esa-sceva/satcom-chunk-collection huggingface dataset:
# Tree Seedings Detection and Counting Using a Deep Learning Algorithm
## 2 Materials and Methods
### Methods
#### 2.2.3 Evaluation Indicators  
To evaluate the performance of the tree seedlings detection model, we used the following evaluation indicators: precision (P), average precision (AP), recall (R), F1_score, accuracy and the mean average precision (mAP), which are defined in the Equations (1)-(6), respectively.  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{1}\]  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{2}\]  
\[\text{Accuracy}=\frac{TP+TN}{TP+FN+FP+TN} \tag{3}\]  
\[\text{F1\_score}=\frac{2 PR}{P+R} \tag{4}\]  
\[\text{AP}=\int_{0}^{1}P(R)dR \tag{5}\]  
\[\text{mAP}=\frac{1}{N}\sum_{i=1}^{N}AP_{i} \tag{6}\]  
where TP (true positive) refers to the number of correctly identified tree seedlings, FP (false positive) refers to the number of incorrectly identified tree seedlings, and FN (false negative) refers to the number of unidentified tree seedlings. The precision represents the ratio of correctly identified tree seedlings to all identified tree seedlings, which are correctly and incorrectly identified. In contrast, the recall represents the ratio of correctly identified tree seedlings to ground-truth tree seedlings in the dataset.
# Predicting Shot Accuracy in Badminton Using Quiet Eye Metrics and Neural Networks
## 5 Results
### Analysis and Insights from Performance Metrics
#### 5.8.3 F1 Score  
The F1 score, balancing precision and recall, shows steady improvement, indicating that the overall predictive performance of the model improves consistently during training.
## 3 Results and Discussion
### The Release of Phenolic Compounds in the Gastric Digestion  
In the first 10 min of simulated gastric digestion (Tables 1 and 2), individual and total phenolic compounds were released from the peel and flesh of apples in statistically significantly lower amounts than the amounts present in natural apples before the digestion (\(p<0.05\)). Exceptions were the released (+)-catechin, (\(-\))-epicatechin, and total flavan-3-ols from the flesh. [PERSON] et al. (2012) [2] came to a similar conclusion. Amounts of total phenolic compounds released from apples in the gastric digestion were lower than amounts before the digestion. [PERSON] et al. (2020) [27] also reported lower amounts of flavonoids, phenolic acids, flavan-3-ols, and dihydrohalcones after the gastric digestion of apples in comparison to the amounts in natural apples. In the further gastric digestion from 10 to 120 min, amounts of individual and total phenolic compounds were statistically similar to amounts released in the first 10 min (Tables 1 and 2). Since the amount of phenolic compounds in the gastric digestion did not decrease in time, it can be suggested that anthocyanins, flavan-3-ols, dihydrohalcones, phenolic acids, and flavonoids were stable in the gastric digestion. Earlier studies reported similar results. In the study of gastric digestion of apples with the use of static and dynamic simulated gastric digestion, in the static gastric digestion, which does not mimic mixing or peristalsis, amounts of released total and individual polyphenols did not change in time [28], similar to our study. However, there was a visible increase in released total and individual polyphenols in the conditions of dynamic gastric digestion, which controls gastric juice injection and mimics contractions in the stomach [28]. Moreover, procyanidin B2 and quercetin-3-galactoside were degraded [28]. [PERSON] et al. (2011) [29] conducted a study of the stability of apple polyphenols in gastric juice and found that hydrocinnamic acids, flavonoids, dihydrohalcones, and monomeric flavan-3-ols were stable. However, procyanidin B2 was also reported to degrade [29]. Since in this study, we used static gastric digestion, our results were similar to earlier studies [28; 29] showing the stability of phenolic compounds. The amount of procyanidin B2 slightly decreased during the gastric digestion, but not significantly, maybe due to the static model of the digestion.
# New Quality Control Algorithm Based on GNSS Sensing Data for a Bridge Health Monitoring System
###### Abstract  
This research introduces an improvement plan for the reliability of Global Navigation Satellite System (GNSS) positioning solutions. It should be considered the most suitable methodology in terms of the adjustment and positioning of GNSS in order to maximize the utilization of GNSS applications. Though various studies have been conducted with regards to Bridge Health Monitoring System (BHMS) based on GNSS, the outliers which depend on the signal reception environment could not be considered until now. Since these outliers may be connected to GNSS data collected from major bridge members, which can reduce the reliability of a whole monitoring system through the delivery of false information, they should be detected and eliminated in the previous adjustment stage. In this investigation, the Detection, Identification, Adaptation (DIA) technique was applied and implemented through an algorithm. Moreover, it can be directly applied to GNSS data collected from long span cable stayed bridges and most of outliers were efficiently detected and eliminated simultaneously. By these effects, the reliability of GNSS should be enormously improved. Improvement on GNSS positioning accuracy is directly linked to the safety of bridges itself, and at the same time, the reliability of monitoring systems in terms of the system operation can also be increased.  
global navigation satellite system; bridge health monitoring system; detection identification adaption; quality control; deflection monitoring analysis +
Footnote †: journal: Sensors
# Scene Text Detection Based on Multi-Headed Self-Attention Using Shifted Windows
## 2 Related Work
### Text Region Suggestion  
The text detection methods based on text region suggestion adopt the framework of the classical object detection method, which uses CNNs to extract image features and then generates a text Region of Interest (RoI). Then, this method judges whether the region belongs to the text instance and filters out the redundant candidate region to obtain the final detection results.  
To make the object detection algorithm SSD applicable to the text detection task, the algorithm of TextBoxes [13] replaces the fully connected layer in SSD with a convolutional layer. In addition, considering the text aspect ratio, the size of the convolution kernel is adjusted using a 1 \(\times\) 5 convolution kernel instead of a 3 \(\times\) 3 convolution kernel, and the ratio of candidate boxes is adjusted.  
In order to solve the problem that TextBoxes can only detect horizontal text, TextBoxes++ [14] extends TextBoxes to produce rectangular text detection boxes with a rotation angle, which can effectively detect rotated text. In addition, it changes the \(1\times 5\) convolution kernel to \(3\times 5\) and also adjusts the aspect ratio of the candidate box.  
The ContourNet [15] algorithm decouples the texture features of the image into contour detection in the horizontal and vertical directions, and suppresses the False Positives (FP) problem, which is common in text detection algorithms, by its joint response. In addition, to address the problem of inaccurate text detection with drastic scale transformation, a modeling that focuses on the text's own shape information was proposed, and a scale-insensitive metric was used to optimize the detection effect of the network.  
To address the problem of the low detection rate caused by the close alignment and multi-directional position of text words in practical applications and the need to improve the detection speed of the algorithm, the YOLOv3 text detection algorithm was improved in [16]. A new CIOU-based rotating box loss function MD-Closs was proposed to improve the detection accuracy. In addition, a step-by-step NMS approach was used to further reduce the computational effort.  
The MOST [17] algorithm dynamically adjusts the perceptual field of the localization prediction layer by aligning the image features with the coarse detection results. At the same time, it adaptively merges the original predictions to focus on accurate predictions and discards inaccurate predictions based on the position of the predictions at the time of the original prediction.
# Fault Prediction of Mechanical Equipment Based on Hilbert-Full-Vector Spectrum and TCDAN
## 3 Construction of Prediction Model
### Process of Prediction  
The fault-prediction process is shown in Figure 7, which is mainly divided into four steps, and the specific process is shown as follows:  
Step 1: The vibration data of the dual channels of the device is processed based on the Hilbert-full-vector spectrum, and the amplitudes corresponding to different characteristic frequencies of the device are obtained.  
Step 2: The amplitude data corresponding to each characteristic frequency obtained in Step 1 are normalized, and then divided into a training set and a test set according to the lag window size.  
Step 3: Input the training set data processed in Step 2 into the TCDAN model, adjust the different parameters of the model for training and testing, and save the best prediction model according to the evaluation index of the test results.  
Step 4: Make a prediction using the best-saved prediction model and output the prediction results.  
Figure 6: Structure of TCDAN.  
Figure 7: The flowchart of fault prediction.  
In this paper, the mean absolute error (MAE), root mean squared error (root mean squared error, RMSE), and mean absolute percentage error (MAPE) was used to analyze the prediction results of each variable, and the calculation of each evaluation index was as follows:  
\[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}\left|\hat{y}_{i}-y_{i}\right| \tag{14}\]  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(\hat{y}_{i}-y_{i}\right)^{2}} \tag{15}\]  
\[\text{MAPE}=\frac{100\%}{n}\sum_{i=1}^{n}\left|\frac{\hat{y}_{i}-y_{i}}{y_{i}}\right| \tag{16}\]  
where n is the total number of samples, \(y_{i}\) is the actual value and \(\hat{y}_{i}\) is the predicted value. MAE and RMSE represent the stability of the model--the smaller the value, the more stable the model. The MAPE is used to indicate the accuracy of the model; the closer the MAPE to 0, the more accurate the model.
## 2 Theoretical Background
### CS Based on ADMM  
The ADMM algorithm is also a classical convex optimization algorithm that introduces new variables to decompose the original objective function into several smaller subprob  
Figure 1: CS Based on ISTA.  
lems for solution, and then the global optimal solution of the original objective function is obtained through the solution among the coordination sub-problems. ADMM has a broader definition for the solution of the inverse problem in Formula (1):  
\[\min\limits_{x\in\mathbb{R}^{2}}\frac{1}{2}\|Ax-y\|_{2}^{2}+\sum\limits_{i=1}^{C }\lambda_{i}g(\mathbf{D}_{i}x), \tag{5}\]  
where \(g(\cdot)\) is the nonlinear regularization parameter, and \(\mathbf{D}_{i}\) is the transformation matrix. ADMM introduces the dyadic variable \(Z\), which transforms Equation (5) into the following:  
\[\begin{array}{l}\min\limits_{x,\{z\}_{i=1}^{C}=1}\frac{1}{2}\|Ax-y\|_{2}^{2} +\sum\limits_{i=1}^{C}\lambda_{i}g(z_{i}),\\ \text{s.t.}\ z_{i}=\mathbf{D}_{i}x(\forall i).\end{array} \tag{6}\]  
Its corresponding Tseng generalized Lagrangian function is defined as follows:  
\[L_{\rho}(x,z,\varepsilon_{i})=\frac{1}{2}\|Ax-y\|_{2}^{2}+\sum\limits_{i=1}^{C }\lambda_{i}g(z_{i})+\frac{\rho_{i}}{2}\|\mathbf{D}_{i}-z_{i}+\varepsilon_{i}\|_{ 2}^{2}, \tag{7}\]  
where \(\varepsilon_{i}\) is a binary variable, and \(\rho\) is a penalty parameter. ADMM method is obtained by alternately minimizing Equation (7) with bivariate updating, as follows:  
\[\left\{\begin{array}{l}X^{k}:x^{(k)}=\left(A^{T}\mathbf{A}+\sum\limits_{i=1}^{C }\rho_{i}\mathbf{D}_{i}^{T}\mathbf{D}_{i}\right)^{-1}\left[\mathbf{A}^{T}y+\sum\limits_{i=1 }^{C}\rho_{i}\mathbf{D}_{i}^{T}\left(z_{i}^{(k-1)}-\varepsilon_{i}^{(k-1)}\right) \right]\\ Z^{k}:z_{i}^{k}=S\Big{(}\mathbf{D}_{i}x^{(k)}+\varepsilon_{i}^{(k-1)};\lambda_{i}/ \rho_{i}\Big{)}\\ M^{k}:\varepsilon_{i}^{k}=\varepsilon_{i}^{(k-1)}+\eta\left(\mathbf{D}_{i}x^{(k)}- z_{i}^{(k)}\right)\end{array}\right., \tag{8}\]  
where \(n_{i}\) is a constant parameter, and \(S(\cdot)\) is a proximal mapping with parameter \(\lambda\).
# Link Investigation of IEEE 802.15.4 Wireless Sensor Networks in Forests
## 4 Performance of In-Network Links
### Exploring Link Correlation
From Figure 20, we cannot see clear clusters of correlation coefficients for each sending node, and then, we cannot derive the convincing conclusion that the link correlation property is widespread in the low-power wireless networks deployed in forest environments. To some extent, this observation contradicts the packet correlation results achieved by several prior works that deploy sensor nodes either in a building or an open area. Even though not being a common behavior for low-power wireless networks deployed in forest, significant correlation between two incident links still exists and can often hold within a relatively long period, say at least a few minutes (see Figure 18a). In the complex forest environment, upper-level networking protocols could potentially depend on the link correlation information to achieve the resource-efficient estimation of the link quality or the link variation pattern. Figure 17: Conceptual illustration of the link correlation. (**a**) Demo of incident links; (**b**) Patterns of the PRR variations. Figure 18: Demo of the link correlation measured in the Jiufeng experiment. (**a**) Correlation = 0.63; (**b**) Correlation = 0.28; (**c**) Correlation = 0; (**d**) Correlation = \(-\)0.4.
# A Convolutional Neural Network as a Potential Tool for Camouflage Assessment
## 2 Experiment 1: YOLO Detection Performance for Camouflaged Persons  
Using a photosimulation approach, we examined whether YOLO is able to detect a person wearing camouflage clothing in two different (bush/desert and urban) environments and whether the associated confidence levels of the detections can serve as a measure of camouflage effectiveness. Thereto, an image of a person in camouflage clothing was digitally inserted at multiple locations in photographs of both environments, and YOLO determined the probability that a person was present.
## 3 Results and Discussion
### Identification of Microphatics Found in Mussel Digestive Glands Using Scanning-Electron Microscopy  
In this study, MP particles were recorded by using SEM on the mussel digestive gland cryosections. Elemental chemical compositions were analyzed by using an energy dispersive spectrometry (EDS) detector. The presence of common elements used in plastic production in microplastic particles, such as carbon (C), hydrogen (H), oxygen (O), nitrogen (N), and various additional elements depending on the type of plastic, such as fluorine (F), chlorine (Cl), sulfur (S), or phosphorus (P), were detected. These elements are characteristic of different types of plastics, can be detected using methods such as SEM-EDS, were identified as MPs (Figure 4a), and were included in further data analysis. On the contrary, a particle that consisted of a larger portion of oxygen and silicon atoms was identified as a mineral particle (Figure 4b) and was not taken into account, as was found in many studies [32; 51; 93; 94; 95]. Analyzing cryosections of a mussel's digestive gland with SEM-EDS could offer a good way to perform detection. Cryosections can be placed directly on a slide and examined using SEM-EDS, enabling the detection of MPs through the atom compositions present in the sample. An additional advancement in using SEM-EDS as a method for MP detection in bivalve tissue is the capability to differentiate diatoms from  
Figure 4: Identified MP particles on mussel cryosection samples (**a**) and an example of a non-plastic samples (**b**) on mussel cryosections analyzed by EDS.  
MPs in mussels, as previously outlined by [PERSON] et al. (2018). [PERSON] et al. have utilized SEM-EDS analysis and shown the presence of SiO\({}_{2}\) at 0.14% (\(w/w\)), Na\({}_{2}\)O at 24.27% (\(w/w\)), and Al\({}_{2}\)O\({}_{3}\) at 0.27% (\(w/w\)) in MPs obtained in mussels, indicating the components which are mostly found in the plastic industries ([PERSON] et al., 2018). Furthermore, the deep fissures observed on the surfaces of MPs may potentially increase the surface area that could serve as a mechanism for the accumulation of pollutants on the MPs and further inside the marine organism ([PERSON] et al., 2018).
## 4 Result and Discussion
### System Simulation Analysis
#### 4.3.4 The Impact of Fines on Evolutionary Results  
The main difference between the spillover penalty mechanism and the non-spillover penalty mechanism is that \(qF_{1}\), a cost account, is added when the coal mine enterprises fully implement intelligent construction. Therefore, Figure 9a shows an evolutionary simulation based on the influence of \(F_{1}\), a single variable, on the probability of intelligent mine construction behavior of coal mining enterprises. In order to facilitate observation, the initial value of probability \(q\) of strict regulation by local governments and probability \(p\) of fully implementing intelligent construction in coal mining enterprises is 0.05; the initial value of \(F_{1}\) is 2.0-4.0, and the step size is 0.5. As can be seen from the figure, with the increase in the number of fines imposed on enterprises by the government, the growth rate of the probability of enterprises fully implementing the intelligent construction of coal mines continues to increase. This situation shows that, to a certain extent, the government's appropriate increase in fines can urge coal mining enterprises to improve  
Figure 8: Influence of the mental account factor on the evolutionary game. (**a**) Different labor costs of enterprises of fully implementing intelligent construction. (**b**) Different labor costs of strict regulation by local government departments.  
intelligent construction quickly. Similarly, the rate of strict government regulation is plotted on the vertical axis, resulting in an image with a trend comparable to that observed in Figure 9a. This indicates that an increase in fines accelerates the system to a certain extent, ultimately reaching a steady state, while not influencing the final strategies of the two parties. The impact of \(F_{2}\) on the government's strategic approach differs significantly from that of \(F_{1}\). In order to facilitate observation, the following values have been selected for analysis in this paper: \(F_{2}\) = 1.4, \(F_{2}\) = 1.7, \(F_{2}\) = 2.0, \(F_{2}\) = 2.3, \(F_{2}\) = 2.7, \(F_{2}\) = 3.0, \(F_{2}\) = 3.3. As illustrated in Figure 9b, when \(F_{2}\) is less than the critical value of 2.3, the government tends to pursue deregulation; when it is greater than 2.3, the government intensifies strict regulation in line with the amount of fine. This indicates that the value of \(F_{2}\) will influence the government's ultimate evolutionary decision.
## 3 Results
### Analysis of MDS Trajectories
#### 3.7.1 RMSD Analysis  
The root mean square deviation (RMSD) measurement is a technique used to calculate its median variation in the atom's displacement throughout a specific period in comparison to a benchmark's instant. Within an assortment of 1-3 A, every protein-ligand interaction's average variation in RMSD is completely reasonable. A major flexible shift in the amino acid structures has taken place whenever the RMSD number is more than 1-3 A.  
As a result, 100 ns MD simulations were performed to evaluate the helical modification for the target polypeptide in its interaction containing the five different ligand molecules represented by CID-25000608, CID-5366020, CID-91696396, CID-7311, and CID-23725625 (control). The normalized RMSD associated with the ligand molecule CID-25000608 ranged from 1.2 to 3.0. The typical RMSD of the ligand substance CID-5366020 ranged from 2.0 to 3.0. The normalized RMSD for the ligand molecule CID-91696396, which had fewer variations, was around 1.2 and 2.8. The normative RMSD for the ligand molecule CID-7311 ranged from 1.2 to 3.2. The average RMSD for the ligand complex CID-23725625 (control) ranged from 1.2 to 3.1. The previous protein-ligand complex arrangement demonstrated translational integrity, as the material's identity fluctuated somewhat as well as being below the permitted threshold (Figure 2).
## 2 Materials and Methods
### CFD Model
The number of discrete solid angles per octant used in the simulations is minimum: two control angles both for the polar and azimuthal angles. It is worth mentioning that this approach allows significant savings in term of CPU time, without affecting the accuracy of the results [47]. * Fire modeling: time-varying source terms of energy, CO, and CO\({}_{2}\). These source terms are set in the conservation energy equation and species (CO and CO\({}_{2}\)) conservation equation, respectively, according to the proposed fire scenario (see Section 2.2). * Boundary conditions: set velocity for 80 circular grilles of the subway station ventilation system (the \"over track system\") and 15 openings of the \"under platform exhaust (UPE) system\" to reach the imposed airflow rates, according to the ventilation emergency operating mode in the subway station (see Section 2.3): atmospheric pressure for all the other openings in the computational domain (the three exits of the station and four connecting tunnels with the neighboring stations in the metro network). This method led to the appropriate overall balance of the mass airflow rates in the computational domain during the simulations. Concerning the boundary conditions for the turbulence quantities (necessary for circular diffusers and grilles of the UPEsystem), they are imposed by means of two parameters: the turbulence intensity and the turbulence length scale. The imposed value for the turbulence intensity is 6%, which corresponds to an average turbulent intensity, also indicated in the literature for such situations. On the other hand, the turbulence length scale is determined based on the hydraulic diameter of the circular diffusers/rectangular grilles of the UPE system. * Numerical resolution: solver--pressure based (velocity formulation: absolute); pressure/velocity coupling--SIMPLE algorithm; spatial discretization (gradient)--least squares cell based, pressure--second order, momentum, turbulent quantities, species (CO, CO\({}_{2}\)), and energy--second order upwind scheme for convection terms and second order central difference scheme for diffusion terms, DO (radiation)--first order upwind scheme for convection term and second order central difference scheme for diffusion term; system resolution--Gauss-Siedel algebraic multigrid (convergence acceleration); under relaxation factors: pressure 0.3, body forces 0.5, density 0.5, momentum 0.4, turbulent quantities 0.4; turbulent viscosity 0.5; CO 0.5; CO\({}_{2}\) 0.5; energy 0.5; discrete ordinates (radiation) 0.5; transient simulations formulation--first order implicit (maximum iterations/time step: 15, time-step 0.5 s).