esa-sceva/satcom-chunk-collection huggingface dataset:
# Fast Face Tracking-by-Detection Algorithm for Secure Monitoring
## 4 Experimental Results
### Real-Time Evaluation  
We also evaluated frame rates of the six algorithms. The frame rate \(FR\) is defined as, \(FR=N/T\), where \(N\) is the total frames in the sequences, \(T\) is the time of a tracker to pass through the sequences.  
Table 2 reports the frame per second (fps) rate of the six trackers over 14 sequences in the OTB2015 dataset. The average frame rate of KCF was the highest, reaching 92.2 fps. The FFTD algorithm was next, at 59.4 fps. The TLD algorithm obtained 19.5 fps. CCT had the worst real-time performance. The speed of FFTD is more than double that of the classic tracking-by-detection algorithm TLD. Compared with KCF, FFTD had reduced speed, but it still met the requirements of real-time applications.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **OPR** & **SV** & **OCC** & **MB** & **FM** & **IPR** & **OV** & **BC** \\ \hline \multirow{2}{*}{**FFTD**} & 0.8491  & 0.784 & 0.8 & 0.765 & 0.863 & 0.848 & 0.816 & 0.971 \\  & 0.8382  & 0.772 & 0.791 & 0.712 & 0.838 & 0.816 & 0.843 & 0.994 \\ \hline \multirow{2}{*}{**KCF**} & 0.851 & 0.791 & 0.796 & 0.689 & 0.669 & 0.851 & 0.607 & 0.959 \\  & 0.814 & 0.734 & 0.773 & 0.644 & 0.703 & 0.814 & 0.638 & 0.939 \\ \hline \multirow{2}{*}{**CXT**} & 0.864 & 0.806 & 0.751 & 0.847 & 0.817 & 0.864 & 0.699 & 0.931 \\  & 0.814 & 0.65 & 0.719 & 0.577 & 0.592 & 0.749 & 0.559 & 0.911 \\ \hline \multirow{2}{*}{**CCT**} & 0.817 & 0.777 & 0.722 & 0.874 & 0.859 & 0.817 & 0.774 & 0.935 \\  & 0.771 & 0.712 & 0.74 & 0.866 & 0.873 & 0.771 & 0.819 & 0.864 \\ \hline \multirow{2}{*}{**Struck**} & 0.785 & 0.693 & 0.651 & 0.585 & 0.68 & 0.728 & 0.502 & 0.925 \\  & 0.771 & 0.673 & 0.714 & 0.533 & 0.657 & 0.715 & 0.534 & 0.921 \\ \hline \multirow{2}{*}{**TLD**} & 0.764 & 0.687 & 0.638 & 0.794 & 0.727 & 0.766 & 0.427 & 0.323 \\  & 0.748 & 0.687 & 0.749 & 0.763 & 0.741 & 0.759 & 0.549 & 0.736 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The precision and success rate of six algorithms on eight attributes.
## 2 Fault Type and Fault Feature Information of the Protection System
### Fault Feature Information Set of the Protection System
The device self-checking information set \(C_{i}\) of the protection system of an intelligent substation in the \(i\)-th fault event is established as shown in Formulas (4)-(7):  
\[C_{i}=\{C_{MU},C_{P},C_{IT}\} \tag{4}\]  
\[C_{MU}= \{C_{MU1},C_{MU2},\ldots,\ C_{MUa}\} \tag{5}\]  
\[C_{P}= \{C_{P1},C_{P2},\ldots,\ C_{Pb}\} \tag{6}\]  
\[C_{I}= \{C_{II},C_{I2},\ldots,\ C_{Ic}\} \tag{7}\]  
In the above Formula (4), \(C_{i}\) contains the device self-checking information in Table 3, and it is divided into three parts: merging unit self-checking information \(C_{MU}\), protection device self-checking information \(C_{P}\), and intelligent terminal self-checking information \(C_{IT}\), where Formulas (5)-(7) subscripts a, b, and c represent the number of these three types of device in the protection system of an intelligent substation. When the secondary monitoring system receives the alarm information, the element in the corresponding position is set to 1, otherwise, it is set to 0. The sampling value information set \(S_{i}\) of the protection system of an intelligent substation in the \(i\)-th fault event is established as shown in Formula (3). \[\left\{ \begin{aligned} S_{i}=\{M_{1},M_{2}\}\\ M_{1}=\{I_{A1},I_{B1},I_{C1},U_{A1},U_{B1},U_{C1}\}\\ M_{2}=\{I_{A2},I_{B2},I_{C2},U_{A2},U_{B2},U_{C2}\}\end{aligned} \right. \tag{8}\]  
In the above Formula (8), \(M_{1}\) and \(M_{2}\) represent the three-phase voltage and current sampling values in Channel 1 and Channel 2, respectively. \(I\) and \(U\) represent the three-phase current and voltage values of the dual channel. To make the sample data of the different units comparable, improve the convergence speed of the model, and improve the accuracy of the model, the sampling value information is preprocessed by the Min-Max method, and the original value \(S_{i}\) in the dataset is mapped to the value \(S_{i}^{{}^{\prime}}\) in the interval [0,1]. The conversion formula is shown in Formula (9):  
\[S_{i}^{{}^{\prime}}=\frac{S-S_{min}}{S_{max}-S_{min}} \tag{9}\]  
In the Formula (9), \(S_{max}\) and \(S_{min}\) are the maximum and minimum values of the sampled values, respectively.
## 2 Material and Methods
### In Vitro Antioxidant Activity- Reducing Power  
The capacity of extracts to reduce Fe3+ was assessed by the ferric reducing antioxidant power (FRAP) method with minor modifications [25]. In separate test tubes, 1 mL of aliquots of various concentrations of ascorbic acid or 1 mL of _P. emblica_ or _A. indica_ extract (100 to 600 \(\mu\)g/mL) was added with 2.5 mL of phosphate buffer (0.1 M, pH 6.6) and 1% of 2.5 mL potassium ferricanide. Later, the solutions were heated for 20 min at 50 \({}^{\circ}\)C. This step was necessary to reduce ferricanide into ferrocyanide. This step was followed by the addition of 2.5 mL of 10% trichloroacetic acid to each test solution to stop the reaction. All the reaction mixtures were centrifuged at 3000 rpm for 10 min. Finally, supernatant from each tube was isolated, and the rest was discarded. The next step was followed by using 2.5 mL of supernatant and mixed with an equal amount of distilled water, and the resultant mixture was vortexed accordingly. Ferric chloride (0.5 mL, 0.5%) solution that was freshly prepared was added to the mixture. The absorbance of ascorbic acid (reference) and test samples was recorded at 700 nm. All the samples were run in triplicate. The blank solution had phosphate buffer (pH 6.6) only. Higher absorbance meant higher reducing power.  
\[\text{Percent reducing power}=[(\text{Ac}-\text{As})/\text{Ac}]\times 100\]  
where Ac represents the control solution absorbance; and As represents the absorbance in presence of extract.  
Evaluation of Antioxidant Activity of Extracts by Hydrogen Peroxide (\(H_{2}\)O\({}_{2}\)) Radical Scavenging Activity  
The ability of _P. emblica_ and _A. indica_ extract to reduce H2O2 s was explored to confirm the antioxidant activity [26]. For this purpose, 40 mM H2O2 prepared in 50 mM PBS (pH 7.4) and 1.0 mL of this solution was mixed to different tubes containing various concentrations of ethanol _P. emblica_ and _A. indica_ extract (50-600 \(\mu\)g/mL) that were kept in the absence of light. The solutions were well mixed, and the absorbance of H2O2 was checked after 10 min at 230 nm against phosphate buffer devoid of H2O2 as the blank solution. The samples were run in triplicates, and the scavenged H2O2 percentage was calculated by the following equation.  
\[\text{H}_{2}\text{O}_{2}\text{ scavenging ability}\text{ (\%)}=[(\text{Mc}-\text{Ms})/\text{Mc}]\times 100\]  
where Mc represents the absorbance of H2O2 solution; and Ms means the extract and H2O2 solution absorbance.
## 4 Geometric Parameter Optimization Design for the Fixed Frog Area
### Optimization Design Method for the Nose Rail Height
\begin{table}
\begin{tabular}{c c} \hline \hline
**Section Position** & **Optimized Value/mm** \\ \hline B & 3.22 \\ C & 1.98 \\ D & 1.65 \\ E & 1.49 \\ F & 1.23 \\ G & 0.72 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Optimization results of the nose rail height value of each section. Figure 13: Plan and side view of the nose rail. Figure 15 shows the change curve of wheel vertical displacement of the vehicle passing through the fixed frog before and after optimization. Before the nose rail optimization, the maximum vertical displacement of wheelset is 0.77 mm. After optimization, the maximum vertical displacement of the wheelset is 0.62 mm, which is reduced by 19.5%. Figures 16 and 17 show the wheel/rail force curve of the vehicle passing through the fixed frog area in main directions. On side of the stock rail side, after the nose rail optimization, the maximum value of wheel/rail lateral force is reduced from 7.8 kN to 6.6 kN, with a decrease range of 15.4%, and the maximum value of wheel/rail vertical force decreases from 83.4 kN to 80.5 kN, with a decrease of about 3.5%. On the side of the frog, after wing rail optimization, the maximum value of wheel/rail lateral force is reduced from 18.4 kN to 15 kN, with a decrease range of 18.5%, and the maximum value of wheel/rail vertical force is reduced from 106 kN to 96 kN, with a decrease range of 9.4%. To sum up, the optimized height values of nose rail can also reduce the impact force between the wheel and rail. In addition, according to the change curve of wheel/rail force, it can be calculated that the derailment coefficient and wheel load reduction rate are within the safe range, that is, the optimization algorithm does not affect the safety of the vehicle. The effectiveness of the optimization method is verified by optimizing the wing rail lifting value and nose rail height of the fixed frog area. By analyzing the wheel/rail  
Figure 14: Evolution curve of the objective function value. Figure 15: Wheelset vertical displacement. dynamic response in a fixed frog area before and after optimization, it is found that the optimized fixed frog can significantly improve the wheel/rail relationship in the fixed frog area, reduce the vertical impact force and lateral force of the wheel/rail, improve the running stability and driving safety of the vehicle, and thus extend the service life of the fixed frog. At the same time, by comparing and analyzing the optimization results of the wing rail lifting value and nose rail height, it is found that the optimization of wing rail lifting value improves the wheel/rail interaction performance in the frog area more. Figure 16: On the stock rail side: (**a**) Wheel/rail lateral force; (**b**) Wheel/rail vertical force.
## 3 Workspace Analysis
### Evaluation and Selection  
Better chromosomes are chosen from the population in order to reproduce the offspring during the process of the IGA. To evaluate a chromosome, a fitness function, which is a quantitative measure, is adopted to measure how satisfactory a chromosome is. In this study, the task-space is only within the position. As a result, the fitness function is defined as  
\[Error_{p}=\|p_{1}-x_{t}\|+\|p_{2}-x_{i}\|, \tag{7}\]  
\[Error_{d}=\underset{k=1,\ldots,8}{max}\theta_{k,j}-\theta_{k,f}, \tag{8}\]  
\[Fitness=Error_{p}+K\cdot Error_{d}, \tag{9}\]  
where \(p_{1}\) is the target point for the thumb, \(x_{t}\) is the tip of the thumb, \(p_{2}\) is the target point for the index finger, \(x_{t}\) is the tip of the index finger, \(\theta_{k,j}\) is the initial angle of each joint before computing the IGA, \(\theta_{k,f}\) is the angle of the joint contained in the chromosome, and \(K\) is the weight of the second criterion (infinity-norm term).  
The position of \(x_{t}\) is calculated using the forward kinematics according to the angle set contained in the chromosome. Equation (7) computes the distance in Cartesian space to indicate how far the tips of fingers are from the target points and, thus, sums the distances to be a criterion for the fitness function. Equation (8) takes the maximum absolute value within the angle differences between the initial angle and the angle generated by the IGA. Equation (8) is derived from the concept of the infinity norm and is used to guarantee the smoothness of the motion. The fitness function is minimized by the IGA, which means that the tips of two fingers are set to reach the target points as closely as possible, with each joint having about the same angular difference.  
On the other hand, to maintain the quality and diversity of the population, the selection operator is performed to select superior chromosomes for breeding of the next population. Supposing that the chosen chromosomes are always the best individuals, the diversity of the IGA will be narrow. Hence, the k-tournament selection operator is used to choose which chromosomes should reproduce new offspring. The k-tournament selection randomly withdraws k chromosomes from the population and picks the top two chromosomes for the crossover operator. The advantage of the k-tournament selection is that it is unnecessary to sort the entire population to get the top two chromosomes, thereby reducing computational resources. In addition, the top two chromosomes may be poor ones, which keeps the population diverse and eliminates a local minimum.
# A Review on the Prospects of Mobile Manipulators for Smart Maintenance of Railway Track
## 6 Benefits of Using a Mobile Manipulator for Railway Track Maintenance
### Financial impact  
The financial benefits of robots and automation have already been demonstrated for other industries. A cable-driven construction robot can save more than EUR 1.2 million in 5 years after considering the cost-benefit analysis with the labor [18]. Furthermore, after considering 10 robots working in 12 different construction sites, researchers concluded that robots save 13% on cost while increasing quality by almost 50% [19]. A whole life-cycle cost model showed that an autonomous robotic tunnel inspection system can save 81% of labor costs and reduce 8700 h of inspection time annually [158]. Another study on tunnel inspection concluded that inspection based on photogrammetry could reduce the cost and data acquisition time to one-fifth, which will directly impact safety costs and conditions [159]. These examples show that railway infrastructure owners will benefit from using mobile manipulators for railway track inspection and repair tasks which will reduce human involvement. With adequately equipped inspection sensors, mobile manipulators can inspect the railway track autonomously. Moreover, they can perform some repair tasks autonomously, assist humans in repair tasks, and ensure information flow in different steps of digital asset management system.
# Mg,Sr-Cosubstituted Hydroxapatite with Improved Structural Properties
## 2 Materials and Methods  
### Synthetic MgSr-HA Macro-granules  
Synthetic, 400-600 \(\upmu\)m MgSr-HA macro-granules were used for thermal stability tests. The material was prepared through a previously described wet granulation process [28]. Briefly, MgCl\({}_{2}\)-6H\({}_{2}\)O and Sr(NO\({}_{3}\))\({}_{2}\) solutions were added to a water suspension containing 1.28 moles of Ca(OH)\({}_{2}\) at 40 \({}^{\circ}\)C. The initial Sr/Ca and Mg/Ca molar ratios were set at 0.20 and 0.15-0.25, respectively. A solution containing 0.77 moles of H\({}_{3}\)PO\({}_{4}\) was dropped into said Ca(OH)\({}_{2}\) suspension, while maintaining heating and stirring. No further chemical (e.g., ammonia) was needed to maintain the high pH conditions required for the precipitation of apatite because the synthesis process is \"self-controlled at high pH.\" The synthesis has a high yield, is reproducible, and allows an easy scale-up. After 24 h of rest, the precipitate was washed and centrifuged, then freeze-dried and sieved at 150 \(\upmu\)m. Porous macro-granules of 400-600 \(\upmu\)m with the peculiar chemical-physical, compositional, and superficial properties of the starting MgSr-HA powder were thus obtained by wet granulation. These materials will be referred to as \"as-prepared MgSr-HA\" (apMgSr-HA).
## 4 Fatigue Analysis and Life Modeling
### Fatigue Aging Damage Model  
The internal rotor's temperature constantly changes during the operation of the motor, and when the temperature rises, the fatigue strength of the rotor decreases. The temperature load histories obtained from the simulation of the magnetic bridge out in the thermal field are distributed jointly with the stress load histories obtained under structural field coupling, and the time duration at each temperature level and stress level is counted. According to the S-N curve of the material at different temperatures, the damage analysis of the rotor is carried out.  
The number of cycles \(n_{ij}\) with temperature \(T\) at level i and stress \(\sigma_{j}\) at level j is obtained after counting through the joint distribution. The S-N curve for the rotor at temperature \(T_{i}\) can be seen in Figure 23 and the fatigue life \(N_{ij}\) at this temperature and stress level can be calculated. According to the Miner linear cumulative damage criterion [22], the damage \(D_{s}\) incurred by the motor rotor is calculated as follows:  
\[\mathrm{N_{ij}}=10^{\frac{|C|_{T_{i}}-\sigma_{j}}{|k_{T_{i}}|}} \tag{9}\]  
\[D_{s}=\sum_{i=1}^{n}\sum_{j=1}^{n}\frac{n_{ij}}{N_{ij}} \tag{10}\]  
where \(|C|_{T_{i}}\) is the rotor material intercept at a temperature of \(T_{i}\) and \(|k|_{T_{i}}\) is the rotor material slope at a temperature of \(T_{i}\).
# Design of an SVM Classifier Assisted Intelligent Receiver for Reliable Optical Camera Communication
## 4 Data Set Preparation  
The prime objective of our proposal is to enable the camera to intelligently determine data transmitting LED and communicate with this LED. Hence, the camera needs a trained AI-based classifier by which it can perform the required task. To train the classifier, a data set is needed which contains some necessary features from the bounding boxes of detected LED regions. In order to build the data set, 1000 preprocessed bounding box images of the LED region are used. The experimental parameters that are used to extract the features are listed in Table 2. Input feature vectors are constructed considering different situations such as varying distance from LED to the camera and moving LED from left to right. However, before performing feature extraction, a closing operation is applied on the selected regions to combine all the neighboring stripes, and the contour is drawn on the shape by combined stripes. It is a morphological filtering technique that is composed of gray level dilation followed by gray level erosion taking the input of the selected regions and a structuring element. The closing operation can be expressed as [32]  
\[f_{p\times q}^{n}\cdot b_{m\times n}=(f_{p\times q}^{n}\oplus b_{m\times n}) \cdot b_{m\times n}, \tag{9}\]  
where \(f_{p\times q}^{n}\) is _thth_ region, and \(b_{m\times n}\) is the structuring element of which \(m,n\ll a,b\).  
In Figure 6, we have presented a single frame where the shape of combined stripes contour is shown. For each projected LED, either data transmitter or unwanted LED, we found this type of shape of the contour formed based on the combined stripe. Analyzing the geometrical pattern of the contour, there is considerable change in the pattern when the communication distance is varied or mark and space frequency have been changed. Hence, based on the contour pattern of the data transmitting LED region and other unwanted LED regions, we have selected several features that are elaborately described in the next section.
# Adaptation to CT Reconstruction Kernels by Enforcing Cross-Domain Feature Maps Consistency
## 3 Data
### Segmentation Data
#### 3.1.1 COVID-Train  
Mosmed-1110  
This dataset consists of 1110 chest CT scans collected in Moscow clinics during the first months of 2020 [34]. Scanning was performed on _Canon (Toshiba) Aquilion 64_ units using the standard scanner's protocol: inter-slice distance of 0.8 mm and smooth reconstruction kernels in particular. However, the public version of Mosmed-1110 contains every 10 th slice of the original series, which makes the resulting slice distance equal to 8.0 mm. Additionally, the 50 series have annotated binary masks depicting COVID-19 lesions (ground-glass opacity and consolidation). We use only these 50 images in our experiments.  
MIDRC-RICORD-1a is the publicly available dataset that contains 120 chest CT studies [9]. The total number of volumetric series is 154. According to the DICOM entries, all images have smooth reconstruction kernels. The dataset contains at least 12 paired images (without considering the studies that contain more than two series). However, we do not use these pairs to enforce consistency since both images have smooth kernels. Also, we use only the images that have non-empty annotations. The images that have empty binary masks of COVID-19 are discarded from both of the datasets. The resulting training dataset consists of 112 volumetric images with smooth kernels.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Source** & \begin{tabular}{c} **Effective** \\ **Size** \\ \end{tabular} & **Kernels** & **Annotations** & **Split** \\ \hline \multirow{4}{*}{COVID-train} & \begin{tabular}{c} Mosmed-1110 \\ [34] \\ \end{tabular} & 50 & \begin{tabular}{c} unknown \\ smooth \\ \end{tabular} & \begin{tabular}{c} COVID-19 \\ mask \\ \end{tabular} & 5-fold \\ \cline{2-6}  & MIDRC [9] & 112 & \begin{tabular}{c} B/L/BONE/ \\ STANDARD \\ (smooth) \\ \end{tabular} & \begin{tabular}{c} COVID-19 \\ mask \\ \end{tabular} \\ \hline \multirow{2}{*}{COVID-test} & Medseg-9 & 9 & \begin{tabular}{c} unknown \\ sharp \\ \end{tabular} & \
\begin{tabular}{c} COVID-19 \\ mask, lungs \\ mask \\ \end{tabular} &
\begin{tabular}{c} hold-out \\ test \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the segmentation datasets. _Effective size_ means the number of annotated images after appropriate filtering.
# Temporal Instability Analysis of Injury Severities for Angle and Non-Angle Crashes at Roundabouts
## 6 Results and Discussion
### Analysis of the Characteristics' Effects
#### 6.1.6 Crash Characteristics  
Among the crash-related factors, several variables were found to significantly affect the injury severity in roundabout crashes. As shown in Table 11, the lane departure indicator was less likely to result in severe injury from angle crashes during 2012-2013, with probabilities of 0.0203, \(-\)0.0143, and \(-\)0.0060 for no injury, minor injury, and severe injury, respectively. However, the same indicator did not have a significant impact on non-angle collisions. Unlike driving in the wrong lane, lane departure can be observed intuitively without knowing the driver's true driving intentions. Lane departure makes surrounding drivers alert, thereby reducing the crash injury severity. Previous research [24] has also revealed that only specific lane departures, such as left-side deviations, are associated with severe injuries.  
In addition, speeding had a persistent negative effect on injuries in angle and non-angle collisions. According to the results in Table 11, speeding was more and more likely to cause severe injuries in angle collisions over time. Similarly, the same indicator was consistently associated with a higher probability of severe injury in non-angle collisions. The findings were supported by [PERSON] et al. [60], who argued that speeding is associated with more serious crash injuries. Therefore, it is necessary for traffic managers to take measures such as speed limits in roundabouts to prevent severe crashes caused by speeding.  
Turning to aggressive driving, the indicator increased injury severity in non-angle collisions (\(-\)0.0167, 0.0140, and 0.0027 for no injury, minor injury, and severe injury, respectively), which is consistent with previous studies [61]. In addition, it is worth noting that aggressive driving did not significantly impact the injury severity outcomes of angle crashes.