esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
To prevent disasters and to control and supervise crowds, automated video surveillance has become indispensable. In today's complex and crowded environments, manual surveillance and monitoring systems are inefficient, labor intensive, and unwieldy. Automated video surveillance systems offer promising solutions, but challenges remain. One of the major challenges is the extraction of true foregrounds of pixels representing humans only. Furthermore, to accurately understand and interpret crowd behavior, human crowd behavior (HCB) systems require robust feature extraction methods, along with powerful and reliable decision-making classifiers. In this paper, we describe our approach to these issues by presenting a novel Particles Force Model for multi-person tracking, a vigorous fusion of global and local descriptors, along with a robust improved entropy classifier for detecting and interpreting crowd behavior. In the proposed model, necessary preprocessing steps are followed by the application of a first distance algorithm for the removal of background clutter; true-foreground elements are then extracted via a Particles Force Model. The detected human forms are then counted by labeling and performing cluster estimation, using a K-nearest neighbors search algorithm. After that, the location of all the human silhouettes is fixed and, using the Jaccard similarity index and normalized cross-correlation as a cost function, multi-person tracking is performed. For HCB detection, we introduced human crowd contour extraction as a global feature and a particles gradient motion (PGD) descriptor, along with geometrical and speeded up robust features (SURF) for local features. After features were extracted, we applied bat optimization for optimal features, which also works as a pre-classifier. Finally, we introduced a robust improved entropy classifier for decision making and automated crowd behavior detection in smart surveillance systems. We evaluated the performance of our proposed system on a publicly available benchmark PETS2009 and UMN dataset. Experimental results show that our system performed better compared to existing well-known state-of-the-art methods by achieving higher accuracy rates. The proposed system can be deployed to great benefit in numerous public places, such as airports, shopping malls, city centers, and train stations to control, supervise, and protect crowds.  
bat optimization; human crowd behavior (HCB); improved entropy (IE); Jaccard similarity; multi-person counting; particles gradient motion (PGM); speeded up robust features (SURF) +
Footnote †: journal: Pattern Recognition Letters
## 3 Results and Analysis on a Meta-Fiber Platform
### Analysis of the Influence of the Fabrication Errors Using FDTD  
To further show the experimental feasibility of the proposed design, the influence of fabrication errors on optical performance influenced is studied. Based on the fabrication experience, the variation of the diameter and the sidewall angle exert the greatest influence. These deviations are usually introduced during the electron beam lithography, the lift-off, and especially the etching process. Typically, the largest sidewall angle deviation (\(\alpha\)) is less than 3\({}^{\circ}\) (Figure 6a) and the diameter variation (\(\delta D\)) is less than 20 nm (Figure 6e).  
Figure 5: Far-field results of the metafiber-based collimating splitter using FDTD. (**a,b**) Two-dimensional and one-dimensional (when \(u_{y}=0\)) far-field intensity of the collimated Gaussian beam array using designed metasurface under TE polarized incidence. (**c,d**) Two-dimensional and one-dimensional (when \(u_{y}=0\)) far-field intensity of the collimated Gaussian beam array using designed metasurface under TM polarized incidence. (**e,f**) Efficiency extracted from different diffraction orders under TE and TM polarized incidence, respectively. The dashed line is at 0.077 to guide the eye.  
The influence of the fabrication error on the divergence, uniformity and the total diffraction efficiency of the device was investigated based on the typical error values above using the FDTD method. The existence of the sidewall angle or the variation of the diameter brings disturbance to the optimized phase profile; thus, the incident beam cannot be perfectly collimated by the beam splitter and the divergence angle increases, as shown in Figure 6b,f. The interference between the inefficient deflected beam and the diffracted beam at the (0, 0) order leads to an interference pattern at the (0, 0) order. This directly deteriorates the uniformity and the efficiency of the output, as shown in Figure 6c,d,g,h. Apart from the phase change and the interference, the lower efficiency can also be attributed to an about 5-10% increase in the reflection. Compared to the sidewall angle formed during the fabrication process, the diameter variation brings much less influence on the final optical performance. During the fabrication, the etching process should be carefully optimized to minimize the sidewall angle for better optical performance.
## 3 System Implementation Based on LPA Synchronization Algorithm
### Multi-h CPM Receiver Implementation
#### 3.2.4 Timing Error Detector  
TED is implemented by the SLPA method of Equation (33) with the sign of the estimated \(\hat{b}_{n}\). We use the VA for TED with traceback length \(D=1\) to calculate the timing error signal, of which the inputs are the imaginary part of MF results. Here, the surviving path index vector \(\mathbf{M}_{n}\) and the global winning state index \(G_{n}\) are reused from the VA of sequence detection. The VA for TED is implemented by two multiplexer banks. Each multiplexer bank is composed of 32 4-to-1 multiplexers. The surviving timing error signal for a phase state is selected through a multiplexer according to the 16-state trellis. The first multiplexer bank calculates the surviving timing error signals using the surviving path index vector \(\mathbf{M}_{n-1}\) at the \((n-1)\)th symbol interval. These values are transmitted to the second multiplexer bank, and the surviving timing error signals are selected according to the \(\mathbf{M}_{n}\). Finally, the estimated timing error signal \(e_{\tau}(n-1)\) is selected by the global winning state index \(G_{n}\) at \(n\)th symbol interval due to \(D=1\). Compared with the LPA-TED of Equation (32), it reduces \(4\times 16\) real multipliers and reserves the advantage of not using early- and late-MF banks.
## 4 The Influence of Error Sources on the Surface Shape Measurement Accuracy
### The Influence of the Scanning Error on the Surface Shape Measurement Error  
In the previous section, the far-field data caused by the scanning error were obtained, and the phase distribution was calculated by the inverse Fourier transform of the far-field data. The phase distribution is substituted into the Equation (3) to solve the surface measurement error distribution caused by the feed offset error. As shown in Figure 12, the RMS value is 0.108 mm. Although the error interference caused by the scanning error on the measurement results is not as regular as the feed offset error, its influence on the measurement accuracy of the antenna surface may be relatively small in many cases. However, in the face of the increasing demand for the accuracy of large aperture reflector antennas, even small scanning errors cannot be ignored.  
In the following, the relationship between the random scanning error and the antenna gain loss is established by using the probability statistics method and adding the random scanning error to the variables u and v of Equation (8).  
As shown in Figure 13, there are significant relationships between scanning error and surface shape measurement accuracy under different antenna diameter conditions. When  
Figure 12: The surface shape measurement error distribution caused by the scanning error.  
\begin{table}
\begin{tabular}{c c c} \hline _fld_ & \(p_{a}\) & \(p_{l}\) \\ \hline
0.3 & 0.016 & 0.017 \\
0.4 & 0.010 & 0.014 \\
0.5 & 0.007 & 0.011 \\
0.6 & 0.005 & 0.010 \\
0.7 & 0.003 & 0.008 \\ \hline \end{tabular}
\end{table}
Table 3: Feed offset error offset coefficients table.  
the standard deviation of the scanning error is 15 arcsec, the RMS of the surface shape measurement error is approximately 0.25 mm for antennas with a diameter of 90 m and 0.37 mm for antennas with a diameter of 110 m. It is worth noting that the holographic measurement accuracy of the GBT 100 m antenna can reach 220 \(\upmu\)m [4], indicating that the RMS of the surface shape measurement error due to scanning error already has a non-negligible impact on measurement accuracy. The larger-diameter antennas are more significantly affected when facing scanning errors, resulting in larger RMS errors. Compensation for scanning errors is more important in application scenarios that require high-precision surface shape measurements.  
By fitting the relationship curve in Figure 13, the relationship equation between the scanning error and the face shape measurement error RMS is obtained as follows:  
\[\varepsilon_{rms}=s_{rms}\sigma \tag{16}\]  
where \(s_{rms}\) is the scanning error RMS coefficient, the value of which is shown in Table 4.  
From Equation (16), it can be seen that the measurement error RMS is proportional to the standard deviation of the scanning error, and the measurement error RMS caused by the scanning error can be quickly calculated by this equation.
## Channel Model
### Channel Attenuation  
The underwater medium reduces the strength of the desired signal when it travels away from the source. This reduction in strength, also called path loss, is modeled by attenuation. The attenuation \(A(d,f)\) of a signal of frequency \(f\) at a distance \(d\) from the source is modeled by the following [27]:  
\[A(d,f)=A_{0}d^{k}\alpha(f)^{k} \tag{6}\]  
where \(A_{0}\), \(k\) and \(\alpha\) are the unity normalization constant, spreading factor and the absorption coefficient, respectively. As a result of variations in absorption, attenuation is often conveniently expressed in decibels as  
\[10 logA(d,f)/A_{0}=k\cdot 10 logd+d\cdot 10 log\alpha(f) \tag{7}\]  
For significantly higher frequencies in kilohertz, [PERSON] empirically models the absorption coefficient in decibels per kilometer as  
\[10 log\alpha(f)=0.11\frac{f^{2}}{1+f^{2}}+44\frac{f^{2}}{4100+f^{2}}+2.75\cdot 1 0^{-4}f^{2}+0.003 \tag{8}\]  
and for smaller frequencies as  
\[10 log\alpha(f)=0.002+0.11\frac{f^{2}}{1+f^{2}}+0.011f^{2} \tag{9}\]
## 5 Results and Discussion
### Performance Measures  
We used the performance measure of sensitivity, specificity, positive predictivity, and accuracy as shown in Figure 7 and described in [45]. Sensitivity is the rate of correctly classified classes among all classes. Specificity is the rate of correctly classified nonevents among all events. Positive predictivity is the fraction of real events in all detected events. Finally, the accuracy is the percentage of correctly predicted classes.
## 3 Detection of Air Gap Eccentricity Faults in PMa-SynRM from the Stator Currents Spectrum  
The research dealing with a three-phase SynRM found in [18] and [28] shows that static eccentricity faults induce triplen harmonic frequencies in the stator currents, although it is known that supply unbalance may also induce such harmonic components, thus hindering the interpretation. These works also conclude that dynamic eccentricity does not generate new harmonic components in the stator currents, although it modifies the odd harmonics. Static eccentricity induces harmonic components in the stator current harmonics which can be localized as [9; 10; 29; 30; 31]:  
\[f_{Eccentricity}=f_{s}\cdot\left(1\pm\frac{k}{p}\right)k=1,\;2,\;3,... \tag{3}\]  
where \(p\) is the number of pole pairs of the motor and \(f_{s}\) is the supply frequency. Therefore, from (3), it is deduced that eccentricity faults can induce particular fractional harmonic frequencies in the stator currents spectra that can be used for diagnosis purposes. The set of harmonics predicted by (3) is present under both stationary and nonstationary operating conditions.  
The line currents are already measured by the current probes included in the drive supplying the PMa-SynRM. Therefore, no additional sensors are necessary. Since, as explained, the amplitude of the stator currents harmonics may be altered by the current loops of the electronic drive, it can hinder the diagnosis from the study of the stator currents. Therefore, the ZSVC-based method is also applied in this work.  
Figure 3: One pole of a five-phase fPMa-SynRM positioned in the direct (\(d\)) and quadrature (\(q\)) axes and the _mmf_ distribution. (**a**) \(d\)-axis position. (**b**) \(q\)-axis position.
## 3 Material Test and Results for LLFD  
### LLFD Material Test Plan  
In a friction damper, a friction force is generated in the sliding and opposite directions by the relative displacement between two materials in contact. This friction force releases the plastic deformation of the building through vibration in the form of thermal energy. Full-size specimens were prepared to verify the seismic performance of the developed LLFD, with a material test conducted using cyclic lateral loads based on the displacement required for the target performance. Meanwhile, the design strength of an LLFD for friction joints was calculated using Equations (1) and (2), i.e., the design formulas for the slip limit state in the steel structure design standards of KDS 41 [23]. Figure 6 shows the details of the LLFD used in the material test, while Table 3 lists the design strengths of friction joints calculated using the theoretical formulas.  
As shown in Figure 6 and Table 3, two types of friction dampers, which employed widely used friction pads and galvanized plates, were used in the LLFD material test. The test was conducted by preparing two specimens for each friction damper. A friction pad (FP) was applied to the FP-LLFD specimen, which had a width of 600 mm, a height of 170 mm, and a thickness of 10 mm. The bolts used were D16-SEA, and the designed friction coefficient (\(\mu\) = 0.2) was based on the research result [35]. The galvanized steel plate (GS)-LLFD specimen, with its geometry and number of bolts the same as those of the FP-LLFD, was produced by applying zinc plating to SS275. The designed friction coefficient (\(\mu\)) had a value of 0.18.  
Figure 5: V-bracing seismic control system equipped with a novel laterally-layered friction damper: (**a**) V-Bracing System; (**b**) Details of LLFD.
## 4 Possible Ways to Improve WUE Remote Sensing
### Remote Sensing Methods Coupled with an Analytical Model  
An analytical WUE model has provide a conceptualization of ecosystem WUE, under which data fusion from multiple platforms and sensors of structural and physiological parameters of vegetation as inputs can improve the WUE remote sensing. The evaporation from canopy interception and T/ET are two important terms in the analytical WUE model, and are also hot topics for research. Accurate remote sensing estimation for these two terms is not only an independent issue, but is also in favor of WUE estimating to upscale to large scales. With the integrated use of remote sensing data, the numerical simulation model, meteorological reanalysis data and ground observation data can also be used to produce high-level remote sensing products. Further analysis on the underlying mechanism of WUE at different time scales is necessary to build a more stable mechanistic model providing the basis of physical processes and biochemical mechanisms for a remote sensing inversion algorithm. On the contrary, the control of vegetation dynamics on the seasonal variability of WUE coupled with the stress of environmental factors (i.e., atmospheric conditions, soil moisture) is another approach to achieve WUE remote sensing inversion at large spatial scales and continuous time scales. Along with the development of high-precision remote sensing algorithms and products, if possible, developing new methods of remote sensing analysis for finely portraying the types, patterns, functions and processes of ecosystems to build the remote sensing-driven ecosystem assessment model is also a direction to follow and a challenge for the future.
###### Abstract
The mechanical testing-crack opening displacement test was used to determine the flexural performance of the specimens, and the findings were compared to code specifications. Further studies are concerned the cracking behavior of the fiber-reinforced concrete element with synthetic fibers [25], and steel fiber-reinforced beams under cyclic deformation [26]. In general, further researchers have been concerned the behaviors of the corroded concrete [27] and using hybrid extreme learning machine-grey wolf optimizer to predict concrete strength that partially replacement of cement [28]. A new technique off-site and self-form segmental concrete masonry was also experimentally studied [29]. Using the extended finite element method, [PERSON] and [PERSON] investigated the behavior of simply supported reinforced concrete beams [30]. The experimental program includes two RC beams loaded in two points. It was found that the mesoscale numerical model produced findings that were closer to the experimental data, and that mesoscale modeling of reinforced concrete is most convenient when the maximum aggregate size is reduced. [PERSON] et al. investigated the effects of numerous critical parameters on the energy absorption (EA) of torsional RC beams reinforced with external FRP [31]. Whereas, (81) gathered datasets for investigating (28) factors were studied and evaluated including the concrete compressive strength through using response surface methodology (RSM). They claimed that the proposed model has an acceptable correlation coefficient (R) of around 80% and is reasonably accurate and the EA also works as a safety index for FRP-strengthened RC beams exposed to torsional loads, preventing unexpected structural failure. Analytical models for predicting the stress-strain diagram of steel fiber reinforced concrete that take into account the major properties of a single type of fibers have been proposed in current research. This is because, to date, a lack of information in predicting the compressive stress-strain relation of concrete with two or more different types of fibers has been reported. For this purpose, this study was suggested to carry the experimental program for testing varied concrete samples reinforced with hybrid fibers (polyvinyl alcohol and steel fibers). The majority of the experimental results were used to develop an empirical model to simulate the HRRC stress-strain relationship. The proposed model simulation was done using the particle swarm optimization (PSO). Besides, the validity of the proposed model was verified with the experimental test results. In addition, ten finite elements (FE)models have been developed using ABAQUS software to further prove the validity of the new proposed stress-strain model when adopted in the numerical analysis methods. Concrete cubes with hybrid-fibers and steel fiber-reinforced concrete columns are used as case studies for this numerical investigation. The findings that predicted using this newly proposed model substantially align with the balanced outcomes achieved in this analysis of the experimental stress-strain curves.
## 4 Experimental Results
### Optimization Evaluation  
To determine the model's optimization, we trained the model multiple times, and the results are presented in Table 4.  
From Table 4, with an increasing number of epochs, the model's accuracy gradually improved and reached its peak at epoch 60. However, we observed a decrease when the number of epochs reached 75, indicating a possible issue of overfitting. Consequently, we decided to choose the model at epoch 60 as our model. In Table 5, the results of our model in terms of other evaluation metrics are presented. The achieved metrics were as follows: accuracy of 69.47%, precision of 76.28%, recall of 69.45%, F1-score of 72.70%, and an approximate time complexity of \(2.1\times 10^{8}\). These findings contribute to a comprehensive assessment of our model's performance across various evaluation criteria.
# Using Computer Vision to Collect Information on Cycling and Hiking Trails Users
## 2 Related Work
### Analysis of Studies
In [21], an approach to people counting in image detection and tracking was introduced. This research introduced an approach to counting people at a bus stop using image detection and tracking. They used NVIDIA Jetson AGX Xavier hardware and NVIDIA DeepStream SDK 5.0 with Python. The method implemented a Kalman filter for counting people, resulting in a notable reduction in counting errors compared to a basic detection approach. Specific details about the dataset were not provided. In [22], the study analyzed the performance of deep learning models for real-time traffic localization and detection using edge equipment. The dataset, created by the authors, included images of Vietnamese vehicles and license plates. They used Nvidia Jetson Nano as the hardware platform and compared MobileNet-SSD and YOLOv4 models for vehicle counting and license plate detection. Both models showed high accuracy in real-time tasks, with MobileNet-SSD processing at 40 frames per second (FPS) and YOLOv4 demonstrating superior detection of smaller objects with around 91% mean Average Precision (mAP). In [23], researchers developed an automatic camera-based method to count pigs in a specific area of a large-scale pig farm. They used three datasets, two created manually and one reused from a previous article. The hardware included cameras for video capture, and a deep learning algorithm, specifically a convolutional neural network (CNN), was used for pig identification and localization. The DeepSORT (Deep Simple Online and Real-time Tracking) algorithm tracked the detected pigs in real time, allowing for precise and individualized counting with around 94.95% mean Average Precision (mAP). This system provided an automated solution for accurate and real-time pig counting in agricultural settings, ensuring continuous monitoring and tracking of pig trajectories. In [24], a system was developed for real-time counting of people and bicycles using a private dataset of 339 videos. The dataset consisted of 4 s videos showing people and bicycles in motion, from which 630 frames were extracted and annotated on the Roboflow platform. The system was implemented on a Nvidia Jetson Nano 2 GB. The study comparedvarious detection models including Faster R-CNN, SSD300, SSD512, YOLOv4, YOLOv4-tiny, YOLOv5n, YOLOv5m, YOLOv5x, YOLOv5n6, YOLOv5m6, and YOLOv5x6, with an average mean Average Precision (mAP) of 44.4%. YOLOv5 showed a higher refresh rate than Faster R-CNN and SSD models and was supported by PyTorch and TensorRT. Object tracking used IOU, V-IOU, and DeepSORT methods, with V-IOU proving superior to IOU for tracking. Tests on YOLOv5 versions with TensorRT showed better results than those with PyTorch. Overall, the system demonstrated efficient detection and tracking, with the V-IOU tracking method outperforming IOU.