esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### SEM Analysis
#### 3.2.2 Zeolite  
SEM micrographs of the synthesized zeolite are shown in Figure 3a,b. The measured size of the synthesized zeolites ranges from 9 \(\upmu\)m to 46 \(\upmu\)m, with an average dimension of approximately 45 \(\upmu\)m. This average size notably exceeds the measurements reported by earlier researchers [47; 48]. Furthermore, the particles exhibit an octahedral three-dimensional crystalline structure, a characteristic of analcime-type zeolite [49]. The EDS spectrum in Figure 3c and the oxide weight percentages listed in Table 1 for the obtained zeolite reveal that SiO\({}_{2}\) is the predominant component (54.72%), which is consistent with the starting material, whereas the oxides MgO, K\({}_{2}\)O, CaO, and Fe\({}_{2}\)O\({}_{3}\) comprise 11.35%.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Weight Percentage \%**} \\ \hline
**Oxides** & **Ignimbrite** & **Zeolite** & **TiO\({}_{2}\)** & **TiO\({}_{2}\)/Zeolite** \\ \hline SiO\({}_{2}\) & 74.97 & 54.72 & — & 73.55 \\ Al\({}_{2}\)O\({}_{3}\) & 13.52 & 19.60 & — & 5.77 \\ Na\({}_{2}\)O & 4.38 & 12.98 & — & 1.06 \\ TiO\({}_{2}\) & 0.18 & 0.52 & 99.52 & 17.54 \\ Cl & 0.37 & 0.83 & 0.48 & 1.18 \\ Others & 6.58 & 11.35 & — & 0.90 \\ \hline Total & 100 & 100 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Weight percentages of oxides in the samples.  
Figure 2: SEM micrograph of (**a**) milling ignimbrite and (**b**) the analyzed area of ignimbrite. (**c**) EDS of the area analyzed in (**b**).
# Predictive Optimization of the Heat Demand in Buildings at the City Level
## 3 Methods
### Forecast Models  
For indoor temperature forecast, a previously developed dynamic model was applied and is presented here in Equation (1) [26]:  
\[\hat{T}_{in,k}(t)=a_{k}\{T_{in,k}(t-1)+\frac{\Delta t}{C_{k}}\Big{[}x_{1,k}P_{k }(t-1)+x_{2,k}P_{k}(t-3)-U_{k}\Big{(}T_{in,k}(t-1)-T_{out,k}(t-1)\Big{)}\Big{]} \}+b_{k}, \tag{1}\]  
where \(k\) denotes an individual building, \(a_{k}\), \(b_{k}\), \(x_{1,k}\), and \(x_{2,k}\) are estimated model parameters and \(C_{k}\) (kJ/K) and \(U_{k}\) (kW/K) are building specific physical constants the thermal capacity and the heat loss coefficient respectively. \(\hat{T}_{in,k}(t)\) is the forecasted indoor temperature at time \(t\) and \(T_{in,k}(t-1)\) is the indoor temperature at time \(t-1\). \(\Delta t\) is the time step between the values \(\hat{T}_{in,k}(t)\) and \(T_{in,k}(t-1)\) namely 3600 s (1 h). \(P_{k}(t-1)\) and \(P_{k}(t-3)\) are the heat demands at times \(t-1\) and \(t-3\) respectively. \(T_{out,k}(t-1)\) is the outdoor temperature at time \(t-1\).  
The applied heat demand model is presented in Equation (2) [27]:  
\[\hat{P}_{k}(t)=\Big{\{}a_{k}\Big{[}P_{k}(t-1)-U_{k}\Big{(}T_{in,k}(t)-T_{out,k} (t)\Big{)}\Big{]}+b_{k}\Big{\}}+w_{k}(t), \tag{2}\]  
where \(a_{k}\) and \(b_{k}\) are estimated model parameters, \(\hat{P}_{k}(t)\) is the forecasted heat demand at time \(t\), \(P_{k}(t-1)\) is the heat demand at time \(t-1\) and \(w_{k}(t)\) is the value of the residual model at time \(t\). The residual model is the hourly average of the residuals of the output from the dynamic model (inside the brackets in Equation (2)) and the measured heat demand.  
When forecasting the indoor temperature and heat demand, the future values are calculated recursively using previously forecasted indoor temperatures and heat demands as inputs. Outdoor temperature forecast should also be used but in this study the simulations were done using measured outdoor temperatures. In previous studies, the average modelling error for the indoor temperature model was below 5% [26] and for the city level heat demand forecast it was 4% [27]. For more in-depth discussion on the applied models and their validation, readers are referred to [26] and [27]. However, it must be noted that the models were developed for the optimization of the heat demand at the city level. Therefore, they have low amounts of free parameters and input variables to enable the ease of application to the whole building stock comprising hundreds or even thousands of buildings.
# Implementation of an Obstacle Recognition System for the Blind
## 3 Obstacle Recognition Model and System Architecture
### Obstacle Recognition Model  
#### 3.1.1 Data  
We took photos of the crosswalk lights and bollards directly to collect data to train the obstacle recognition model. It was important to use obstacle images with various backgrounds in the training model. Therefore, crosswalk lights and bollards were photographed at various locations and times of day and night to obtain different backgrounds for the training data. In addition to the various backgrounds, it was also necessary to photograph the obstacles from several different angles.  
Bollards have various standard specifications from city to city and look different even if they are the same size. We decided to collect images of the bollards near Sookmyung Women's University. In the case of crosswalk lights, we collected images of both green and red lights. The various data was gathered not only during the day but the night as well. This was necessary for the ability to detect crosswalk lights even in the dark of night. Additionally, crosswalk lights have three types of lights (red, green, and flashing), so data was accumulated for each type. Therefore, we collected six types of data in total for crosswalk lights, using a combination of the two conditions (day and night) and the three types of lights (red, green, and flashing). Test data were also collected, with the same method as the training data, to confirm whether the trained model worked proficiently.  
The data used to train the model required not only image data but also position information of obstacles within the image. The position data was saved as an extensible markup language (XML) data  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Navigation Aid** & **System with Radio-Frequency** & **Proposed Obstacle** \\  & **for Blind [10]** & **Identification (RFID) [11]** & **Recognition System** \\ \hline Sensors Used & Several ultrasonic & Several RFID & One camera \\ Obstacle type recognition & Impossible & Possible & Possible \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between related works and the proposed system.  
file by marking the coordinates of each obstacle within the image as a bounding box. The LabelImg tool [14], an image labeling utility program, was used to generate such data. This tool could store various output formats by adding boxes of objects in the images or videos. In this way, we have created approximately 6350 pairs of image data and position data for training and 1000 pairs, thus far, for test data.
# Frequency Contour-Strip Method for Characterization of Damage in Structures under Noisy Conditions
## 2 Transverse Vibration of Timoshenko Beam Bearing a Crack
### Intact Beam  
Assuming the beam is undamaged, the general solutions of Equations (1a) and (1b) can be derived based on the method of separation of variables as \(w(v,t)=W(v)T(t)\) and \(\psi(v,t)=\Psi(v)T(t)\), where \(T(t)\) is only a function of time t. \(W(v)\) and \(\Psi(v)\) are given as follows:  
\[W(v)=A_{1}\cosh\lambda_{1}v+A_{2}\text{sinh}\lambda_{1}v+A_{3}\cos\lambda_{2} v+A_{4}\sin\lambda_{2}v \tag{2a}\]  
Figure 1: A cantilever beam with a crack.  
\[\Psi(v)=A_{2}m_{1}\cosh\lambda_{1}v+A_{1}m_{1}\text{sinh}\lambda_{1}v-A_{4}m_{2} \cos\lambda_{2}v+A_{3}m_{2}\sin\lambda_{2}v \tag{2b}\]  
where \(A_{i}\)(_i_ = 1-4) are constants to be determined with the boundary conditions; \(\lambda_{1},\ \lambda_{2},\ m_{1},\ m_{2}\), are defined, respectively, as  
\[\lambda_{1}=\left(\sqrt{a^{2}-b}-a\right)^{1/2},\ \lambda_{2}=\left( \sqrt{a^{2}-b}+a\right)^{1/2},\ m_{1}=\frac{\tau s+\lambda_{1}^{2}}{\lambda_{1 }},\ m_{2}=\frac{\tau s-\lambda_{2}^{2}}{\lambda_{2}}, \tag{3}\] \[a=\frac{\tau(r+s)}{2},\ b= \tau(\tau rs-1),\ r=\frac{I}{Dl^{2}},\ s=\frac{E}{E\varGamma},\ \tau=\frac{\varrho \varGamma}{\varGamma}L^{4}\omega^{2},\]  
with \(\omega\) being the eigen-frequency. For a certain beam, the geometric and physical parameters are all given. Therefore, \(\lambda_{1},\ \lambda_{2},\ m_{1},\ m_{2}\), are only the functions of \(\omega\), respectively.
# RTK with the Assistance of an IMU-Based Pedestrian Navigation Algorithm for Smartphones
###### Abstract  
Real-time kinematic (RTK) technique is widely used in modern society because of its high accuracy and real-time positioning. The appearance of Android P and the application of BCM47755 chipset make it possible to use single-frequency RTK and dual-frequency RTK on smartphones. The Xiaomi Mi 8 is the first dual-frequency Global Navigation Satellite System (GNSS) smartphone equipped with BCM47755 chipset. However, the performance of RTK in urban areas is much poorer compared with its performance under the open sky because the satellite signals can be blocked by the buildings and trees. RTK can't provide the positioning results in some specific areas such as the urban canyons and the crossings under an overpass. This paper combines RTK with an IMU-based pedestrian navigation algorithm. We utilize attitude and heading reference system (AHRS) algorithm and zero velocity update (ZUPT) algorithm based on micro electro mechanical systems (MEMS) inertial measurement unit (IMU) in smartphones to assist RTK for the sake of improving positioning performance in urban areas. Some tests are carried out to verify the performance of RTK on the Xiaomi Mi 8 and we respectively assess the performances of RTK with and without the assistance of an IMU-based pedestrian navigation algorithm in urban areas. Results on actual tests show RTK with the assistance of an IMU-based pedestrian navigation algorithm is more robust and adaptable to complex environments than that without it.  
GNSS; android smartphones; RTK; AHRS; ZUPT; MEMS-IMU 14 June 2019; Accepted: 19 July 2019; Published: 22 July 2019
# Interpolation-Filtering Method for Image Improvement in Digital Holography
## 2 Image Quality Enhancements in Digital Holography
### Noise Reduction Methods in Digital Holography
#### 2.3.1 Suppressing Zero-Order and Twin Image  
Eliminating parasitic diffraction orders, in the simplest case, implies registration of an off-axis hologram. Diffraction orders appear spatially separated in the reconstructed image plane [3]. The realization of such an approach is not always possible, and in certain situations, it does not allow the full removal of the influence of parasitic diffraction orders.  
One of the popular digital methods is the suppression of low frequencies in the Fourier plane of the hologram [51; 75]. This approach mainly decreases the zero-order. Window filtering in the plane of spatial frequencies better copes with the task of suppression of zero-order and twin images but requires a more accurate selection of suppressed frequencies.  
To overcome negative diffraction orders, a hardware-software method based on phase steps in standard [85] and in parallel [86] has been developed. Holograms are registered with different phase shifts. It is possible to determine the complex amplitude of the object beam by solving a system of equations like (1). The disadvantage of the standard method is the necessity of precise control of the mutual positioning of optical elements since the phase steps must be controllable. The need to register several holograms limits the applicability of the method in dynamic problems.  
There are also longer approaches based on iterative filtering, regularized inversion, and compressive sensing [41; 82; 83; 84]. Deep learning-based methods have also become widely used nowadays [84; 85; 86; 87; 88; 89; 90]. They can utilize both learning from samples and blind reconstruction [87].
## 4 Empirical Results and Discussion
### Ranking Consistency Analysis  
Kendall's tau rank correlation coefficient was employed to evaluate the relationship between PageRank rankings and the official final standings. Using the top eight teams recognized by the tournament's official awards each year as the reference sample, Kendall's tau was computed for each season from 2013 to 2023, as shown in Table 1. In 10 out of the 11 seasons, Kendall's tau values exceeded 0.70, and in most cases, the corresponding \(p\)-values were below 0.05, indicating statistically significant correlations. Notably, the values for 2014 and 2018 reached 0.929 and 0.905, respectively, suggesting near-perfect agreement between PageRank rankings and official results. The lowest correlation occurred in 2019, with a Kendall's tau of 0.643.  
\begin{table}
\begin{tabular}{c c c} \hline
**Year** & **Kendall’s \(\boldsymbol{\tau}\)** & \(\boldsymbol{p}\)**-Value** \\ \hline
2013 & 0.786 ** & 0.0055 \\
2014 & 0.929 *** & \textless{}0.001 \\
2015 & 0.733 & 0.0556 \\
2016 & 0.810 * & 0.0107 \\
2017 & 0.786 ** & 0.0055 \\
2018 & 0.905 * & 0.0028 \\
2019 & 0.643 * & 0.0312 \\
2020 & 0.786 ** & 0.0055 \\
2021 & 0.714 * & 0.0141 \\
2022 & 0.714 * & 0.0141 \\
2023 & 0.810 * & 0.0107 \\ \hline \end{tabular} Note.: \({}^{*}\)\(p<\)0.05; **\({}^{*}\)\(p<\)0.01; *** \({}^{*}\)\(p<\)0.001.  
\end{table}
Table 1: Kendall’s Tau correlation and significance (2013–2023).  
Figure 2: PageRank heatmap by team and year.  
Overall, the consistently high Kendall's tau values demonstrate the reliability and validity of PageRank as an objective ranking method. In particular, the method proves to be useful in single-elimination formats where official rankings are not assigned beyond the top eight, offering a robust and interpretable estimate of relative team strength in the lower ranks as well.
# Holographic Mixed Reality System for Air Traffic Control and Management
## 2 Literature Review
The previously proposed pipeline for augmented reality systems overlaid the real windows with visual information through head-up displays [16]. Only a mathematical formulation was provided and no real-time or simulation-based experimental results were provided in the proposed work at that time. Later in 2018, under the RETINA concept, they validated the concept of superposing of a synthetic overlay on the outside tower view with the flight's information [21]. The concept proposed in this method was not applicable to view air traffic from remote locations in the case of bad weather conditions. Whereas, the authors method provided the entire view of the airfield in holographic mixed reality along with the important information tagged with each flight. In this way, our proposed method dealt with low visibility conditions as well as monitoring the air traffic from remote locations. Moreover, the method presented in [21] facilitated the controllers in low visibility conditions but did not reduce the overload for air traffic controllers to interact with multiple computer screens for air traffic management. Most of the research has only been done on the visualization of air traffic, but not the interaction mechanisms, whereas in our work, along with the improvement in the visualization of air traffic, air traffic controllers were enabled to easily switch between multiple interfaces and interact with them. Keeping in mind the limitations in existing systems, the authors proposed a mixed reality system to overcome these problems. The 3D information in our system was not confined to a 2D display or a static real-world location. Air traffic controllers could move around and view the 3D spatial information for better analysis of the air traffic situation. Moreover, by using this system, controllers could move the whole interface with them instead of sticking to a particular physical location. As the 3D information of air traffic was combined with the real world, our system provided 3D sound to give a better understanding of the location of an airplane with respect to the airport in mixed reality. Moreover, unlike other systems, our proposed system could be used remotely from any location and was not tied to the control tower. Therefore, in the case of bad weather conditions, our system enabled air traffic controllers to view air traffic even from remote locations. The multiple interfaces to view the air traffic in our system were provided within one device, so it required less effort to interact with multiple interfaces
## 3 System and Threat Models  
### Mix-Zone Deployment in VANETs Using Fake Beacons  
Figure 2 illustrates the system model for implementing fake beacon approaches in mix zones within VANETs at junctions. The mix zone, depicted by the red circle, is established by an mRSU through the periodic transmission of beacons. In VANETs, vehicles periodically broadcast beacons containing their state information \(x(t)\) at time \(t\), which consists of coordinates \((x(t),y(t))\) and speeds \((v_{x}(t),v_{y}(t))\). These beacons form a chronological sequence, representing the vehicle trajectory \(r\).  
As depicted in Figure 2, the solid black line represents the vehicle trajectory with a pseudonym called [PERSON], which enters the mix zone. After receiving the beacon from the mRSU, the vehicle requests a change in the pseudonyms, to ensure anonymity within the mix zone. Upon receiving a request to change the pseudonym, the mRSU performs a check to determine if there are enough vehicles within the mix zone to maintain k-anonymity. If there are at least k vehicles present, the mRSU directly applies the k-anonymity algorithm to change the pseudonym of each vehicle. However, if the number of vehicles is insufficient, the mRSU generates fake beacons for each entering vehicle and modifies the pseudonyms accordingly. The dotted black lines, representing all communications and beacons within the mix zone, are encrypted to ensure privacy.  
As illustrated in Figure 2, the vehicle's pseudonym is changed from [PERSON] to [PERSON], while two additional fake trajectories are generated to represent vehicles with pseudonyms [PERSON] and [PERSON], respectively. This design ensures that the attacker cannot distinguish the real trajectory of the vehicle among the three trajectories. By generating multiple fake trajectories, the system prioritizes the identity and location protection of individual vehicle identities.  
However, various studies have shown that attackers can identify real trajectories from fake trajectories by analyzing the semantic information of beacons, especially when the generated fake trajectories lack authenticity. To address this concern, researchers often generate a large number of fake beacons to deceive attackers, but this approach leads to a significant increase in transmission overhead. In light of this, the study aims to address the threat model associated with beacon semantic analysis. Subsequently, the CPCROK scheme will be presented in Section 4, which safeguards vehicle identity and location privacy without compromising the core functionality of the VANETs.
## 4 Numerical Results and Discussion
Figure 4 also shows the effect of the damping on the structural response of a corrugated nanoplate with \(l^{*}=0.5\), \(\mu=1.41\) nm\({}^{2}\), \(c=0.25L_{x}\), where the natural frequency of the structure increases proportionally with the viscoelastic damping. This increase is even more pronounced for increasing wave magnitudes \(F^{*}\). This means that the geometry of corrugations can affect the response of the nanostructure significantly, especially for higher values of viscosity. Thus, the exact geometry of wrinkles and imperfections resulting from a manufacturing process must carefully account for the structural study of a hyper-viscoelastic nanoplate. Another parametric investigation considers the combined effect of the dimensionless length scale \(l^{*}\) and wave amplitude \(F^{*}\), for a corrugated nanoplate with 5 corrugations (i.e., for \(c=0.1L_{x}\)), while keeping fixed the non-locality \(\mu=1.41\) nm\({}^{2}\) and the viscosity \(g=2\) Ns/m\({}^{2}\). This is shown in Figure 5, whose curves translate upwards constantly as the length scale parameter increases. Furthermore, the natural frequency increases monotonically as the dimensionless wave magnitude \(F^{*}\) is increased, independently of the length scale \(l^{*}\). The results have been suitably repeated for a different number of corrugations, which are not reported here for the sake of brevity.
## 4 Structural Analysis of the APTS' L-Shaped Bracket Structure  
To provide displacement and stress constraint values for the topology-optimized design and compare its performance with the original reinforced bracket structure of the APTS, this Section outlines the loading and boundary conditions of the original L-shapedbracket structure. It then conducts displacement and stress analysis on the structure. The bracket structure for an APTS must withstand various loading and boundary conditions to ensure stability and functionality. The loading conditions include static loads, such as the self-weight of the bracket and mounted equipment; dynamic loads from operational movements and vibrations; and environmental loads, such as temperature changes. Boundary conditions involve fixed supports that prevent translations and rotations, pinned supports that allow rotations, and sliding supports that accommodate thermal expansions.
## 2 Methodology
### Experiments  
The experiments that serve as training and validation data for the NN are summarized in Table 1, with a total of 34. The selection of these experiments was based on the need to cover a wide range of operating conditions and ensure that the NN can learn patterns that generalize well to different scenarios. The configurations are designed to reflect a variety of process variables, such as concentration, filter plate number, end pressure, and filter medium cycles, which are expected to significantly influence the filtration process. By varying these parameters, we ensure that the network is exposed to a comprehensive set of inputs and can develop a robust understanding of the system's behaviour.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Concentration [g/L]** & **Filter Plate Number** & **End Pressure [bar]** & **Cycles** & **Frequency** \\ \hline
6.25 & 2 & 2.0 & 34 & 1 \\ \hline
6.25 & 2 & 4.0 & 32 & 1 \\ \hline
6.25 & 2 & 5.0 & 31 & 1 \\ \hline
6.25 & 2 & 6.0 & 30 & 1 \\ \hline
6.25 & 2 & 8.0 & 29 & 1 \\ \hline
12.50 & 1 & 10 & 4 & 1 \\ \hline
12.50 & 2 & 10.0 &
\begin{tabular}{c} 2, 4, 5, 6, 7, 14, \\ 23, 35, 36 \\ \end{tabular} & 9 \\ \hline
12.50 & 2 & 7.0 & 5 & 1 \\ \hline
12.50 & 2 & 8.0 & 6 & 1 \\ \hline
12.50 & 2 & 0.2 & 1 & 1 \\ \hline
12.50 & 2 & 0.5 & 10,11 & 2 \\ \hline
12.50 & 2 & 0.7 & 12,13 & 2 \\ \hline
12.50 & 3 & 10 & 1,2,3 & 3 \\ \hline
25.00 & 2 & 10.0 & 24 & 1 \\ \hline
25.00 & 2 & 5.0 & 18 & 1 \\ \hline \end{tabular}
\end{table}
Table 1: Available training and validation data from experiments.  
Figure 3: Integration of augmented reality (AR) with the digital twin (DT) framework [31]. This figure shows a virtual 3D model of a chamber filter press overlaid onto its physical counterpart using AR. At the current stage, the AR system primarily supports visualization of the geometry and structure of the filter press. It can assist operators by highlighting specific components—such as individual filter chambers—and providing interactive maintenance instructions, for example, indicating how to access or remove parts. This lays the foundation for future functionality, such as real-time diagnostics or condition monitoring.