to compare the results of the DFT of Figure 7a versus the SHyFTA model of Figure 7b. For the former, due to the limitations of the DFT modelling, it was assumed to adopt only a policy of type 2C that restores the system as good as new from the Crack Formation and the Debris Penetration, assuming the repair rates of Table 7.  
The SHyFTA model has been obtained from the Dynamic Fault Tree of Figure 7a by replacing the basic events BE3 and BE5 of the Debris Penetration and the Crack Formation with the corresponding hybrid basic events HBE3 and HBE5. In the SHyFTA model, these latter can be coded so as to hook the time variable to the actual aging of the basic event, and better model the characteristic nonlinear time deterioration of such events. In this way, the aging is modelled by the means of a Piecewise Deterministic Markov Process expressed with the following formula:  
\[\frac{\text{dL(t)}}{\text{dt}}=\text{ u(t)} \tag{8}\]  
where u(t) is a discrete stochastic variable that measures the instantaneous degradation rate of the component. As for the SHyFTA model, the simulation results will account for two preventive maintenance policies: 1P halves the effect of the Debris Penetration and the Crack Formation in the system (L(t) = L/2), whereas the preventive maintenances 2P restores the condition as good as new (e.g., L(t) = 0). The two preventives maintenance's take place every 1000 h. Likewise, the DFT simulation, the corrective approach 2C restores the Crack Formation and Debris Penetration aging as good as new. Figure 8 shows the results of the SHyFTA model of Figure 7b. In this case it is also possible to see that the preventive policy improves the system reliability. In particular,  
Figure 7: Fault Tree models of the Steam Turbine with condition monitoring sensors: (**a**) Dynamic Fault Tree model and (**b**) SHyFTA model. the policy 2P reduces the system unreliability from 8.74 \(\times\) 10\({}^{-3}\) to the value of 7.08 \(\times\) 10\({}^{-3}\). The policy 1P performs in between and presents a system unreliability of 8.05 \(\times\) 10\({}^{-3}\).
# Intelligent Evaluation of Chinese Hard-Pen Calligraphy Using a Siamese Transformer Network
## 4 Results and Discussion  
### Chinese Character Detection and Recognition Experiments Analysis  
The Chinese character detection and recognition model was implemented using Python version 3.7 and PyTorch version 1.5.1. Libraries such as Numpy, Torchvision, and OpenCV-Python were installed to facilitate the detection and recognition of handwritten Chinese character samples. Following multiple rounds of testing, the model was trained using a learning rate of \(10^{-4}\) and a batch size of two.  
The Chinese character detection and recognition model was implemented using the YOLOv5 detection network. Alternatively, the usage of an image classification network can also achieve satisfactory Chinese character recognition results. For comparative experiments, the well-established and computationally efficient GoogLeNet network from the realm of classification networks was selected for evaluation. As depicted in Figure 11, the GoogLeNet network exhibited an optimal training set loss of 0.366, with the loss converging around 0.40 after approximately 20 epochs. By the time the model reached six epochs, the recognition accuracy on the validation set had reached one.  
In contrast, the training process of the YOLOv5 model, as depicted in Figure 12, illustrates the behavior of various loss components over training epochs. The \"train/box_loss\" gradually approached 0.005 as the epoch value neared 100, indicating the loss in predicting bounding boxes. Examining the \"train/obj_loss\", which represents the loss related to the presence of target objects, the optimal value achieved was 0.003. Similarly, the \"train/cls_loss\", indicating the loss pertaining to prediction results, reached its best value of 0.001. With an increase in the number of training epochs, precision, recall, and mAP values escalated, eventually reaching a maximum value of 1.  
Figure 11: GoogleNet training process.  
By observing Table 1, it is evident that the YOLOv5 model outperforms the GoogLeNet network in terms of precision, recall, and mAP metrics. Testing with the same sample as illustrated in Figure 13 clearly indicates the superior accuracy of the YOLOv5 model. Therefore, it can be concluded that the performance of the YOLOv5 model surpasses that of the GoogLeNet network in this task.  
The Chinese character detection and recognition network, based on YOLOv5, has achieved the task of detecting and recognizing Chinese characters. By utilizing the lightweight CSP-Darknet53 as the main feature extraction network, YOLOv5 reduces computational complexity, conserves storage space, and enhances inference speed. In comparison to the GoogLeNet network, this approach has resulted in a 0.10% improvement in precision, a 0.10% increase in recall, and a 0.25% enhancement in mAP metrics. These advancements signify the effectiveness of this method for Chinese character writing detection and recognition.
## 3 Methods
### Feature Extraction for Each Image Pixel
#### 3.2.4 Texture Features  
Image texture provides relevant characteristics of spatial structures. In this study, the aquaculture lands hold relative regular shapes and arrangements, which are reflected in the regular change of gray levels of images pixels. Considering the results of previous studies, we combined different kinds of textures to address the problem of insufficient accuracy and to reduce the interference of the surroundings.  
Gray Level Co-occurrence Matrix (GLCM) [56] is a method to analyze texture characteristics, and it calculates the correlation between two gray levels to reflect the comprehensive information about directions, intervals, and the changes in magnitude and speed. Some studies suggested that better recognition performance could be obtained by using extreme values to calculate texture features in different directions than in any direction or average direction. NDWI, NDBI, and backscattering coefficient of VV/VH were employed as input images for calculating GLCM, with gray level of 16, 150 \(\times\) 150 m sliding windows and four directions (Figure 4). The NDVI image was mainly used to distinguish aquaculture ponds and natural waters, and it was not applied to compute texture features in this study.  
Numerous texture feature bands (\(n=18\)) can be generated using GLCM method in GEE. Redundant textural measures are possibly complex and may reduce computing speed, which may influence the performance of classification. Following [PERSON]'s work [57], Angular Second Moment, Contrast, Correlation, Variance, Sum Average, Inverse Difference Moment, and Entropy were selected and computed from GLCM (Table 1), and their maximum and minimum values in four directions (Figure 4) were analyzed. Therefore, for each input image, a 14-dimension feature vector was obtained (7 measures \(\times\) 2 values).
## 2 Materials and Methods  
### Electrical Analysis of an SC Cell Operating at Constant Power  
A complete mathematical formulation for the electrical analysis of SCs operating at constant power is presented in [46] by the authors of this paper. Nevertheless, the main electrical variables involved in the charge and discharge of an SC cell at constant power will be hereafter described for the sake of clarity. As it can be seen in Figure 2, the cell is emulated by means of a RC-series electrical model, R being the internal resistance and C the rated capacitance, which is assumed to be constant. The remaining variables in Figure 2 are the internal voltage, u, the external voltage, u\({}_{\text{co}}\), and the discharge current, i.  
The initial internal voltage of the cell is called U\({}_{0}\). A discharge at constant power, P > 0, is assumed to begin at t = 0. From the mathematical formulation obtained in [46], a dimensionless variable, g\({}_{1}\)(t), can be defined as:  
\[\text{g}_{1}(\text{t})=-\text{W}_{-1}\Bigg{(}\frac{-\exp(\frac{-\text{g}(\text {t})}{2\cdot\text{R}\cdot\text{P}})}{2\cdot\text{R}\cdot\text{P}}\Bigg{)}, \tag{1}\]  
where W\({}_{-1}\)(x) is the secondary branch of the Lambert-W function. In the event of a charging process (P < 0), the main branch of the Lambert-W function, W\({}_{0}\)(x), must be used. The argument of this function includes another variable, g(t), measured in V\({}^{2}\), that varies over time according to:  
\[\text{g}(\text{t})=\text{U}_{0}^{2}+\text{U}_{0}\cdot\sqrt{\text{U}_{0}^{2}-4 \cdot\text{R}\cdot\text{P}}-4\cdot\text{R}\cdot\text{P}\cdot\ln\left(\text{U} _{0}+\sqrt{\text{U}_{0}^{2}-4\cdot\text{R}\cdot\text{P}}\right)+2\cdot\text{R} \cdot\text{P}\cdot\left[\ln(2)-1\right]-\frac{4\cdot\text{P}\cdot\text{P}}{C}. \tag{2}\]  
Figure 2: Discharge of an SC at constant power.  
As is shown in this equation, the linear function \(g(t)\) decreases over time for discharging processes and increases for charging. In order to obtain a clearer mathematical representation, it can be abbreviated as:  
\[g(t)=A-\frac{4\cdot P\cdot t}{C}, \tag{3}\]  
where \(A\) is the \(y\)-intercept of \(g(t)\),  
\[A=U_{0}^{2}+U_{0}\cdot\sqrt{U_{0}^{2}-4\cdot R\cdot P}-4\cdot R\cdot P\cdot \ln\left(U_{0}+\sqrt{U_{0}^{2}-4\cdot R\cdot P}\right)+2\cdot R\cdot P\cdot[\ln( 2)-1]. \tag{4}\]  
As is detailed in [46], all the electrical variables involved in the analysis of the cell charge and discharge at constant power, as well as time, can be expressed as a function of \(g_{1}(t)\). For instance, the power losses, \(P_{d}\), in \(R\) can be calculated as:  
\[P_{d}=\frac{P}{g_{1}}, \tag{5}\]  
and the internal voltage, \(u\), can be expressed as:  
\[u=\sqrt{R\cdot P\cdot g_{1}}+\sqrt{\frac{R\cdot P}{g_{1}}}. \tag{6}\]
# AERQ--A Web-Based Decision Support Tool for Air Quality Assessment
###### Abstract  
Technological advancements in low-cost devices, the Internet of Things (IoT), numerical models, big data infrastructures, and high-performance computing are revolutionizing urban management, particularly air quality governance. This study examines the application of smart technologies to address urban air quality challenges using integrated sensor networks and predictive models. The decision support system (DSS), AERQ, incorporates the AERMOD modeling tool, achieving a 10 m spatial and 1 h temporal resolution for air quality predictions. It processes hourly climate and traffic data via a high-performance computing (HPC) platform, significantly enhancing prediction accuracy and decision-making efficiency. AERMOD has been calibrated and validated for NO\({}_{2}\), showing a good performance against observations. Tested in Cagliari, Sardinia, Italy, AERQ demonstrated a 99% reduction in computation time compared to modern desktop systems, delivering detailed 5-year scenarios in under 15 h. AERQ equips stakeholders with air quality indices, scenario analyses, and mitigation strategies, combining advanced visualization tools with actionable insights. By enabling data-driven decisions, the system empowers policymakers, urban planners, and citizens to improve air quality and public health. This study underscores the transformative potential of integrating advanced technologies into urban management, providing a scalable model for efficient, informed, and responsive air quality governance.  
air quality; nitrogen oxides; atmospheric pollution; air quality model; AERMOD; GIS; WebApps; emissions; decision support system (DSS); traffic; urban air quality; Cagliari Article
# Micro-Blog Sentiment Classification Method Based on the Personality and Bagging Algorithm
## 5 Micro-Blog Sentiment Classification Experiment
### Comparison of Bagging Algorithm Integration Methods  
Experiments were used to verify the feasibility of applying the bagging algorithm. In the direct summation method, the weight of each classifier is kept equal (i.e.,\(q_{j}=\frac{1}{6}\)). In the weighted sum method, the weight of each personality set is determined using cross-validation. The main criterion is to make precision as high as possible. In order to limit the computational workload, we restricted the number of experiments to 30 and picked the set with the highest precision as the approximate optimum. The optimal weights obtained for each personality set are: \(q_{1}=0.16,q_{2}=0.23\), \(q_{3}=0.16,q_{4}=0.16,q_{5}=0.14,q_{6}=0.15\). \(q_{1}\), \(q_{2}\),..., \(q_{6}\) are the weight parameters of ALL, HC, HA, HE, LA, and LE. For comparison purposes, Table 4 lists some of the other weight settings.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Weight (\(q_{1}\),\(q_{2}\),\(q_{3}\),\(q_{4}\),\(q_{5}\),\(q_{6}\))** & **Precision** \\ \hline
0.16,0.23,0.16,0.16,014,0.15 & 96.91 \\
0.15,0.15,0.23,0.16,0.15,0.16 & 96.81 \\
0.14,0.16,0.25,0.15,0.15,0.15 & 96.81 \\
0.15,0.15,0.15,0.24,0.15,0.16 & 96.63 \\
0.15,0.27,0.15,0.14,0.15,0.14 & 96.55 \\
0.14,0.15,0.15,0.15,0.25 & 96.45 \\
0.16,0.16,0.16,0.21,0.15,0.16 & 96.43 \\
0.2,0.16,0.16,0.16,0.16,0.16 & 96.42 \\
0.16,0.16,0.15,0.16,0.21,0.16 & 96.41 \\
0.14,0.16,0.15,0.16,0.24,0.15 & 96.36 \\
0.14,0.15,0.14,0.15,0.14,0.28 & 96.27 \\
0.25,0.15,0.15,0.15,0.15 & 96.27 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Partial weight setting comparison.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Personality** & **ALL** & **HA** & **HC** & **HE** & **LA** & **LE** \\ \hline F1 score & 88.31 & 89.19 & 89.59 & 87.39 & 72.28 & 83.25 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of basic sentiment classifiers.  
The weight is usually proportional to the precision of the classifier on the training set; the higher the precision, the greater the weight. As shown in Table 3, the precision of the HC personality set is the highest, which consequently resulted in its weight being then largest. This is consistent with the results obtained in Table 4.  
The experimental results of the two integration methods are shown in Table 5. The weighted sum method has a higher F score. The summation method assumes that all classifiers have equal weights. However, in practical applications, different classifiers should have varying weights given their different degrees of importance. Therefore, the weighted sum method is recommended as the final integration method.
## 6 Open Issues and Future Trends
### Cyber-Physical Integration Towards Intelligence  
Future aircraft under DIMA architecture will increasingly rely on advancements of network and information technologies [73]. The available sensors, data concentrators and actuators establish a connection between the physical world and the cyber network. Specifically, the cyber-physical design and integration will give support for the physical world, such as monitoring of concurrent events, asset health monitoring and prognostics [74], which suggests data-driven approaches for modeling the quality measures of the aircraft, for example, reliability and maintainability, for understanding the dynamic interactions between different components in the DIMA architecture from a statistical view [75]. Powerful machine learning techniques can be leveraged to build the models and learn the association of components for predicting the failure rate and MTTR accurately [76], particularly withdeep learning [77]. In this case, rather than traditional physical equations, quality measures can be modeled by machine learning techniques, where training data can come from either discrete event simulations or real world scenarios. Potentially, the modeling of the quality measures can be more accurate and hence the solution of the DIMA optimization will be more reliable.
###### Abstract  
Chinese rural construction is currently booming, but faces considerable challenges in terms of energy performance. The objective of this research was to analyze the energy performance of tubular houses, which are a unique type of rural house in southern China, with a particular architectural form and environmental adaptations. Previous field measurements showed that there was much room for improvement, with both winter and summer cases requiring particular attention. Numerical simulations of the annual energy consumption were conducted using Open-Studio. The results show that various levels of reduction in energy consumption (varying from 1.6% to 30.5%) were achieved by combining different renovations. Among them, using solar energy with a sumroom was found to be the most effective approach, with an energy-saving rate of 28%, followed by the approach of attaching insulation to the walls and roof, with an energy-saving rate ranging from 13.2% to 30.5%. The integrated optimization measures had an energy-saving rate of 47.4% with a total renovation cost of CNY 41,143.1, and the payback period of investment was within five years. If a tubular house with improved thermal insulation can be inherited as a component in the process of urbanization, it will aid in energy conservation and natural ecosystem protection for southern China.  
tubular house; sustainability; energy performance; passive energy-saving technology; Open-Studio; integrated optimization Article
_Applied_  
_sciences_  
_Article_  
**The Relationships between Somatic Cells and Isoleucine, Leucine and Tyrosine Content in Cow Milk**  
**[PERSON] 1, [PERSON] 2, [PERSON] 3, [PERSON] 1, [PERSON] 1, [PERSON] 2, [PERSON] 1,* and [PERSON] 1**  
1 Department of Pharmaceutical Sciences, University of Perugia, Via Fabretti 48, 06123 Perugia, Italy; [EMAIL_ADDRESS] (F.I.); [EMAIL_ADDRESS] (L.P.); [EMAIL_ADDRESS] (E.C.); [PERSON].N.)  
2 Department of Veterinary Medicine, University of Perugia, Via San Costanzo 4, 06126 Perugia, Italy; [EMAIL_ADDRESS]  
3 School of Advanced Studies, University of Camerino, [PERSON] 55, 62032 Camerino, Italy; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS] (B.T.C.G.); [EMAIL_ADDRESS] (R.S.); Tel.: +39-075-5857929 (B.T.C.G.); +39-075-5857976 (B.T.C.G.); +39-075-5855161 (R.S.)  
Received: 10 December 2018; Accepted: 18 January 2019; Published: 21 January 2019  
**Featured Application: Monitoring the levels of the amino acids isoleucine, leucine, and tyrosine could represent a new potential tool for a rapid diagnosis of subclinical mastitis in cows and hence to qualify cow milk in association with the traditional somatic cell count-based method.**  
**Abstract: The early diagnosis of cow subclinical mastitis represents a pivotal factor for a prompt and adequate animal treatment. Although several methods are available, the somatic cells count (SCC) still remains the elective test directly carried out on milk samples. In mastitis affected cows (even at subclinical stages), altered concentrations of specific metabolites, including free amino acids, is a well-known occurrence. In order to define the relationships between the variation of the unbound amino acids content with the SCC value, a direct ion-pairing reversed-phase method based on the use of the evaporative light-scattering detector (IP-RP-HPLC-ELSD) was applied to 65 cow milk samples. The statistical analysis of variance (ANOVA) was pursued in order to find a correlation between the SCC value and the concentration of isoleucine (Ile), leucine (Leu), valine (Val) and tyrosine (Tyr). Samples were divided in two groups according to their SCC value: Group I comprised all ones with SCC < 400,000 cells/mL; Group II encompassed those with a SCC > 400,000 cells/mL. Statistical analyses highlighted significant differences in the content of the branched-chain amino acids Ile and Leu, between the two groups (\(p\) < 0.02* and <0.005**, respectively). This study confirms that a dysmetabolism of certain free amino acids parallels elevated SCC values.**  
**Keywords: branched-chain amino acids; cow milk; ion-pair reversed-phase liquid chromatography; somatic cells count; subclinical mastitis**