esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Traffic Data
### Traffic Data Preprocessing  
* Traffic data format: The traffic information provided by road operators is usually the number of vehicles crossing the highway at some measurement points. Contrary to this, the traffic simulators expect the traffic given by routes of cars or vehicle flows. Both of these models expect the start and end positions to be provided. This means that we need to transform the measurement data into some form of a flow model.
* Traffic data: In the phase of traffic data collection, we processed the real-life measured data gathered by Hungarian Public Roads. The data included traffic counter information from various sections of the modeled M0 highway with a one-hour resolution. The data covered three weeks from 2019; one represented winter, one represented spring, and one represented autumn. We split the measurement data into different periods in the day to better showcase the relevant traffic patterns. We took the measurements' average to calculate the traffic's magnitude in the specific time period. This means that the measured values of the different intervals are scaled to one hour. After that, we selected periods representing the minimum, the maximum, and the average traffic within that specific day. This method also preserved the typical traffic patterns of that period. The traffic patterns among the measurement points are visible in Figure 3. The S and N letters indicate the direction of the lanes--south or north. The numbers on the X-axis identify the measurement points. The number of vehicles passing the measurement point is represented on the Y-axis.  
Figure 3: Traffic measurement values with the sensor positions in the examined periods.
## 3 Mathematical Model
### Optimization
#### 3.2.2 Particle Swarm Optimization (PSO)  
The PSO technique uses a large number of samples to explore the optimal solution, as with GA techniques. A GA is based on a biological evolution process, whereas PSO is based on natural phenomena such as birds flocking or fish moving in a group together. PSO keeps a record of the best position of the individual and of the population using the objective function. In a PSO, _'pbest'_ and _'gbest'_ denote the best objective function for the population and for the group, respectively [37]. The velocity for each individual in a population is calculated using _'pbest'_, _'gbest'_, and its initial velocity. The new position for each individual  
Figure 3: Genetic algorithm.  
is then calculated with the help of the initial positions and their velocity. Each step for the PSO algorithm is described below.  
If we assume the 'nth' individual in a set, then its position is given by:  
\[x_{i,n}=x_{i,min}+(x_{i,max}-x_{i,min})u_{i} \tag{17}\]  
where \(x_{i,min}\) and \(x_{i,min}\) are the bounds for a variable \(x_{i}\), and \(u_{i}\) represents a random number between 0 and 1. The fitness value of the nth individual can be calculated by:  
\[A_{i,n}=f(x_{i,n}) \tag{18}\]  
In the beginning, the best fitness value (\(\mathit{pbest}_{i,n}\)) for each individual is \(A_{i,n}\), and the global best value can be calculated as:  
\[\mathit{gbest}_{i}=\text{ min}(\mathit{pbest}_{i,n}) \tag{19}\]  
The new velocity for the individual can then be calculated as:  
\[v_{i+1,n}=\varphi_{1}v_{i,n}+\psi_{1}(\mathit{pbest}_{x,j,n}-x_{i,n})u_{i}+ \psi_{2}(\mathit{gbest}_{x,j}-x_{i,n})u_{i} \tag{20}\]  
where \(\varphi_{1}\), \(\psi_{1}\), and \(\psi_{2}\) are the tuning factors, which are 0.8, 2, and 2, respectively, in our study. Now, the new position can be calculated as:  
\[x_{i+1,n}=x_{i,min}+(x_{i,max}-x_{i,min})u_{i} \tag{21}\]  
Based on the new position, we can evaluate the new fitness value, which is:  
\[A_{i+1,n}=f(x_{i+1,n}) \tag{22}\]  
If the new fitness value is less than that of \(\mathit{pbest}_{i,n}\), then it is replaced with the \(A_{i+1,n}\). The global fitness value is computed as:  
\[\mathit{gbest}_{i+1}=\text{min}(\mathit{pbest}_{i+1,n}) \tag{23}\]
## 5 Numerical Results
Actually, in all the results in Figures 1-4, 'Simulation \(E\left[\left(\hat{\theta}_{1}^{(u=1)}-\theta_{1}^{(0)}\right)^{2}\right]\)' and 'Simulation \(E\left[\left(\hat{\theta}_{1}^{(u=1,v)}-\theta_{1}^{(0)}\right)^{2}\right]\)' are approximately equal. Instead, to quantify how computationally efficient the proposed algorithm is, execution time is obtained both for analytically derived MSE and for the Monte Carlo simulation-based MSE. The number of incident signals is two, where two signals are incident from \(20^{\circ}\) and \(40^{\circ}\). The number of antenna elements is 10, and the number of snapshots is 1000. In getting the Monte Carlo simulation-based MSE, since the computational complexity is nearly proportional to the number of repetitions, the number of repetitions varies from 100 to 1000 in increments of 100. Figure 5 shows how computationally efficient the proposed algorithm is. The execution times of the simulation-based MSEs and analytic MSEs are illustrated with respect to the number of repetitions. Note that the execution time for analytically derived MSE is essentially independent of the number of repetitions. It is clearly shown in Figure 5 that execution time for the Monte Carlo simulation-based MSE is much greater than that for the analytically derived MSE even for the number of repetitions of 100. Figure 5 illustrates that getting analytically derived MSE is much less computationally intensive than getting Monte Carlo simulation based MSE, which justifies why the analytically derived MSEs should be employed for performance analysis. \[\text{Simulation }\;E\left[\left(\hat{\theta}_{1}^{(u=1)}-\theta_{1}^{(0)} \right)^{2}\right]=\frac{1}{W}\sum_{w=1}^{W}\left(\hat{\theta}_{1(w)}^{(u=1)} -\theta_{1}^{(0)}\right)^{2}\]0 Comparing the execution times between Monte Carlo simulation-based MSEs and Analytic MSEs.
## 2 Laboratory Test
### Experiments Conducted  
For the sound intensity test experiment, a matched pair of microphones is used to construct a sound intensity probe in opposition. A spacer is positioned between the microphones to maintain a constant distance, enabling sound intensity calculations using the phase difference between the two microphones. Given the focus on the mid-to-low frequency range of sound radiation generated by the specimen, a spacer column of 12 mm is selected, corresponding to an allowable bandwidth range of 125-6300 Hz. The effective bandwidth ranges from 1 to 5120 Hz with an accuracy of 1 Hz. In order to ensure the precision and legitimacy of the results, a 10 s interval is allocated for the evaluation of each data point. Within each second, four data points are taken, starting from the measurement at time 0. A total of 41 measurements are taken in each sequence, and the final result is obtained by calculating the average result derived from these 41 data points.  
According to the ISO9614-1 standard [18], valid measurements in general engineering applications occur when the sound pressure level measured exceeds the sound pressure level of background noise by 10 dB. Figure 6 displays the spectral graph of the radiated sound pressure level and the sound pressure level of background noise from the experimental results. It is discernible from Figure 6 that within the frequency range of around 1-200 Hz, the sound pressure level values measured experimentally are considerably akin to the ambient noise. This suggests that the ambient noise prevails within this frequency range. There are three main reasons for this: Firstly, the environment contains many sources of low-frequency noise, such as chassis cooling fans, which can penetrate effectively and are difficult to isolate completely. Secondly, hand-held sound intensity meters can experience slight vibrations during the measurement process, causing minor collisions between internal components and generating noise. Thirdly, the selectively absorbed high and mid-frequency sound waves increase the relative proportion of low-frequency sound due to reflections from surfaces such as the ground in the environment. In the 200-2000 Hz frequency range, most of the measured data, with the exception of a few troughs, exceeds the background noise sound pressure level by more than 10 dB, making the data obtained in this range credible and reliable. By measuring sound pressure and sound intensity at the sampling plane with the sound intensity meter and then using the radiated sound power formula, the radiated sound power within this area can be calculated directly, as shown in Figure 7.  
Figure 6: Comparison graph of sound radiation from the reinforced laminated panel and background sound pressure level.
## 1 Introduction
The combination of a high-speed camera and digital image correlation (DIC) techniques [40; 41; 42] provides an alternative method to obtain the real-time in-plane strain fields on the surface of specimens and to identify the first initiation of cracks by virtue of the strain concentration [43; 44]. In this work, we selected concrete as the targeting brittle material considering the wide use of concrete in civil engineering. The Brazilian tests were conducted on \(\Phi\)75 mm concrete discs with concentrated-load and distributed-load boundary conditions. Similar to the ordinary Brazilian test, the disc was directly compressed by two flat rigid platens, which resulted in a concentrated-load boundary. For this reason, the Brazilian discs were loaded with distributed loads and connected using two curved steel jaws to depict the distributed-load boundary. In addition, a high-resolution camera was utilized to record the deformation and failure of the specimens. To synchronously collect the images and forcing, a synchronous data acquisition system was employed. Moreover, the strain evolution on the surface of the specimens was analysed by the DIC method to examine the effects of the stress state, crack initiation, as well as crack propagation on the tensile strength of concrete. In Section 2, basic elastic theories and the experimental methodology used in the Brazilian test including the tensile strength, fracture criterion, and strain field are explained in detail. In the Brazilian test, the material of the Brazilian disc was regarded as homogeneous, isotropic, and linearly elastic before the initiation of cracks in brittle materials. In Section 3, we report the key results of the experiment. In Section 4, the failure process of specimens was examined using the DIC method and the tensile strength based on the elastic solutions is evaluated.
# Some Applications of ANN to Solar Radiation Estimation and Forecasting for Energy Applications
## 3 ANN Method and Implementation
### ANN Implementation for GHI and GTI Irradiation  
Several steps are applied in view to find the optimized MLP:  
* Choice of the network size (number of hidden layers and hidden nodes per layer): Too small a number of hidden nodes does not allow good learning, but an oversized number increases the training time with a marginal improvement [32; 33], need more data and the ANN can be over-trained. In accordance with the principle of parsimony and with the literature [34], only one hidden layer was used.
* Determination of the optimal number of neurons in the hidden layer: It is realized in testing various configurations and calculating the adequacy. Some empirical rules exist, but their efficiency is not really proven: The number of hidden neurons equal to the inputs number [35], to 75% of it [36], to the square root of the product of the number of inputs and outputs [37]. Here, the number of hidden neurons was taken between 1 and the number of inputs +1. Each MLP architecture was trained 8 times per architecture in order to avoid random effects.  
Figure 1: Architecture of an artificial neuron and a multi-layered neural network.  
Learning (or training) process: It consists in modifying the weights until the gap between the actual and simulated outputs reaches a desired accuracy. The Levenberg-Marquardt learning algorithm (LM) was used as in most studies. Another preprocess called k-fold sampling was used with the dataset [38; 39], this cross-validation is a statistical method used to estimate the skill of machine learning models. It is commonly used in applied machine learning to compare and select a model for a given predictive modeling problem, which has a lower bias than other methods [40; 41]: It consists in dividing randomly the data set into a training data set (80%) and a test data set (20%), the training set and the test set are different for each fold; this process is repeated k times and the value of the reliability metrics given in this paper are the average value on the k-fold. Here, k is taken equal to 10. Thus, the results are independent of the set of data used for the training; using only one data set (with its own statistical particularities) can reduce the robustness of the conclusions.  
Other information specific to each study will be given in the corresponding chapter.
# An Innovative Internal Calibration Strategy and Implementation for LT-1 Bistatic Spaceborne SAR
## 4 Temperature Compensation
### Temperature Compensation of Antenna Calibration Networks  
The antenna calibration networks located outside the satellite cabin has adopted active temperature control design, then a test plan was developed based on its in-orbit temperature environment. The temperature range of the test chamber is \(-20\)\({}^{\circ}\)C\(\rightarrow\)+45 \({}^{\circ}\)C, the test chamber is started and maintained for 40 min after reaching the pre-set temperature point, and then the performance indicators of the antenna calibration network are recorded.  
The measured data of multiple temperature tests were analyzed, and the temperature compensation reference value can be calculated according to Formula (13), and then the compensation error can be calculated according to Formula (14). As shown in Figure 9, within the range of \(-20\)\({}^{\circ}\)C\(\rightarrow\)+45 \({}^{\circ}\)C the amplitude compensation error of the antenna calibration networks after temperature compensation is 0.05 dB, and the phase compensation error is 0.85\({}^{\circ}\).  
Figure 8: The temperature distribution of the antenna calibration networks.  
Figure 9: \(Cont\).
# An Overview of Indoor Localization System for Human Activity Recognition (HAR) in Healthcare
## 4 Localization Methods
### Triangulation  
Triangulation uses the geometric properties of triangles to compute object locations [33]. When the AOA measurement is available, the triangulation technique can be used to estimate the location of the target node using trigonometric laws [34]. The node at unknown coordinates estimates its angle to each of the two reference nodes and, on the basis of these angles and the positions of the reference nodes, computes its position using simple trigonometrical relationships (Figure 9). The position of the target node can be determined by the intersection of the direction lines of the pair of angles formed concerning the reference nodes (A, B). After obtaining the angles \(\theta_{1}\), and \(\theta_{2}\), the physical position of T, which represents the target to be located, can then be calculated on the basis of the predetermined coordinates of the reference nodes. In three-dimensional angulation, obtaining a precise position requires one length, one azimuth, and two angle measurements. Triangulation is a complex technique, requiring knowledge not only of the position of beacons but also of their spatial rotation. Therefore, calculations are not more complex than trilateration.
# Integrated Spatio-Temporal Graph Neural Network for Traffic Forecasting
## 2 Related Work
### Traffic Flow  
Traffic flow, referring to the passage of vehicles through spatial units such as road segments or sensor points, holds pivotal importance for various applications, including traffic congestion management, traffic light optimization, and emissions reduction efforts [12].  
This survey covers traffic flow problems at road, region, and station levels, each with unique challenges. Road-level issues involve traffic volumes and metrics like traffic flow and road origin-destination [13]. Region-level flow extends the analysis to traffic volume within defined city regions, categorized by transport mode, including taxi, bike, ride-hailing, and dockless e-scooter flows. Station-level flow focuses on traffic volume at physical stations like subway or bus stops, categorized by station type.  
Despite the effectiveness of traffic sensors, data collection remains challenging due to the high deployment and maintenance costs associated with them. Alternative approaches, such as leveraging mobile and IoT devices like GPS sensors, offer potential solutions, albeit with data quality concerns [14]. Moreover, traffic light control introduces additional complexities, as signal timing changes can significantly impact traffic flow dynamics and relationships between road segments. Thus, understanding and mitigating these challenges is crucial for accurate traffic flow prediction and effective traffic management strategies.
# First Approach in Analysis of Tool Wear When Milling Additive Manufacturing (AM) Parts
## 4 Discussion  
The width of the wear flank land on IN718 L-PBF's support structures is higher than in the machining of solid material in terms of both tool travel path (Figure 2) and cutting volume (Figure 3). Generally, the higher wear of tools after support structure milling could be explained by differences in tool wear mechanisms. The milling of IN718 as a solid material results in the abrasive wear of tools (Figure 4a), and the machining of support structures additionally results in surface attrition (Figure 4b). Surface attrition is a result of the cyclical thermal and mechanical loading of the tool [19]. Furthermore, the milling of support structures leads to a higher fluctuation in the resulting bending moments. One explanation for this is higher vibrations due to the cyclical loading of the tool, which correlates with the tool wear observations. In Figure 5, the number of peaks of the bending moment per path is 10 and corresponds to the number of nodal points of the support structure per path (compared to Figure 1d,e). The higher the cutting volume per path, the higher the amplitude of the fluctuations of the bending moments. As such, the recurring impact at every support structure beam leads to the fatigue of the tool's cutting edge, resulting in cracks and pitting.  
After milling 304H WAAM, the tools exhibit a more significant increase in wear on the surface layer, particularly for path lengths starting from 1920 mm, compared to the solid material (Figure 6). At path lengths of 640 and 1280 mm, the width of the flank wear land is relatively higher when milling solid material due to the chipping of the tool. The microstructure, and consequently the hardness, often varies within WAAM parts, for example, changing with the height of the part [17]. This variation can account for the differences in tool wear. Moreover, the thermal conditions at the surface differ from those inside the specimen, leading to greater heterogeneity that could affect the results of tool wear.  
The measurements of bending moments (Figure 7) from 304H WAAM milling tests indicate that the moments are lower when milling the surface layer, which can be attributed to the material removal rate at the surface compared to the solid material. Considering tool wear, it could be inferred that the surface layer's hardness is higher than that of the solid layer below, as lower forces resulting in higher tool wear may be due to the higher hardness of the surface region, as the results in Figure 9 show. Differences in microstructure  
Figure 9: Hardness measurements depending on distance to 304H WAAM specimen surface.  
depending on the distance to the surface have also been observed in [20], which supports the assumption of a hardness gradient reported within this study.  
Most likely, an abrasive mechanism predominates the tool wear during 304H WAAM milling. The occurrence of corner chipping is also common in both of the aforementioned milling tests, resulting from the overloading of milling tools.
## 3 Analysis of Vertical Vibrations in Track-Bridge Structures Under Short-Wavelength Irregularities
### Model Validation
In addition, the rail vibration displacement and acceleration time histories calculated by both methods show excellent consistency. The displacement amplitude of the rail is 0.9663 mm (hybrid method) and 0.9621 mm (FEM), with an error of 0.44%. The acceleration amplitude of the rail is \(1.0056\times 10^{3}\) m/s\({}^{2}\) (hybrid method) and \(0.9942\times 10^{3}\) m/s\({}^{2}\) (FEM), with an error of 1.15%. The displacement amplitude of the track slab is 0.4645 mm (hybrid method) and 0.4456 mm (FEM), with an error of 4.24%. The acceleration amplitude of the track slab is \(43.9355\) m/s\({}^{2}\) (hybrid method) and \(42.3621\) m/s\({}^{2}\) (FEM), with an error of 3.71%. The results indicate that under short-wavelength irregularity excitation, the rail exhibits relatively high vibration accelerations but small vibration displacements. Meanwhile, both the acceleration and displacement of the track slab are substantially reduced, highlighting the excellent vibration-damping performance of the rail fasteners. These differences fall within acceptable engineering limits, further verifying the accuracy and efficiency of the hybrid method for rapid vibration prediction of track-bridge structures. This establishes a solid foundation for the multi-objective optimization analysis presented in the subsequent sections.
# Analysis and Design of a Diplexing Power Divider for Ku-Band Satellite Applications
## 4 Conclusions  
A new technique for integrating diplexers and power dividers was proposed and validated experimentally at Ku-frequency with the aim of applications in space communication. For space exploration and deep space probe observation, precise multiband and multifunction communication devices are required. The proposed device is based on extended connectors, via transitions, substrate integrated waveguides and stacked printed circuit boards that make it a modular device. Two different frequency spans of 13-15.8 GHz and 16.6-18.2 GHz are envisioned with this technique covering Ku-band applications. The achieved insertion loss for the power divider was less than 1.5 dB. This compact structure with diplexing power dividing features was designed, fabricated, and tested in various solutions.  
Conceptualization, F.K., A.A.-e.-E. and H.B.; validation, F.K., A.A.-e.-E. and A.G.; writing--original draft preparation, F.K.; writing--review and editing, H.B., L.T. and A.G.; funding acquisition, H.B. and L.T.; Supervision, H.B. and L.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are openly available.  
This research was supported by FRQNT and NSERC.  
The authors declare no conflict of interest.