esa-sceva/satcom-chunk-collection huggingface dataset:
# Traffic Measurement and Congestion Detection Based on Real-Time Highway Video Data
## 2 Related Work
### Object Detection
#### 2.1.2 Edge Detection  
In [6], [PERSON] developed edge detection for grey-scale images because color information is not required. In principal, edges are locations where brightness changes abruptly, i.e., from white to dark or vice versa. An edge pixel has a gradient which is the rate of change and a direction or orientation. In most cases the direction is considered to be the direction of the largest growth. Moreover, [PERSON] [1] suggests that efficient edge detection is primarily based on good detection ability (low probability of wrongly marking non-edge points), localization (edge points located as close as possible the center of true edges) and providing a single response to a single edge point.  
According to [PERSON] [6] edge detection is typically performed using either the first or the second derivate operators. Additionally, it is also possible to combine both functions. However, to get the local maximum on the edge--this is where the rate of change is highest--the first derivate is used. The second derivate results in the zero-crossing on the edge. [PERSON] [1] describes different kind of operators, with some of the most famous and commonly used ones being the Sobel gradient, the Prewitt gradient and the Laplace operator.  
Edge detection is used as an underlying technology for a robust shape detection algorithm for two dimensional objects, as stated by [PERSON] et al. [7]. Boundary shape information such as rectangular shapes of vehicles are important indicators for recognition tasks in aerial imagery. Hence, edge detection is often used for such a purpose due to the fact that borders can be established easily. Furthermore, clustering multiple edges is needed to form rectangular shapes to finally detect vehicles. For the presented example dealing with vehicle detection four linearly elongated edge operators are used, each for one of the four sides of the vehicle.  
Another example is presented by [PERSON] et al. in [8], where the edge detection technique is used to obtain an edge set for each candidate text area of road signs. Based on this initial areas, further processing and clustering can be made to achieve the overall goal of detecting text on road signs. Furthermore, the proposed application can be used for, e.g., driving assistant systems.
# Method of Multi-Label Visual Emotion Recognition Fusing Fore-Background Features
## 3 Approach
### Multi-Label Emotion Recognition Classifier (ML-ERC)
#### 3.2.2 The Design of the Cosine Similarity Matrix  
Continuous convolution in GCNs reduces the similarity of node representations in the original feature space, as illustrated in Figure 6a,b. Figure 6a shows the Isomap [36] dimensionality reduction plot of the initial node semantic features \(\mathbb{R}^{m\times\omega}\), where semantically similar emotions are closely positioned, such as \"Excitement, Happiness, Pleasure\", \"Anger, Annoyance, Aversion\", and \"Suffering, Pain\". Figure 6b shows the Isomap dimensionality reduction plot of the node semantic features \(\mathbb{R}^{m\times\omega}\) after two layers of convolution, where the similarity of node semantic features is disrupted, with semantically similar emotions positioned far apart and dissimilar emotions positioned close together.  
Figure 6: (**a**) Graph of dimension reduction using initial node features with Isomap. (**b**) Graph of dimension reduction using Isomap with node features after two layers of GCN convolution.  
To prevent consecutive convolution operations from disrupting the similarity of node features, a cosine similarity matrix \(C\) is introduced to ensure that the semantic similarity of nodes remains unchanged after each convolution. The construction is organized as follows:  
Step 1: Obtain the initial cosine similarity matrix \(C_{ij}\) according to Equation (16):  
\[C_{ij}=\frac{R_{i}^{\omega}\cdot R_{j}^{\omega}}{\left|\left|R_{i}^{\omega} \right|\right|\left|\left|R_{j}^{\omega}\right|\right|} \tag{16}\]  
where \(\left|\left|\ldots\right|\right|\) represents the \(L_{2}\) norm.  
Step 2: To ensure that similarity values between different emotion categories are on the same scale, eliminate dimensional discrepancies, and reduce the impact of extreme values, normalize \(C_{ij}\) by the Z-score to obtain the cosine similarity matrix \(C\). Figure 7b illustrates the resulting \(C\) using \(\mathbb{R}^{m\times\omega}|_{Glove}\) as an example.
# Integrated Predictor Based on Decomposition Mechanism for PM2.5 Long-Term Prediction
## 3 Experiments
### Evaluation of Prediction Results  
The prediction performance of different models was evaluated by comparing the prediction errors. Four quantitative evaluation indicators such as root mean square error (RMSE), normalized root mean square error (NRMSE), mean absolute error (MAE), and symmetric mean absolute percentage error (SMAPE) were used to evaluate the performance of the models. Furthermore, we utilized the Pearson correlation coefficient (R) to measure the linear relationship between the prediction and actual data. Their calculation formulas are shown in Equations (11)-(16).  
In addition, we visualized the absolute error between the actual data and prediction values, as shown in Equation (16), which reflects the magnitude of the predicted value from the observed value.  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{N}\left(x_{pre}(i)-x_{obs}(i)\right)^{2}}{N}} \tag{11}\]  
\[NRMSE=\frac{1}{\max(x_{pre})-\min(x_{pre})}\sqrt{\frac{\sum_{i=1}^{N}\left(x_{ pre}(i)-x_{obs}(i)\right)^{2}}{N}} \tag{12}\]  
\[MAE=\frac{1}{N}\underset{i=1}{\overset{N}{\sum}}\left|x_{pre}(i)-x_{obs}(i)\right| \tag{13}\]  
\[SMAPE=\frac{1}{N}\underset{i=1}{\overset{N}{\sum}}\frac{\left|x_{obs}(i)-x_{ pre}(i)\right|}{\left(\left|x_{obs}(i)\right|+\left|x_{pre}(i)\right|\right)/2} \tag{14}\]  
\[R=\frac{\sum_{i=1}^{N}\left(x_{obs}(i)-\overline{x}_{obs}(i)\right)\left(x_{ pre}(i)-\overline{x}_{pre}(i)\right)}{\sqrt{\sum_{i=1}^{N}\left(x_{obs}(i)- \overline{x}_{obs}(i)\right)^{2}\sum_{i=1}^{N}\left(x_{pre}(i)-\overline{x}_ {pre}(i)\right)^{2}}} \tag{15}\]  
\[AE=x_{pre}-x_{obs} \tag{16}\]  
where \(N\) is the number of predictive datasets; \(x_{obs}\) represents the PM2.5 data, namely, ground truth value and \(x_{pre}\) is predicted value; \(\max(x_{pre})\) represents the maximum of predictive data; \(\min(x_{pre})\)represents the minimum of predictive data; \(\overline{x}_{obs}\) represents the average of PM2.5; and \(\overline{x}_{pre}\) represents the average of prediction.  
Obviously, RMSE, NRMSE, MAE, and SMAPE will be a non-negative number, and the smaller the values of these errors, the more accurate the predictions. The maximum value of \(R\) is 1, and a higher value of R indicates a better fit between the prediction and original data.
# Detecting Dynamic Communities in Vehicle Movements Using Ant Colony Optimization
## 3 Experiments  
The performance of EACO was evaluated using both simulated and real-world vehicle movement datasets. EACO was also compared with three representative dynamic community detection methods: FacetNet [19], DYNMOGA [20], and RWLA [4]. FacetNet uses modularity as the objective function, and uses the expectation-maximization method to identify communities. EACO and FacetNet both require a coefficient \(\gamma\in(0,1)\) to balance partition quality and temporal partition coherence at each snapshot. DYNMOGA uses modularity to evaluate the quality of communities identified at the current snapshot, and uses normalized mutual information (NMI) [36] to measure the similarity between the community structure identified at the current snapshot and that identified at the previous one. The parameters of DYNMOGA were set according to the original paper: number of generations = 200, population size = 200, crossover rate = 0.8, and mutation rate = 0.2. RWLA first uses a random walk method [37] to calculate the visiting probabilities of network nodes and layers, then merges the graphs at different time snapshots into a single graph according to the visiting probabilities, and finally uses the Leiden algorithm [38] to identify dynamic communities from the merged graph. Two damping factors \(\alpha\) and \(\beta\) were both set to 0.85 according to the suggestion of the original paper. The codes of FacetNet, DYNMOGA, and RWLA were obtained from the authors of the original papers.
## 4 Experiments  
### Datasets  
To evaluate the effectiveness of TAPT, we performed various experiments using two public animal sound datasets, BirdVox-14 SD and ANAFCC [22], with significantly different class distributions. BirdVox-14 SD contains 6600 h of audio derived from 37 classes of avian animals (e.g., birds and insects) and collected from ten autonomous recording units located in Ithaca, New York, USA. Among these classes, we excluded 16 classes that contained audio of unknown animal species or audio of several species, and used the remaining 21 classes for the experiments. ANAFCC contains short audio waveforms of bird flight calls derived from 27 classes. In this case, we excluded 12 classes for the same reason as in BirdVox-14 SD and used the remaining 16 classes.  
The dataset is divided into 2 subsets: the meta-training set \(M_{train}\) (from which \(S_{task}\) is extracted) and the meta-test set \(M_{test}\) (from which \(T_{target}\) is extracted). We first sorted the classes of the datasets according to the number of data samples. Then, to represent the data shortage situation, we used the classes with a small number of samples as the meta-test set and the remaining classes as the \(M_{train}\). As a result, 15 classes and 6 classes for BirdVox-14 SD and 10 classes and 5 classes for ANAFCC were used as \(M_{train}\) and \(M_{test}\), respectively. Table 1 shows the number of classes and samples in \(M_{train}\) and \(M_{test}\) of each dataset; Table 2 presents classes in the datasets and their corresponding animal species; and Figure 2 illustrates the number of data samples from each class in the datasets.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & \multicolumn{2}{c}{\(M_{train}\)} & \multicolumn{2}{c}{\(M_{test}\)} \\ \hline BirdVox-14 SD & \# of classes & \# of samples & \# of classes & \# of samples \\ \cline{2-5}  & 15 & 4644 & 6 & 131 \\ \hline ANAFCC & \# of classes & \# of samples & \# of classes & \# of samples \\ \cline{2-5}  & 10 & 40821 & 5 & 2283 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of classes and samples in meta-training and meta-test for each dataset.
## 3 Results and Discussion
### Design Sensitivity Analysis
The direct differentiation method is based on the differentiation of the equations of motion with respect to the design parameters, thereby producing differential equations for the sensitivity functions [43]. The advantage of direct differentiation is that the dynamic and sensitivity equations are simultaneously integrated in time, resulting in effective control of the time integration errors [45]. The MBD solver in this paper uses the finite forward difference method for the sensitivity analysis as the default setting. The reference modal force is perturbated by step \(\Delta_{j}\) in the direction of measurement \(L_{j}\) so the approximate value of the partial derivative may be calculated:  
\[\frac{\partial f_{i}}{\partial L_{j}}\approx\frac{f_{i}(\mathbf{L}+\mathbf{ \Delta})-f_{i}(\mathbf{L})}{\Delta_{j}}, \tag{29}\]  
where  
\[\mathbf{\Delta}=\big{[}0,\ldots,0,\Delta_{j},0,\ldots,0\big{]}. \tag{30}\]  
This approach is time-consuming, especially if it is serially computed as it is in the case of the MBD solver MSC Adams. Nevertheless, the modal force calculus for each measurement in the subroutine is independent and may be parallelized. For the ability to parallelize the solution to Equation (29), one must know the size of the perturbation step \(\Delta_{j}\). It must be estimated appropriately so that the calculated sensitivity analysis, hence Jacobian, will be as accurate as possible. The optimal perturbation step size depends on individual measurements. Therefore, the perturbation step calculation is demonstrated in the MBD simulation of the piston-liner interaction in the following section.
## 4 Experimental Results  
For experimental measurements, the experimental platform was designed as shown in Figure 7a, consisting of the stator assembly, moving element, rubber ring, scale encoder, holder, and the transmission and circuit components. The prototype with the transmission platform was fabricated as shown in Figure 7b while vibration and mechanical characteristic measurements were conducted to verify the finite element analyses results. Figures 8a and 9a show the experimental system. The applied signal was supplied by a digital function generator (AFG 3022B; Tektronix Inc., Beaverton, OR, USA) and amplified by power amplifiers (HFVA-64; Foneng Technology Co., Ltd., Shenzhen, China).
## 1 Introduction
In addition, it referred to others about state monitoring methods, such as the extended Kalman filter (EKF) applied in the estimation of the position of the intake valve of the engine, and a theoretical basis, which was built up for the algorithm proposed in this paper [18; 19]. Although [PERSON] et al. [17] benefits were well proved, the algorithm was not sensitive to the initial real-time monitoring degradation data in solving the RUL estimation. Based on the Kalman filter, it was the important measure to the real-time condition monitoring of RUL, this paper proposed an inverse Kalman filter. Furthermore, Bayesian updating method and expectation maximization (EM) algorithm were used to estimate the RUL. Finally, the strong tracking filtering (STF) method was used to enhance the robustness. In order to verify the validity of the method, the S826 rail vehicle micro-switches were chosen as the research object. Thus, the real-time prediction of RUL for micro-switches were necessary. The data-driven method to solve the prediction of this kind of system could provide a feasible way for optimization of problems [20]. The remainder of this paper is organized as follows. Section 2 constructs a general stochastic process-based degradation model and then presents a degradation path-dependent approach for adaptive RUL estimation via real-time condition monitoring data. It discusses how to estimate initial parameters by using an inverse Kalman filter and illustrates a Bayes technique improved by the STF method, which can update the system parameters more accurately in real time. In Section 3, the test rig is designed to obtain performance degradation data for micro-switches. Section 4 provides several simulations and a case study to illustrate the application and usefulness of the developed approach. Section 5 concludes the paper. Notations used in this paper.
## 4 Practical Application
### Further Impact  
The practical examples shown above illustrate two application options from the engineering field; however, the use of the RooTri() function is not limited to these examples, as it can also be used in many different ways. A classic field of application for determining isolines can be found in geography. Here, land surfaces are surveyed in order to extract elevation levels from geographical information. Scalar fields are also the subject of research in meteorology. Atmospheric variables such as pressure and temperature distributions or humidity are examined as parameters that can be used to create weather maps. Other areas of application can be found in biology or chemistry when examining substance concentrations, for instance.
# Evaluation of Linearization Methods for Control of the Pendubot
## 3 Control Problem
### Linear Controller  
Since the control approach discussed in Section 3.1 does not guarantee stabilization of the closed-loop system at the chosen equilibrium the overall control strategy based on a hybrid approach (6) will be used. For this purpose, firstly a set of feasible initial conditions for which the controller (20) applied for system (11) brings the robot state near the equilibrium point should be obtained.  
Secondly, recalling that the linear approximation of dynamics (11) established at the equilibrium point provides a controllable linear system [18], it is possible to locally stabilize (11) at this point using the following linear feedback  
\[u_{\text{lin}}=-K(x_{r}-x), \tag{25}\]  
where \(x_{r}=[q_{r1}\ 0\ q_{r2}\ 0]^{\top}\) and \(K=[k_{1}\ k_{2}\ k_{3}\ k_{4}]\) stand for the reference state and the controller gains, respectively. The selection of gains \(K\) should be made appropriately and can refer to pole-placement strategy, LQR approach, and others.
## 4 Conclusions  
Spark ablation technology was applied for producing nanoparticles from platinum ingots as a feed material by using air as a carrier gas. The synthesized nanomaterial, composed of an amorphous platinum oxide PtO (83 wt. %) and crystalline metallic platinum (17 wt. %), was used for formulating functional colloidal ink. The formulated ink was characterized by the following principal parameters: a concentration of platinum of 24.5 wt. %, viscosity of 11.4 mPa-s (25 \({}^{\circ}\)C), surface tension of 43.9 mN/m (25 \({}^{\circ}\)C) and boiling point of the solvent of 119.6 \({}^{\circ}\)C. Annealing of the deposited ink at 750 \({}^{\circ}\)C resulted in the formation of a polycrystalline material with a resistivity of 1.2\(\cdot\)10\({}^{-7}\)\(\Omega\cdot\)m (25 \({}^{\circ}\)C) comprising 99.7 wt. % of platinum. To demonstrate the possibility of the application of formulated ink in printed electronics, we patterned conductive lines and microheaters on alumina substrates and 20 \(\mu\)m thick LTCC membranes with the use of aerosol jet printing technology. The power consumption of the microheaters fabricated on the LTCC membranes was found to be about 140 mW at a temperature of the hot part of 500 \({}^{\circ}\)C, thus allowing one to consider these structures as promising micro-hotplates for MOS gas sensors. The catalytic activity of the synthesized nanoparticles was demonstrated by measuring the thermal responses of the non-sintered microheaters (heat treated at 400 \({}^{\circ}\)C) to hydrogen, which opens the prospective application of spark discharge-produced platinum-based powders in catalysis, in particular for the fabrication of catalytic pellistors.  
Conceptualization, I.A.V. and A.A.V.; methodology, I.A.V., A.A.E. and V.V.I.; investigation, I.A.V., I.S.V., N.P.S., T.L.S., A.S.M., V.I.B., P.V.A., Y.Y.L., A.M.M., A.A.L., V.A.B. and A.E.V.; resources, I.A.V., A.A.V., Z.L., E.P.S. and V.V.I.; data curation, I.A.V., N.P.S., T.L.S. and I.S.V.; writing--original draft preparation, I.A.V., I.S.V. and A.A.V.; writing--review and editing, I.A.V., I.S.V., N.P.S. and T.L.S.; visualization, I.A.V., I.S.V., N.P.S. and T.L.S.; supervision, I.A.V. and A.A.V.; project administration, I.A.V., A.A.E. and V.V.I.; funding acquisition, I.A.V. and V.V.I. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Higher Education of the Russian Federation (state contract no. 075-00337-20-03, project identifier FSMG-2020-0007, project title: Development of functional materials with controlled electrical, chemoresistive and catalytic properties for manufacturing sensor microsystems by using methods of printed electronics).  
Figure 16: Resistance transients of a non-sintered microheater upon exposure to 2500 ppm of hydrogen mixed with air obtained at a voltage of 2 V (blue curve) and 3 V (red curve).
## 4 Discussion  
This study developed a method that can support significant savings in both energy and cost. The LCC and the LCE of the Pareto optimal designs (except the first design) generated at Stage 1  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Lighting Control Mode** & **LCC (CNY \%)** & **LCE (MJ)** \\ \hline MLS & 8,429,845.70 & 4,068,169.843 \\ ALS & 8,364,098.08 & 4,015,142.393 \\ ALD & 8,384,345.69 & 4,028,560.143 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Information about the fifth design with three lighting control modes.  
Figure 3: Pareto frontier of case study.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.** & **Window-to-Wall Ratio** & **Glazing Type** & **Wall Type** & **Artificial Light Type** & **LCC (CNY \%)** & **LCE (MJ)** \\ \hline
1 & 0.900 & 1 & 1 & 2 & 4,158,961.07 & 5,624,928.382 \\
2 & 0.748 & 2 & 2 & 1 & 4,811,119.35 & 4,765,746.660 \\
3 & 0.201 & 3 & 3 & 2 & 6,410,224.77 & 4,285,789.883 \\
4 & 0.139 & 2 & 3 & 2 & 8,332,691.86 & 4,098,387.582 \\
5 & 0.137 & 1 & 3 & 2 & 8,336,950.97 & 4,014,879.593 \\
6 & 0.100 & 2 & 2 & 2 & 10,245,469.86 & 3,956,413.052 \\
7 & 0.085 & 1 & 2 & 2 & 10,716,024.16 & 3,902,559.339 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Information about optimal designs.  
were compared with the LCC and LCE of the initial design; Figure 4 shows the results. This outcome illustrates that most designs performed better than the initial design: The LCC savings ranged from 1.25% to 55.66% and the LCE savings ranged from 10.10% to 26.39%. As a result, this proposed method can help achieve the goal of developing high-quality designs.  
At Stage 2, the best fit lighting control mode was added to the selected optimal design. Table 5 shows that compared with the initial design, the ultimate optimal design can save 22.92% of the LCC and 24.26% of the LCE. The savings of the ultimate optimal design required a little more LCC and LCE than the fifth design. This is because in fifth design assumed that the occupancy can turn the lights on/off in time when the indoor illumination cannot/can be up to the standard of 500 lux. In contrast, for the ultimate optimal design, the lights were controlled using occupancy sensor switches, which have extra energy consumption and costs. The lighting control mode in the fifth design cannot be realistically achieved. As such, a lighting control mode is necessary in the proposed lighting design method.  
In the proposed lighting design method, the computer generated most of the procedures. The computing time required 82 h, faster than with traditional design methods. Further, during the process, designers only needed to build the model into the simulation software, select the design factors, and enter the pre-set values into the program. This method required less professional knowledge of architectural physics, reducing the difficulty of selecting an excellent sustainable building design scheme. These factors demonstrated the efficient, precise, and user-friendly characteristics of this method.