esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Ecological Hazard Assessment  
According to the measured data and Equations (1) and (2), corresponding values (the P\({}_{1}\) and \(P_{N}\)) can be obtained. The single pollution index range of each pollutant is 0.3-0.6 for P\({}_{\rm Cr}\), 0.3-1.5 for P\({}_{\rm Ni}\), 0.4-4.2 for P\({}_{\rm Cu}\), 0.7-32.5 for P\({}_{\rm Pb}\), 2.0-44.9 for P\({}_{\rm Zn}\), 0.5-17.4 for P\({}_{\rm As}\), and 3.3-258.3 for P\({}_{\rm Cd}\). The level of P\({}_{1}\) in the farmland soils around the Dachang mining area can be ordered as: P\({}_{\rm Cd}\) (54.7) \(>\) P\({}_{\rm Zn}\) (12.1) \(>\) P\({}_{\rm Pb}\) (8.1) \(>\) P\({}_{\rm As}\) (7.7) \(>\) P\({}_{\rm Cu}\) (1.7) \(>\) P\({}_{\rm Ni}\) (0.6) \(>\) P\({}_{\rm Cr}\) (0.4). In the whole study area, the soil is not polluted by Cr and Ni, is slightly polluted by Cu, and is extremely polluted by the other four elements (Cd, Zn, Pb, and As) [18; 19]. The Nemerov integrated pollution index (\(P_{N}\)) is 39.6, and the pollution level is extremely polluted. Notably, Cd contributes the most to the comprehensive pollution index, with a contribution rate ranging from 33% to 89%, and its contribution rate is as high as 68% in the whole study area.  
According to Equation (3), the PERI of soil samples can be calculated, and the results are as follows. The level of E\({}_{1}\) heavy metals in the farmland soils around the Dachang mining area can be ordered as: E\({}_{\rm Cd}\) (1842.6) \(>\) E\({}_{\rm Pb}\) (152.1) \(>\) E\({}_{\rm As}\) (150.0) \(>\) E\({}_{\rm Zn}\) (12.1) \(>\) E\({}_{\rm Cu}\) (1.7) \(>\) E\({}_{\rm Ni}\) (0.6) \(>\) E\({}_{\rm Cr}\) (0.4). The risk of Cd is the most significant, which belongs to the extremely strong ecological hazard level, followed by As and Pb, which belong to the relatively strong ecological hazard level. Zn, Cu, Ni, and Cr belong to the slight ecological risk level. The potential ecological hazard index (\(RI\)) of the study area is 2196.9, which belongs to the extremely strong ecological hazard level. The contribution rate of Cd is as high as 84%, which is the main pollution element. This result is highly consistent with the evaluation result of the Nemerov integrated pollution index.
# Adaptive Cruise Control for Cut-In Scenarios Based on Model Predictive Control Algorithm
## 3 The Cut-In Vehicle Safety Distance Model
### The Safe Distance Model of the Cut-In Vehicle for Curve Lane
#### 3.2.2 The Minimum Longitudinal Safe Distance between Cut-in Vehicle L and Host Vehicle H  
As shown in Figure 18 below. Cut-in vehicle L changes lanes from the outer lane to the inner lane. The dashed-line vehicles are the positions of the two vehicles at the initial time, and the solid-line vehicles are the positions of the two vehicles at the possible collision time. The arc length from the cut-in vehicle along the outer lane to the host vehicle at the initial moment is \(l_{1}(0)\), arc length from host vehicle H along the inner lane to cut-in vehicle L at the initial moment is \(l_{2}(0)\), and the minimum safe distance between the cut-in vehicle L and host vehicle H at the initial moment is assumed to be \(MSD(L,H)\).  
In order to make it so that cut-in vehicle L does not collide with host vehicle H during the lane change process, the following equation needs to be satisfied:  
\[S_{r}(t)=\left(\begin{array}{c}l_{2}(0)+\int_{0}^{t}\int_{0}^{\lambda}(a_{L }(\tau)-a_{H}(\tau))d\tau d\lambda+\\ (v_{L}(0)-v_{H}(0))\times t-l_{0}\end{array}\right)>0\quad\forall t\in\left[t _{c}+t_{adj},T\right] \tag{26}\]  
According to the geometric relationship, the calculation formula of \(l_{1}(0)\) and \(l_{2}(0)\) is:  
\[l_{2}(0)=\frac{l_{1}(0)}{R}(R-D) \tag{27}\]  
During the lane change process, the minimum safe distance between the cut-in vehicle and the host vehicle along the inner lane is:  
\[l_{2}(0)=Max\left[\int_{0}^{t}\int_{0}^{\lambda}\Delta a_{H-L}d\tau d\lambda +\Delta v_{H-L}(0)\times t+l_{0}\right] \tag{28}\]  
According to the formula of the law of cosines, the minimum safe distance \(MSD(L,H)\) between cut-in vehicle L and host vehicle H at the initial moment of lane change is:  
\[MSD(L,H)=\begin{cases}\left(R^{2}+\left(R-D\right)^{2}-2R(R-D)\cos\varphi \right)^{1/2},&l_{2}(0)>0\\ -\left(R^{2}+\left(R-D\right)^{2}-2R(R-D)\cos\varphi\right)^{1/2},&l_{2}(0)<0 \end{cases} \tag{29}\]  
where \(\varphi\) is the central angle corresponding to \(l_{2}(0)\), which is obtained using the following equation:  
\[\varphi=\frac{l_{2}(0)}{R-D} \tag{30}\]  
Figure 18: The positions of cut-in vehicle L and host vehicle H in the curved lane.
## 1 Introduction
Furthermore, the surface current, radiated beam patterns, and average gain were analyzed by using the high-frequency structure simulator (HFSS) and computer simulation technology (CST) programs. The effects on the beam patterns radiated from a monopole antenna, based on the number of closed-loop devices, i.e., one, two, and four, were also analyzed. The designed monopole WAVE antennas with closed-loop devices were implemented and their characteristics were compared with the simulation results.
## 3 Results and Discussion
### Color Analysis
Regarding color saturation (chroma, C*), statistical analysis revealed significant differences among all samples (\(p\) < 0.05). The control cheese (SHGC-C) exhibited the highest chroma value (26.76), representing the most vivid and pure coloration due to its greater distance from the origin in the (\(a^{\star}\), \(b^{\star}\)) coordinate space [91]. The addition of red and green seaweed pigments reduced chroma by approximately 8-10 units, leading to a more heterogeneous color distribution in the supplemented products. Both supplemented cheeses (SHGC-PP and SHGC-U) exhibited \(\Delta E\) values significantly higher than 3.0, the commonly accepted threshold for perceptible color differences to the human eye [92]. Specifically, SHGC-PP showed the most pronounced deviation (\(\Delta E\) = 13.86 \(\pm\) 0.84), reflecting substantial changes in all three dimensions of color (\(L^{\star}\), \(a^{\star}\), \(b^{\star}\)). This suggests a visually intense transformation caused by the incorporation of _Palmaria palmata_, which is known to impart red pigments such as R-PE [84]. SHGC-U presented a \(\Delta E\) of 9.26 \(\pm\) 0.50, also well above the perceptibility threshold. Statistical analysis revealed significant differences between the SHGC-PP and SHGC-U samples, indicating that the type of seaweed incorporated had a distinct impact on the color variation. Although the change was less intense than that observed with _Palmaria palmata_, the incorporation of _Ulva_ sp. still led to a visible color shift toward a greener hue, associated with the presence of chlorophylls from the seaweed [87]. These results confirm that both seaweeds significantly alter the visual appearance of the cheeses, making them distinguishable from the control. Such variations may influence consumer perception, acceptance, or expectations, depending on market familiarity with algae-enriched products. Overall, all samples demonstrated medium to high lightness (\(L^{*}\)), with yellownness (\(b^{*}\)) dominating over redness (\(a^{*}\)), reinforcing the characteristic white-yellowish hue, particularly in the control cheese. These findings align with previous studies [89; 93], where the pronounced whiteness of goat cheese was attributed to the species' ability to convert \(\beta\)-carotene entirely into vitamin A, coupled with the smaller fat globule size in goat's milk compared to cow's milk (3.49 vs. 4.55 \(\mu\)m) [81; 94]. Although most studies adopt four to six readings per sample for instrumental color evaluation [92], future research would benefit from increasing the number of readings to enhance randomization, improve repeatability, and ensure more reliable results.
###### Abstract  
Three-dimensional (3D) visualization technology, such as augmented reality (AR), has served as the display for building information modeling (BIM)-based architectural design collaboration to provide more effective design observation and communication for stakeholders. That said, AR has several technical limitations in terms of personal device issues, user experience, and visualization quality. A new form of AR called spatial augmented reality (SAR) has been introduced to address these issues, which uses a digital projector to present graphics on physical objects for augmenting real-world objects. Therefore, SAR has great benefits and potentials to combine with BIM for design collaboration. This paper introduces a BIM-based SAR operational framework, where 3D building models generated from BIM software are imported to projection mapping tools to display building surface textures on physical white building models. A case study using Revit and 3 ds Max as the BIM software, and MadMapper as the projection mapping tool, was conducted to demonstrate the feasibility of the proposed framework and to evaluate the projection performance of SAR. The case study showed that the texture of BIM models could be projected on the objects clearly and realistically. Additionally, the proposed SAR method potentially offers intuitive observation of building models and comfortable wear-free experience for collaborative design, and the qualitative analysis by changing the parameters was conducted to test the different projection conditions. Since it is expected that the use of SAR can be promoted by overcoming the discussed technical limitations and possible solution application, this study aims to traceability provide the whole process of BIM-based SAR for architectural design collaboration.  
spatial augmented reality; building information modelling; design collaboration; architectural design; projection mapping +
Footnote †: journal: Applied Sciences
# Risk-Based Selection of Inspection Method for External Post-Tensioning System of Bridges
## 5 Summary and Conclusions  
Post-tensioning elements play a critical role in the structural integrity of post-tensioned bridges and their inspection is important for the maintenance of these bridges. However, the selection of inspection method for a condition rating of these elements has been rather subjective, relying mostly on the experience of the inspectors. A unified and reliable risk-based selection method is, however, lacking. Such risk-based methods should rely on the success and accuracy of the inspection method for detecting defects and risk/consequence for lower accuracy or false negative results. Towards addressing this knowledge gap, a study was conducted exploring practical means and a procedure for the risk-based selection of the inspection method. Since corrosion is the major deteriorating factor for post-tensioning elements, this study focused on inspection methods capable of detecting corrosion of the main tension steel elements. Twenty-nine NDE methods were reviewed and classified under nine main categories for their applicability and corresponding accuracy. A risk-based selection of the inspection method (RBSIM) was developed based on NSBI condition rating scales and the accuracy levels for NDE methods obtained from the literature. For calculating risk, the consequence of a false negative inspection was equated to bridge failure. To demonstrate the process, the proposed RBSIM framework was used to select the inspection method for a case study bridge. Among ten available NDE methods, a two-stage inspection method was selected because of its minimum risk calculated by the procedure. The recommended method was implemented successfully for the bridge of the case study identifying damage and defects requiring remedial actions recommended to the bridge owners.  
Conceptualization, M.T. and A.B.M.; methodology, M.T. and A.B.M.; formal analysis, M.T. and A.B.M.; resources, M.T. and A.B.M.; writing--original draft preparation, M.T.; writing--review and editing, M.T. and A.B.M.; supervision, A.B.M. 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 available on request from the corresponding author.  
The authors declare that they have no known competing financial interest or personal relationships that could have appeared to influence the work reported in this paper.  
Figure 15: Activities during the second stage of two-stage inspection. (**a**) application of video-borescope, (**b**) a sample of steel photo captured by borescope.
## 4 System Model
### Linear Topology  
In linear topology, a linear wireless network of \(\mathcal{N}\) ICN nodes is considered. As shown in Figure 4, the nodes are connected to the server in a multi-hop manner. The server (access point) is assumed to be equipped with content catalog \(\mathcal{C}\). The buffers \(\mathcal{B}\) of all the nodes are considered to be of same size. The nodes act both as a content requester and provider (relaying nodes).
## 2 Background
### Petri Nets (PN)  
A PN [11] is a graphical formalism used for modeling the behavior of systems. It is a bipartite graph composed of two types of nodes, which are linked using weighted arcs. Nodes are places and transitions represented as circles and rectangles, respectively. Places can contain a finite number of tokens.  
Definition 1: Petri Net (PN) A PN is a tuple \(\Gamma=(P,L,F)\), where (i) \(P=(P_{0},P_{1}\cdots P_{m})\) is a finite ordered set of places; (ii) \(T=(T_{0},T_{1}\cdots T_{n})\) is a finite set of transitions; (iii) \(F:(P\times T)\cup(T\times P)\longrightarrow\{0,1,2,\cdots\}\) is a set of directed weighted arcs that represent flow relations.  
Figure 1: The sequential execution of the grading example.  
A marking \(m\) denotes the current state that is represented by tokens in each place of \(P\). For instance, let \(P=\{P_{0},P_{1}\}\) be a set of places, and \(m=[0,2]\) denotes that \(P_{0}\) and \(P_{1}\) contain 0 and 2 tokens, respectively. In this paper, we represent this marking using \(m=\{P_{1},P_{1}\}\).  
A transition \(T_{l}\) is enabled if and only if all its input places \(P\in(P_{in}\times T_{l})\longrightarrow j\) contain a sufficient number of tokens, \(j\). Firing \(T_{l}\), denoted as \(m\xrightarrow{\Gamma,T_{l}}m^{\prime}\), consumes and produces new tokens. Firing \(T_{l}\) consumes the corresponding \(j\) tokens from each input place and produces new tokens in the out places of \(T_{l}\), respectively. The number of tokens produced is the weight of the arc \((T_{l}\times P_{out})\longrightarrow k\). This firing leads to a new marking \(m^{\prime}\) including the new tokens of every output place \(\{P_{out}^{1},P_{out}^{2},\ldots P_{out}^{k}\}\).  
Example 1: Figure 2 depicts the initial structure of the grading example. Each arc has a weight equal to one.
# Half-Elliptical Resonator Lowpass Filter with a Wide Stopband for Low Band 5G Communication Systems
## 2 LPF Design Process  
The design procedure of the proposed low pass filter is illustrated in Figure 1 through 7 steps. The proposed LPF is designed based on the half-elliptical shaped resonator. In the first three steps, the half-elliptical shaped resonator, its corresponding LC equivalent (LCE) model and the equation of its created transmission zero are obtained. In step 1, an LC equivalent circuit with the desired response is presented. The proposed LC model creates a transmission zero at the desired frequency. In step 2, with Equation (14), the location of the transmission zero can be obtained analytically. In step 3, the realization of the proposed half-elliptical shaped resonator is presented with a microstrip line. In step 4, the validity of these three steps is investigated. If the EM simulation of the extracted microstrip resonator and schematic simulation of the LC model are in good agreement, this step passes. In step 5, another two half-elliptical shaped resonators are designed in a similar way. In step 6, two suppressing cell resonators are designed. In step 7, finally, the proposed low pass filter is shaped based on the resonators designed in the previous steps.  
In this design, firstly, dual half-elliptical resonators are used to achieve a sharp transition band, and meandered lines are used for size reduction. Secondly, a half-elliptical resonator is used to generate a transmission zero to achieve a wider stopband. At the end, two suppression cells are added to attain a wider stopband.
# Extraction and Detection of Surface Defects in Particleboards by Tracking Moving Targets
## 1 Introduction
When applied to the detection of surface defects in particleboards, a large number of small wood chips on the surface of the board may cause serious false detection. Reference [15] proposed a method based on thresholding segmentation to detect surface defects in a glass substrate. A straight-line intercept histogram was established directly from the two-dimensional information of an image, and the Otsu criterion was then used to find the best intercept threshold from the one-dimensional histogram. However, this method can only detect the surface of the glass substrate by taking photos under the static state, and it cannot realize dynamic detection on the production line. Through the study of existing technologies, the aim of this paper is to examine the problem of detecting surface defects in particleboards. In the whole production process of particleboards, the board is always in motion in the production line, and the surface defects are mostly exposed shaving defects. In view of this situation, we adopted the moving target tracking technology and the image segmentation technology to achieve image capture, extraction, and detection of surface defects in particleboards, including defects of different sizes and shaving defects with different depths. The main contributions of this study can be summarized as follows:  
(1) The KCF moving target tracking algorithm was used to track surface defects in particleboards in the production line, and the forward-backward error was introduced to reduce the tracking error using the median flow method. (2) The motion targets of each frame in video sequence were extracted using the edge detection method to obtain more complete and accurate characteristic data of the moving target. (3) According to the extracted defect images, various defect characteristic parameters were calculated, and the surface defect level of the particleboard was evaluated using the fuzzy pattern recognition method. The rest of the paper is organized as follows. Section 2 introduces the KCF target tracking algorithm, which was used to capture the surface defects in the particleboard. Section 3 introduces the samples and equipment used in the experiment. In Section 4, the tracking and detection results of the surface defects are given. Section 5 gives the calculation of the defect characteristic parameters according to the extracted defect images. The defects are also analyzed using fuzzy pattern recognition. Finally, the conclusions of the work are drawn in Section 6.
# Modelling and Analysis of Hybrid Transformation for Lossless Big Medical Image Compression
## 4 Simulation and Analysis  
The overall methodology of this research includes KT steganography, DWTs, and lossless wavelet packet compression of medical images for privacy, preservation, and efficient storage. The steganography technique uses the LSB replacement technique, which replaces LSB bits of pixels with patient data in binary form.
## 3 Results  
The geographical map (Figure 3) delineates Sen's slope estimations of the MSAVI across the study area, spanning from 1984 to 2020. We categorized the maps into negative and positive trends in value per year, offering insights into the dynamics of vegetation growth and production throughout the specified period (Table 1).  
During the months of JJA, Sen's slope for the MSAVI exhibited a range of \(-0.47\) to \(0.75\) per year. This range indicated fluctuation in the MSAVI trend over the summer months. While the average value of \(-0.00011\) per year suggests a relatively horizontal pattern, significant variations are evident. The standard deviation (SD) of \(0.0056\) per year indicates a modest level of variability around the average trend. During the SON period, Sen's slope varied from \(-0.29\) to \(0.4\), indicating comparatively lower trend variability. The average value of \(0.0012\) per year suggested a slight upward trend in the MSAVI during the fall months, with a very small amount of variation around the average trend, as indicated by the SD of \(0.0032\). For the period MAM, Sen's slope ranged from \(-0.37\) to \(0.27\) per year, indicating a significant level of heterogeneity in the trend of the MSAVI over the spring months. The average value of \(0.00023\) per year suggests a virtually constant pattern akin to that observed during the summer months. The SD of \(0.0047\) per year indicates a modest level of variability around the average trend. Similarly, during the DJF period, Sen's slope ranged from \(-0.37\) to \(0.26\) per year, highlighting fluctuations in the trajectory of the MSAVI over the winter months. The average value of \(0.000225\) per year indicated a constant pattern resembling that of the spring and summer months. The SD of \(0.0047\) per year indicated a modest level of variability around the average trend. These data offer crucial insights  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Months** & **Min** & **Mean** & **Max** & **SD** \\ \hline JJA & \(-0.47\) & \(-0.00011\) & \(0.75\) & \(0.0056\) \\ \hline SON & \(-0.29\) & \(0.0012\) & \(0.4\) & \(0.0032\) \\ \hline MAM & \(-0.37\) & \(0.00023\) & \(0.27\) & \(0.0047\) \\ \hline DJF & \(-0.37\) & \(0.000225\) & \(0.26\) & \(0.0047\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The statistics of Sen’s slope for MSAVI raster.  
into seasonal fluctuations and patterns in the MSAVI, contributing to our understanding of changes in vegetation and the overall health of the ecosystem over time.
## 5 Algorithm That Determines Singular Points Generation
### Singular Points Density Analysis  
Due to its structure, a 3D chart is largely unclear. In addition, demands for the number of executed cycles grow. Since the mechanisms are of a radial symmetry around the \(z\)-axis, suitable rendering is that of the workspace diagonal section (Figure 8b). By making use of the Pythagorean theorem, the spatial points lying outside the section plane may be transferred inside the section plane. Rendering inside the workspace section requires fewer simulations to be generated. Such a section shows--beyond doubt--which areas are more singularity-prone. It goes without saying that the computational system would also work without transition in the cross-section. It would just take substantially longer. The majority of the mechanisms examined have radial symmetry.  
The mutual configuration of the points has been obtained by comparing the critical distance of the singular and the non-singular point. If this distance is smaller in the analysis than the established critical value, then the workspace point in question may have both a singular and a non-singular configuration (Table 1). The analysis of a large number of points has shown that in all mechanisms each singular point has its non-singular counterpart. In other words, a singular point in the workspace may be approached in theory also through an infinitesimally close non-singular configuration of the robot's arms (Figure 8c). However, this fact does not mean that these points can be attained through a smooth movement of robotic arms.  
A greater informative value of the analysis is obtained from the density of the singular points' distribution in space (Figure 10).  
Figure 10: Singular points generation in the robot’s workspace, 50,000 samples, \(\det{\bf|}\)\({\bf|}\) = 0.01. (**a**) Singular points of the Kuka robot; (**b**) singular points of the URM robot.  
The Kuka robot displays fewer singular points (6575) at the selected number of samples compared to the URM (22450) robot. The point distribution in the case of Kuka robot is closer to the \(z\)-axis. In terms of programming, this distribution is more beneficial than an even points distribution in the URM robot's case.