esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Previous Architecture of the People-Counting System  
The case study for our research focuses on the campus of Cesena, part of the University of Bologna. The architecture of the people-counting system was proposed and described in depth in [41]. Here, we summarize the main components of such a system, their organization, and how they interact with each other. In [41], the architecture of the people-counting system is been thoroughly detailed, together with the main UIs. In this section, we provide an overview of the system's key components, their organization, and their interactions.  
The system is designed with three layers, each serving a distinct function. The initial layer is the data acquisition layer, utilizing Intel RealSense D415 Depth cameras connected to a Raspberry Pi 4 Model B via USB. These cameras capture high-resolution images at 1280 \(\times\) 720 pixels every five minutes. This interval is set to optimize storage management, though it can be modified to suit specific operational requirements. This layer is responsible for collecting raw visual data from the environment, which forms the foundation for subsequent processing stages. The second layer is the prediction layer, which processes the images collected by the cameras. This layer employs a custom model based on YOLOv3 (You Only Look Once version 3), an advanced object detection algorithm. The model analyzes the images to detect and count the number of individuals present. It achieves this by segmenting each image into various regions, applying bounding boxes around detected figures, and assigning confidence intervals to each detection. The YOLOv3 model has been fine-tuned through transfer learning to enhance its accuracy in the specific context of this application. The processed results, including the number of detected individuals and corresponding timestamps, are temporarily stored in a CSV file for later retrieval. The final layer is the API layer, which is built around an HTTP server. This server handles requests for data and retrieves the information stored in the CSV file. When a request is made, the server accesses the CSV file to extract the relevant data and sends it back to the requester. This layer acts as the interface through which users can access the processed information from the prediction layer, facilitating easy and efficient data retrieval.  
The three-layer architecture follows a fat client-thin server model, offering advantages such as increased scalability, semi-offline functionality, enhanced availability, and compliance with privacy regulations.  
The current User Interface is built as a web application, primarily devoted to visualizing information about lessons and students who are attending them for each course of study that takes place on the Cesena Campus.
## 4 Simulation and Analysis
### Simulation Experiment 2  
In this simulation experiment, the motion mode of the target is set as a periodic turning and climbing motion, which is a typical tactical maneuver in the BVR air combat. Set \(n_{xt}=0\), \(n_{yt}=1.06\), and \(n_{zt}=4\sin(0.1t)\), where \(t\) is the simulation time. Based on the method proposed in this study and the above simulation settings, the simulation results are shown in Figures 11-13.  
As can be seen from Figure 11, the three methods can still generate correct attack guidance trajectories as the target is maneuvering in three dimensions. The trajectories are roughly similar with a certain difference. It can be clearly seen from Figure 12 that the proposed method can rapidly reduce the aiming error angle and stabilize it near zero with little oscillation. By contrast, the other two algorithms have various degrees of aiming error angle oscillation with target maneuvering. The aiming error angle in the horizontal direction is obviously larger than that in vertical direction because of the maneuver magnitude of the target. Besides, the variation amplitude and trend of control overload of the three methods are roughly similar (see Figure 13).  
Comparisons of quantitative evaluation indexes of the three methods in simulation experiment 2 are shown in Table 3.  
As can be seen from the data in Table 3, the proposed method is obviously superior to the PID and the AFC method in terms of evaluation indexes of \(I_{e}\) and \(I_{d}\). In other words, its aiming efficiency is relatively higher and it can provide longer high-precision aiming time for the UCAV. Specifically, the proposed method has around 87 s and 70 s more high-precision aiming time than the PID and the AFC method respectively. Moreover, based on the above advantages, the control overload cost of the proposed method is also slightly less than that of the other methods. Through simulation experiment 2, the effectiveness and superiority of the proposed method are further verified.
# A New Constructive Heuristic Driven by Machine Learning for the Traveling Salesman Problem
## 2 Materials and Methods
### Statistical Study
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Position** & **Method** & **TPR** & **FPR** & **Accuracy** & **Precision** \\ \hline \multirow{3}{*}{1} & MF & 92.57\% & 61.65\% & 85.20\% & 90.52\% \\  & FI & 77.39\% & 46.26\% & 74.18\% & 91.41\% \\  & CW & 82.79\% & 46.56\% & 78.80\% & 91.88\% \\ \hline \multirow{3}{*}{2} & MF & 83.21\% & 29.57\% & 78.19\% & 81.30\% \\  & FI & 66.00\% & 26.20\% & 69.06\% & 79.56\% \\  & CW & 72.29\% & 27.01\% & 72.57\% & 80.53\% \\ \hline \multirow{3}{*}{3} & MF & 52.41\% & 9.23\% & 81.59\% & 64.15\% \\  & FI & 44.80\% & 15.99\% & 74.62\% & 46.87\% \\  & CW & 55.03\% & 15.04\% & 77.79\% & 53.54\% \\ \hline \multirow{3}{*}{4} & MF & 38.47\% & 4.79\% & 88.15\% & 53.28\% \\  & FI & 38.96\% & 11.09\% & 82.70\% & 33.30\% \\  & CW & 45.99\% & 10.40\% & 84.18\% & 38.59\% \\ \hline \multirow{3}{*}{5} & MF & 27.12\% & 2.27\% & 93.75\% & 41.65\% \\  & FI & 30.59\% & 7.88\% & 88.65\% & 18.82\% \\ \cline{1-1}  & CW & 36.20\% & 5.75\% & 90.98\% & 27.35\% \\ \hline \multirow{3}{*}{\(>\)5} & MF & 22.72\% & 0.01\% & 99.98\% & 13.94\% \\  & FI & 27.55\% & 0.03\% & 99.97\% & 9.60\% \\ \cline{1-1}  & CW & 29.01\% & 0.02\% & 99.98\% & 14.71\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: TPR, FPR, accuracy and precision comparison across several positions and methods. Figure 3: TPR (**A**) and FPR (**B**) comparison for MF, FI and CW heuristics. The first five positions in the CL are considered separately, while all the others are shown in the \(>\)5 bars. To get a better look at the results shown in Figure 3, Table 1 emphasizes the values obtained during the experiment. MF exhibits a higher TPR for shorter edges as expected, while CW performs better with longer edges. However, less obvious is MF's higher FPR in the first position. The latter fact brings attention to the importance of decreasing the FPR for the most frequent position. Note that the CW's precision is higher concerning the other ones for the first position, which can be read as the main reason why CW comes up with better TSP solutions than MF. So, one of the main reasons for using ML for TSP is to reduce the FPR during the construction of the solution.
# Influence of Uneven Subgrade Frost Heave on Deformation and Damage of CRTSIII Slab Track
## 1 Introduction
In addition, they cannot analyze the influence of track structure concrete damage on its further deformation. With due considerations to the durability requirement of track structure in long-term operations, it is necessary to carry out systematic research on the deformation and damage characteristics of slab track under frost heave deformation. In recent years, with the development of the theory of damage mechanics and its gradual application in damage research of concrete, [PERSON] [24] proposed the concrete  
Figure 1: CRTSIII slab track structure. damaged plasticity (CDP) theory, which was improved by [PERSON] [25]. [PERSON], [PERSON] et al. [26] analyzed the influence of cement asphalt mortar debonding on the damage distribution and mechanical response of CRTSII prefabricated slab by using the CDP theory. [PERSON], [PERSON] et al. [27] predicted the damage behavior of high-strength compressive concrete under static and dynamic loads by using the CDP theory. Thus, the CDP theory is applicable for analyzing concrete material damage, which can provide support for the prediction of slab track deformation and damage in the present study. In the present study, based on the FEM method and the concrete damage plasticity (CDP) theory, a static model of CRTSIII slab track with damage plasticity and reinforcement is established to analyze the influence of uneven frost heave on track structure deformation and damage. This study also highlights the advantages of a damage plasticity model compared with a linear elastic model. Based on the damage plastic model, the influences of various frost heaving parameters on the deformation, interlayer seam, as well as the stress and damage behaviors of track structure are analyzed, which could provide a theoretical reference for the maintenance of CRTSIII slab track in frozen soil areas.
## 5 Deep Reinforcement Learning for Workflow Scheduling
### Problem Description in the Form of RL  
#### 5.1.1 State Space  
The state \(s_{\_}t\) consists of two components: workflows/sub-tasks state and environment state. The workflows/sub-tasks state refers to the adjacency matrix and sequence embedding, which remain unchanged throughout the entire execution process. The environment state includes the remaining resource capacity and the current timestamp in the system.
# Practical Demonstration of 5G NR Transport Over-Fiber System with Convolutional Neural Network
## 3 Convolutional Neural Network Architecture
### CNN Model Salient Features
#### 3.1.2 Activation Functions  
A node's capacity is not defined apriori. The activation helps to deduce it. It accomplishes this by establishing a relationship between the node's various weights and biases and then applying the relationship to the node as a function, so generating responses. It also assists it in learning complex data patterns. They convert the node's incoming signals into an output signal that will either be used in the network's next layer or will be the output. In contrast to the gradient of sigmoids, which grows smaller as the absolute value of gradient increases, the chance of a vanishing gradient is lesser when considering ReLu. ReLu has a constant gradient, which aids learning and makes it the better choice.
# Efficient Crowd Anomaly Detection Using Sparse Feature Tracking and Neural Network
## 3 Materials and Methods  
Figure 1 provides a schematic representation of the proposed methodology, which is organised into two distinct sections: (i) crowd representation and behaviour descriptors and (ii) dimensionality reduction and classification. In the first section, the crowd is represented through the extraction of feature points from every L (default 20) frame, forming trajectories and Delaunay triangles. The visual descriptors capturing spatial and temporal information, such as the velocity, density, etc., are employed to elucidate the crowd's state. Moving  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Methods**} & **Datasets** & \multirow{2}{*}{**Performance Metrics**} \\  & **Used** & \\ \hline GAN [24] & ShanghaiTech & 73.8\% AUC \\ RNN, 2D CNN [25] & Violent- & 93.53\% Accuracy \\ CNN, RNN KNN, Optical Flow [26] & ShanghaiTech & 73.62\% Accuracy \\  & Hajj & \\ Optical Flow GAN [27] & datasets & 79.63\% Accuracy 98.1\% AUC \\  & UMN & \\ CNN Residual LSTM [28] & UCF-Crime & 70.4\% AUC \\ CNN [29] & ShanghaiTech & 240.0 MAE, 260.5 MSE \\ CNN, Random Forest [23] & HajjV2 & 76.08\% AUC \\ Optical Flow [30] & ShanghaiTech & 89.29\% AUC \\ CNN, Histogram of Optical Flow, SVM [22] & HajjV2 & 88.96\% AUC \\ gKLT + Collectiveness Energy Index (CEI) [31] & UMN & Scene 1: 92.32\%, Scene 3: 94.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Recent works on crowd anomaly detection.  
on to the next section, the descriptors for a frame are computed and they are aggregated into histograms. These histograms undergo a dimensionality reduction process using PCA/autoencoders and are subsequently subjected to classification using neural networks (NN) to categorise normal or abnormal behaviour accurately. The methods used in our approach are further elucidated in the following sub-sections.
# A Survey on Traffic Prediction Techniques Using Artificial Intelligence for Communication Networks
## 8 Statistical Models
### Bayesian Estimation  
In passive optical networks (PONs), an optical line terminal (OLT) sends packets of data to optical network units (ONUs) located at the user end. [PERSON], [PERSON], and [PERSON] [53] use Bayesian Estimation to predict the interarrival time of packets from the OLT to the ONU. Calculations are performed at the OLT, such as the necessary sleep/doze time, and the predicted traffic accumulated during sleep is calculated. The command is then sent from the OLT to the ONU, telling it when to sleep and wake up to receive the next packet. The ONU estimates the average interarrival time of packets using Bayesian estimation and returns its estimation to the OLT for calculations. Bayesian estimation is a probability-based statistical method, used in this paper to find the most likely average number of interarrival packets. The process is iterated to update the likelihood of the average number of packets with each additional measurement. Using this method, the authors are able to achieve 63% energy savings, with a 13% reduction in the average delay that is expected from the ONU sleeping and waking.  
[PERSON] et al. [54] propose wavelength splitting optimization algorithms to maximize traffic throughput during periods of traffic spikes in elastic optical networks. Instead of turning equipment on and off to handle high traffic volumes, traffic is split into different lightpaths when a threshold is reached. Traffic is processed through four algorithms to determine the number of splitpoints, which lightpaths to split, resource allocation after a split, and incremental traffic routing. The authors found that wavelength splitting optimization could increase the overall traffic throughput in a network, assuming that the network traffic could be split to begin with. One downside of traffic splitting is that it interrupts network traffic for a short amount of time to tear down and create new lightpaths. However, the proposed optimization technique is a viable solution for short-term traffic spikes lasting from hours to days as it only requires a few minutes of calculation and network reconfiguration.
## 1 Introduction
### Long-Range Vehicular Communications State-of-the-Art
In dark conditions, the sensitivity is high, whereas as the power of the background light is increasing, the sensitivity is decreasing. This way, in strong light conditions, the possibility of photodiode saturation is mitigated. In addition to photoelectron saturation, the incident light also generates a proportional shot noise component. Together with the internal thermal noise, these two causes represent the main sources of noise perturbing VLC. Both the thermal and the shot noise are additive white Gaussian noise sources. Additional details regarding their effects on optical communications can be found in [2; 53; 54]. In conclusion, the usage of a logarithmic transimpedance circuit should prevent photodiode saturation and from this point, the VLC receiver data decoding capability will be determined by the ability of the signal processing plan to extract the data.
# Comparative Study of Machine Learning Classifiers for Modelling Road Traffic Accidents
## 3 Methodology
### Machine Learning Classifiers
#### 3.1.3 k-Nearest Neighbour  
k-NN classifiers, also known as lazy learners, are a form of instance-based method and are among the simplest classifiers that can handle classification problems well. The algorithm is a supervised method that employs both regression and classification. The \(k\) refers to the number of the nearest neighbours a model can consider [43; 44]. The algorithm works on similarity measures between new data and categorises the new data into groups related to the available classes. One of the advantages of the algorithm is that it is straightforward to implement. During the analysis, \(k\) values of 5 and 10 were used.
## 2 Materials and Methods
### Conceptual Comparison Between MORSE and a Previously Tested Perturbation Method  
Our perturbation method differs from a similar method originally proposed by [34] in the context of linear programming and later implemented for binary/integer problems by [6]. In the latter work, each binary and integer coefficient in the objective function is multiplied by the quantity \((1+\varepsilon\xi_{ij})\), where \(\varepsilon\) is a positive constant and \(\xi_{ij}\overset{iid}{\sim}U(-1,1)\). In both cited studies, the choice of \(\varepsilon\) did not depend on the input instance. As a consequence, optima may not be preserved; indeed, the fact that small perturbations can change optimal solutions was a key point on non-robustness that [PERSON] and [PERSON] made in their perturbation analysis. [PERSON] and [PERSON] [6] tested their version of a perturbation method on just three instances, and only for _fixed_ values of \(\varepsilon\), and found that the results were not favorable for finding all optimal solutions. [PERSON] and [PERSON] did not prove that their perturbation preserved optimal solutions, although their intent was to preserve optima or near-optima. In contrast, our MORSE method multiplies the coefficient for each binary and integer variable by a random real number in the range \([1-\varepsilon,1+\varepsilon]\), where \(\varepsilon\) is determined _adaptively_ based on the size of the coefficients in the objective function and the bounds on the objective function variables (see Section 2.2). As shown above, the perturbed instances generated by MORSE preserve optimal solutions.
## 3 Results and Discussion
### Evaluation of Self-Healing Performance as a Function of Temperature and pH
#### 3.2.1 Volume Change in Cracked Section
These results indicate that the void volume decreased with a pH in the order of 12.0 \(>\) 9.0 \(>\) 10.5 at a self-healing temperature of 40 \({}^{\circ}\)C; according to the literature, these conditions are known to be advantageous for  
Figure 8: Scanning area and calculation of crack width, as presented in [28]. Figure 9: Conceptual diagram of the frequencies of CT-values. the generation of vaterite [24]. Furthermore, the volume reduction was about 20%-25% higher in the CN series than in the CM series, indicating that the supply of nanobubbles with CO\({}_{3}\)\({}^{2-}\) ions was more effective for self-healing than the supply of microbubbles. Figures 12 and 13 show X-ray CT images of cracks before and after self-healing at different temperatures and pH values. By combining the luminance of the CT images and the difference in the density of the cement-paste test bodies, the images appear dimmer: the lower the density, the darker the image [28]. The green, red, and blue colors on the left side (X-CT images) of the figure represent the front face, side face, and top face, respectively, in the image domain of the test bodies in the 2D images, while the histograms show the volume of the cement matrix and section with voids (voids + cracks) [1]. Since there were no marked changes in the void structures before and after self-healing, as revealed by the analysis of the 3D images in Figures 12 and 13, most of the volume reduction in the section with voids resulted from the closing of the crack by self-healing. It was thus concluded that when ultrafine nanobubbles containing CO\({}_{3}\)\({}^{2-}\) ions were supplied with nanoscale Ca\({}^{2+}\) ions of a saturated Ca(OH)\({}_{2}\) solution at a controlled temperature and pH, the generation and precipitation of self-healing materials was promoted not only on the surface, but also inside the crack. Figure 11: Volume change as a function of pH (temp. = 40 \({}^{\circ}\)C). Note: CM: Ca(OH)\({}_{2}\) + microbubbles; CN: Ca(OH)\({}_{2}\) + nanobubbles; 9.0, 10.5, 12.0: pH of self-healing. Figure 10: Volume change as a function of self-healing temperature (pH = 9.0). Note: CM: Ca(OH)\({}_{2}\) + microbubbles; CN: Ca(OH)\({}_{2}\) + nanobubbles; 20, 40, 60: Temperature of self-healing (\({}^{\circ}\)C). Figure 12: X-ray CT images of cracks before and after self-healing at different temperatures and a fixed pH of 9.0: (**a**) CM series; (**b**) CN series.
## 3 Results
### Effect of Focal Point Position  
Figure 11 shows the results of measuring cupula displacement by changing the focal position of the ultrasound transducer. In previous simulations, the focus was fixed at the position (\(-\)3, 0, 42), but in this analysis, the focal position was shifted from x = \(-\)3 mm to x = \(-\)2 mm, \(-\)1 mm, 1 mm, 2 mm, 3 mm to observe the displacement changes. The main objective of this study was not only to assess the feasibility of SCC stimulation using focused ultrasound but also to verify whether the direction of rotational sensation could be controlled by adjusting the focal position.  
The results show that the direction of displacement significantly changes according to the focal position. Referring to the FEM domain in Figure 1, previous analyses were based on situations where x < 0. In those cases, the acoustic streaming was configured to occur primarily under the left ampulla area near x = \(-\)3 mm, resulting in clockwise streaming velocity (Figure 7a). When the focus moves closer to the center of the SCC, more acoustic dissipation is delivered to the temporal bone rather than the SCC. This leads to a decrease in the streaming velocity and cupula displacement. Consequently, the displacement decreases as the focal point moves toward the center of the SCC: 0.68 \(\upmu\)m at x = \(-\)3 mm (Figure 11c), 0.50 \(\upmu\)m at x = \(-\)2 mm (Figure 11b), and 0.24 \(\upmu\)m at x = \(-\)1 mm (Figure 11a).  
On the other hand, when x > 0, the endolymph flow inside the SCC shows a counterclockwise direction, resulting in the opposite displacement of the cupula compared to the previous case. Similarly, when the focal point moves closer to the outer edge of the SCC, greater displacement is observed: \(-\)0.25 \(\upmu\)m at x = 1 mm (Figure 11d), \(-\)0.48 \(\upmu\)m at x = 2 mm (Figure 11e), and \(-\)0.57 \(\upmu\)m at x = 3 mm (Figure 11f). These results suggest that the direction of the cupula displacement can be controlled by adjusting the focal position of the transducer, thereby enabling the control of both clockwise and counterclockwise flows.  
Figure 10: Comparison of temperature changes at 800 kHz with and without the presence of the SCC. The graph shows the temperature variation in the temporal bone at the same point (z = 39.4 mm) in both cases and in the endolymph region (z = 42 mm) when the SCC is present.  
Additionally, the difference in displacement magnitude depending on the focal position indicates that the greatest streaming velocity and cupula displacement occur when the ultrasound is applied tangentially to the outer circumference of the SCC. This implies that when the acoustic wave is focused in a specific direction within the SCC, it can induce greater fluid flow and deformation, which confirms the effective control of vestibular stimulation.  
In conclusion, this study demonstrates that by adjusting the focal position, the direction and magnitude of the fluid flow within the SCC can be controlled. This further strengthens the feasibility of controlling rotational sensations through ultrasound vestibular stimulation.