esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Analysis
### Acceleration Response of Tunnel Lining
Under either left incidence or right incidence, the horizontal and vertical acceleration responses of the 60-150 m tunnel interval (i.e., the tunnel interval near the rock mountain) are noticeably higher than those of other intervals. In other words, the acceleration response of the tunnel in the plain-mountain site is greater than that in the plain site. As stated in Section 4.1, the wave field of the soil site located on the right side of the mountain for right incidence is remarkably complex due to the total reflection at the soil-rock interface and surface reflection, and this results in the fact that the tunnel's lining may be subjected to multiple waves, which aggregate the vibration of the lining. Similarly, for left incidence, the transmitted SV- and P-waves produced by the left-side-incident SV-wave at the soil-rock interface possess different propagation and vibration directions from the incident wave that comes from the boundary beneath the tunnel (see Figures 12 and 13), and, thus, the superposition of these waves may aggregate the vibration of the tunnel's lining. However, in the plain-soil site, the site conditions are uniform, and no total reflection or refraction occur. Therefore, compared to the tunnel in the plain site, the tunnel interval adjacent to the rock mountain may be subjected to multiple waves possessing different directions of propagation and vibration due to obliquely incident wave and non-uniform subsurface geometry conditions, and, thus, the acceleration response of the tunnel in the 60-150 m range is higher. Moreover, the horizontal and vertical acceleration responses of the arch vault, arch bottom, left arch, and right arch of the 60-150 m tunnel interval under left-side incidence are lower than those under right-side incidence. This is because multiple reflections occur at the site's surface and soil-rock interface in the cases with right-side incidence, and,  
Figure 13: Diagram of the wave field for (**a**) left-side-incident SV-wave and (**b**) right-side-incident SV-wave with an incidence angle = 33.69\({}^{\circ}\) in the plain-mountain site. thus, multiple conversions of waves may occur to a great extent. Therefore, the complex superposition of waves induces the aggravation of the lining's acceleration response. Regarding left-side incidence, the propagation direction of the transmitted and reflected waves generally points to the lateral model boundary, and, thus, it is easier for the waves to run outside of the site, meaning that the degree of multiple reflections is lower.
## 3 Results and Discussion
### Differential Scanning Calorimetry of Margarine  
Figure 1A shows the melting profile of 10% beeswax oleogel (BWO), the control margarine, and sample 5 (replacement of 100% (PHPO) and 25% (PO) with BWO). The melting thermogram of hydrocolloid-based oleogel (HBO), the control margarine, and sample 4 (replacement of 100% (PHPO) with HBO) are shown in Figure 1B. In the industry, thermal profile determination is essential to control properties such as spreadability and thermal stability, especially at room temperature. The melting point of a fat or oil can be affected by the length of the FA chain (increasing the chain length leads to an increase in melting point), degree of unsaturation (increasing the degree of unsaturation leads to a decrease in melting point), the presence of TFA (TFA have higher melting point than the cis form) and polymorphisms (\(\alpha\)--lowest melting point, \(\beta\)'--medium melting point and \(\beta\)--highest melting point) [30]. The control sample had two melting peaks in the melting range of 27 to 37 \({}^{\circ}\)C, revealing the fat content separated into two parts. The high melting point component consists of saturated triacylglycerglycerols melted at higher temperatures and is the final peak. The soft component contains unsaturated fatty acids with a low melting point and is responsible for the first melting peak. This is in line with the composition of margarine, which contains palm oil, partially hydrogenated palm olein oil, and sunflower oil. Beeswax oleogel had multiple melting peaks and a melting range of 48 to 63 \({}^{\circ}\)C. As shown in the figure, with increasing replacement, the melting point, and melting range increased, so that sample 5 (100% (PHPO) and 25% (PO) with oleogel) was similar to the control in terms of rheological and textural characteristics and had a melting range of 26-50 \({}^{\circ}\)C. The wide melting range of this sample can be attributed to the heterogeneity of the compounds in margarine because the different compounds present (triacylglycerglycerols of palm and partially hydrogenated palm olein oils, and fatty esters, \(n\)-alkanes, free fatty acids, and free fatty alcohols in beeswax) have different melting points. Other studies have reported similar results [14]. As shown in Figure 1B, the oleogel had a melting peak at 210 \({}^{\circ}\)C, and no peak of thermal degradation was observed. These results were similar to other studies that reported thermal stability and high melting points of hydrocolloid-based oleogels [31,32]. Furthermore, it was observed in Sample 4, which was similar to the control in terms of rheological and textural properties, that the melting point was transferred to high temperatures by increasing the amount of hydrocolloid-based oleogel in the margarine.
## 3 Methods
### Canopy Closure Profile Generation  
Canopy closure is always calculated as the ratio of canopy return energy to total return energy using Equation (1). Based on the return energy profile, the canopy closure at height \(z\) was calculated as the ratio of cumulative canopy return energy from the canopy top to height \(z\) to the total return energy, as shown in Equation (2). Then, the canopy closure profile was generated. To eliminate the difference derived from the different reflectance from the ground and the canopy, the reflectance ratio between the vegetation and the ground was considered when calculating the canopy closure profile.  
\[f_{\text{cover}}=\frac{E_{v}}{E_{v}+E_{g}}, \tag{1}\]  
\[f_{\text{cover}}(z)=\frac{E_{v}(z)}{E_{v}(0)+\frac{\rho_{v}}{\rho_{g}}E_{g}}, \tag{2}\]  
where \(f_{\text{cover}}\) represents the canopy cover of the whole canopy;\(f_{\text{cover}}(z)\) represents the canopy cover from the canopy top to height \(z\); \(E_{v}\) and \(E_{g}\) are the whole canopy return energy and ground return energy, respectively; \(E_{v}(z)\) and \(E_{v}(0)\) are the canopy return energies from the canopy top to height \(z\) and from the canopy top to height \(0\), respectively; and \(\rho_{v}\) and \(\rho_{g}\) are the reflectance of the vegetation and the ground, respectively.
# Marble and Glass Waste Powder in Cement Mortar
## 3 Results and Discussion
### Mechanical Properties of Mortar
#### 3.2.3. UPV  
The UPV test, conducted in accordance with ASTM C597-09 [33], assess the uniformity, relative quality, and durability of mortar. Figure 8 presents the UPV results for mortar samples with partial MGWP replacement, evaluated at curing ages of 3, 7, 28, 56, and 91 days.  
The UPV values progressively increased with curing age, ranging from 2.53 to 2.96 km/sec at 28 days to 3.06-4.25 km/sec at 91 days. At 10% MGWP replacement, the UPV results were 18.5% higher at 28 days compared to the 7-day results and 7.8% higher at 56 days relative to the 28-day values. The rate of increase in UPV values diminished after 28 days.  
According to the IS 13311 (Part 1): 1992 [57], mortar incorporating MGWP was rated as \"good\" at 10% replacement and \"medium\" at 15% replacement at 28 days. Significant improvements in quality were observed at 56 and 91 days, with the highest UPV value achieved at 10% MGWP replacement. This result aligns with the highest bulk density and compressive strength values, as well as reduced porosity and water absorption in these mixes. The improved UPV performance is attributed to the additional C-S-H gel formation  
Figure 8: Effect of MGWP on UPV of mortar.  
Figure 7: Bulk density of mortar with various replacement levels of MGWP.  
from the pozzolanic reaction of GWP, combined with the filler effect of MGWP, which reduces air voids and densities the matrix [58]. These factors enhance the durability and mechanical properties of the mortar, as supported by previous research [55].
## 6 Results and Discussion
### Discussion  
J1, as listed in Table 3, reveals that the maximum displacement reduction effect of the ISAMD was better than that of the TMD. The maximum displacement responses of the roof were further reduced by about 4-10%. The root mean square of the displacement reduction effect of the ISAMD was greater than that of the TMD, leading to a reduction of 8-10%. Conversely, the shock absorption effect of the structural acceleration responses of the building with the ISAMD was no better than that of the TMD. However, the maximum displacement response of the control mass block was significantly less than that of the TMD, so the required installation space of the ISAMD is 2-4 times that of the TMD. The comparison between the structural responses of the building with the TMD and the bare structure shown in Table 3 and Figure 6 indicates that the frequency ratio of the optimal control performance indices of the building with TMD was around 0.95. When the frequency ratio was slightly offset, the control performance indices J1, J3 and J5 increased. For example, when the frequency ratio was 1.2, J1, J3 and J5 were 0.83, 0.79 and 0.94, respectively. The shock absorption effect was very limited, manifesting the so-called detuning effect. In contrast, the relationship of the control performance indices to the frequency ratio, as presented in Table 2 and Figures 6-11, revealed that the minimum values of J1 and J3 occurred on a very flat curve. The frequency ratio was almost constant in this range. That is, the sensitivities of the control performance and frequency ratio were extremely low. Basically, this phenomenon could be expected because the maximum roof displacement reaction and the root mean square displacement of the building with the ISAMD had reduction rates of more than 30% and 60%, respectively. The control mass block displacement of the ISAMD was 2-4 times that of the TMD, as shown in Figures 9d, 10f, 11d, 12f and 13d. This suggests that the installation space of the ISAMD must be large. In fact, the higher ISAMD frequency of the control mass could provide the benefit of reducing the installation space, such as the simple pendulum TMD of the Taipei 101 building. It has a 6.8 s cycle, which requires a pendulum length of 11.48 m, occupying a space of four stories. Conversely, if the frequency ratio of the ISAMD is 3 with a 2.26 s cycle, the pendulum length only needs to be 1.27 m. It can be set up in one story. Therefore, the space requirement of the ISAMD may not be greater than that of the TMD.
# Publish/Subscribe-Middleware-Based Intelligent Transportation Systems: Applications and Challenges
## 3 Background of Middleware
### Publish/Subscribe Middleware
#### 3.1.2 Publish/Subscribe-Middleware-Based ITS  
Middleware plays a crucial role in ITS by enabling seamless communication, real-time data exchange, and efficient event-driven processing across various transportation infrastructures. Integrating middleware solutions to ITS becomes essential for ensuring interoperability among heterogeneous systems, optimizing decision making, and enhancing overall system performance. Middleware facilitates data distribution in traffic surveillance, vehicle-to-everything (V2X) communication, Smart Infrastructure, and transportation logistics, providing a scalable and adaptable framework for intelligent mobility solutions.  
Figure 7 illustrates how publish/subscribe middleware connects the different views in the ARC-IT framework shown in Figure 1. Each node (Node 1, Node 2, up to Node n) represents a distinct infrastructure element within the ITS, such as sensors, vehicles, traffic lights, Roadside Units (RSUs), or surveillance cameras. These nodes have individual Physical and Communications Views, which may vary based on vendor implementations. The middleware facilitates inter-node communication by interfacing with the transport layer of the Communications View, thereby enabling interoperability across heterogeneous Physical Views. Also, the Enterprise and Functional Views interact with the Physical and Communications Views through the middleware, enabling unified data exchange and service coordination. This architecture simplifies system deployment, as developers do not need to ensure direct compatibility between new and existing components. The middleware also embeds essential services such as QoS and security mechanisms to support reliable, scalable, and secure communication within ITS.
## 7 System Performance Analysis  
To validate the system's performance, a comparative analysis was conducted on model loading times and response times for driving commands. The comparison was made against the virtual simulation and communication software of established industrial robotics companies, KUKA and ABB. The robot models used for testing were the KUKA KR150 and ABB IRB2400, with Benchmarking Scripts employed as the testing tools.  
Figure 11: Real-time data reception from robot diagram.
### Related Work
The authors provide an overview of ISAC and propose various solutions to optimize S&C performance. Specifically, UAV motion control, wireless resource allocation, and interference management for ISAC systems are used for one or more UAVs. Two application scenarios to exploit the synergy between S&C are sensor-enabled UAV communications and communication-enabled UAV sensors. Another recent study addressing machine learning (ML) techniques to support UAV networks is presented in [24]. The adaptive nature of UAV networks along with their context-sensitive perception makes UAVs a suitable candidate for future communication technologies. With self-adaptive techniques, ML is considered one of the best technologies to support UAV networks in achieving their communication goals and reliability. The authors highlighted four key components of UAV communication operations where ML can make significant contributions. These include feature perception and extraction, feature interpretation and reconstruction, trajectory and mission planning, and aerodynamic control and operation. The survey classifies the latest popular ML tools based on their applications to the above four components and conducts a gap analysis. It is shown that different ML techniques are dominating the applications for the four key modules of UAV communication operations. Various studies have focused on the perspective of UAV communication between ground and airborne stations. It is believed that the use of mobile edge computing (MEC) in parallel with UAV networks will make it more reliable and efficient for future communication operations. Table 1 briefly provides both the scope and the features of UAVs covered in this paper. All the details are provided in the following sections.
## 5 Machine Learning Principles Based on Predictive Methods
### Cubic Spline Interpolation  
Cubic spline interpolation aims to provide accurate approximations with low-degree polynomials when the number of data points is large to avoid oscillations and excessive fluctuations in the interval. This is achieved by dividing the interval into subintervals and constructing a piecewise approximation using several cubic polynomials.  
Suppose that we have \(n\) data; that is, \((x_{1},y_{1})\), \((x_{2},y_{2})\),..., \((x_{n},y_{n})\) with \(x_{1}<x_{2}<\cdots<x_{n}\). The interpolating polynomial is as follows:  
\[P(x)=\left\{\begin{array}{rl}p_{1}(x),&x\in[x_{1},x_{2}]\\ p_{2}(x),&x\in[x_{2},x_{3}]\\ &\vdots\\ p_{n-1}(x),&x\in[x_{n-1},x_{n}]\end{array}\right. \tag{1}\]  
where \(p_{i}(x)\) is of the form \(a_{i}+b_{i}x+c_{i}x^{2}+d_{i}x^{3}\) with \(a_{i}\), \(b_{i}\), \(c_{i}\), \(d_{i}\) constants; that is, a polynomial of degree less than or equal to three, corresponding to the interval \([x_{i},x_{i+1}]\). In addition, \(P(x)\) satisfies the following conditions:  
* Continuity; that is, \[p_{i}(x_{i+1})=y_{i+1}=p_{i+1}(x_{i+1})\quad\forall i=1,2,\ldots n-2.\]
* It is twice differentiable (class \(C^{2}\)); that is \[p^{\prime}_{i}(x_{i+1})=p^{\prime}_{i+1}(x_{i+1})\quad\forall i=1,2,\ldots n-2.\] \[p^{\prime\prime}_{i}(x_{i+1})=p^{\prime\prime}_{i+1}(x_{i+1})\quad\forall i=1,2,\ldots n-2.\]
* \(p_{1}(x_{1})=y_{1}\) y \(p_{n-1}(x_{n})=y_{n}\).
* The second derivatives at their boundary are zero; that is, \(p^{\prime\prime}_{1}(x_{1})=0\) y \(p^{\prime\prime}_{n-1}(x_{n})=0\).  
Suppose that, with the genetic algorithm, we obtain \(n\) data, with \(n<number\) of instances. The idea is to obtain the missing data using an interpolating (dashed) polynomial.  
It is worth noting that the spline interpolation does not provide control over the slope of the curves between subintervals. Hence, if the points obtained through the genetic algorithm vary, it can result in a complete change in the interpolation curve. To mitigate this limitation, exponential regression was utilized. This approach leads to a slight improvement in the Pareto front solutions as it results in fewer dominated solutions compared to those obtained through cubic spline interpolation.
## 4 Discussion
### Advantages and Limitations  
In this paper, the fitting abilities of the four function fitting models are compared in the two time series patterns--the yearly time series pattern and the seasonal time series pattern. Different results exist in the two different patterns. For the yearly time series pattern, the Harmonic model shows the best fitting accuracy, and the CCTM model is slightly inferior to the Harmonic model. Most of the cropland, forest, and grassland NDVI time series have obvious seasonal time-series variation characteristics [48; 49; 50], and the Harmonic model has the most symmetrical fitting functions among the four function fitting models [51], so the Harmonic model has shown the best fitting accuracy among the four models.  
For the seasonal time series pattern, the four function fitting models have all shown higher fitting accuracy than the models in the yearly time series pattern. Obviously, the seasonal pattern is a kind of segment fitting technique, and therefore the higher fitting accuracies were obtained in this pattern. The CCTM model has shown the highest fitting accuracy in this pattern. The CCTM model is designed according to the type of time series variation rates. It is a more comprehensive function fitting model than the Harmonic model. When it comes to time series with fewer periodic variations, such as the time series in one season, the CCTM model shows better fitting ability than the Harmonic model.  
The higher accuracy of the CCTM model also shows that the CCTM model has a stronger comprehensive function fitting ability than the Harmonic model for the asymmetric time-series variations induced by vegetation growth, natural disasters, etc. From the meaning of the second derivative mathematical function, the CCTM model has more comprehensive representation techniques. Therefore, the CCTM model can be more accurate than the Harmonic model for the continuous-time series reconstruction.  
The seasonal segment fitting results show that there are differences among different vegetation types. Obviously, different vegetation types mean different seasonal variation cycles and ranges of time series values, which lead to different fitting effects among different function fitting models. For the EBF vegetation type, the periodic variation characteristic is not significant in the yearly NDVI time series, and the Harmonic model and the CCTM model showed the largest differences.  
However, the CCTM model has more complex function fitting structures than the Harmonic model. If there is no requirement for high-precision reconstruction results or model interpretability, the CCTM model is not suggested.
###### Abstract  
The grounded electrical-source airborne transient electromagnetic (GREATEM) system is widely used in mineral exploration. Meanwhile, the induced polarization (IP) effect, which indicates the polarizability of the earth, is often found. In this paper, the Maxwell equations in the frequency domain are transformed into fictitious wave domain, where Maxwell equations are solved by the time domain finite difference method. Then, an integral transformation method is used to convert the calculation results back to the time domain. A three-dimensional (3D) numerical simulation in a polarizable medium is presented. The accuracy of this method is proven by comparing it with the analytical solution and the existing method, and the calculation efficiency is increased five-fold. The simulation results show that the GREATEM system has a higher response amplitude in the conductive region, while IP effects cannot be identified in the conductive area. The GREATEM system has a higher response amplitude in the low-resistance region, but IP effects cannot be identified in the low-resistance area, and the detection of IP effects is more suitable for the high-resistance area. Therefore, it is necessary to improve the detection ability of the GREATEM system in the low-resistance area.  
wave field transformation; finite difference method; Cole-Cole model +
Footnote †: journal: Applied Physics Letters B
# A Hybrid Optimization Algorithm for the Synthesis of Sparse Array Pattern Diagrams
###### Abstract  
To comprehensively address the challenges of aperture design, element spacing optimization, and sidelobe suppression in sparse radar array antennas, this paper proposes a hybrid particle swarm optimization (PSO) algorithm that integrates quantum-behavior mechanisms with genetic mutation. The algorithm enhances global search capability through the introduction of a quantum potential well model, while incorporating adaptive mutation operations to prevent premature convergence, thereby improving optimization accuracy during later iterations. The simulation results demonstrate that for sparse linear arrays, planar rectangular arrays, and multi-ring concentric circular arrays, the proposed algorithm achieves a sidelobe level (SLL) reduction exceeding 0.24 dB compared to conventional approaches, including the grey wolf optimizer (GWO), the whale optimization algorithm (WOA), and classical PSO. Furthermore, it exhibits superior global iterative search performance and demonstrates broader applicability across various array configurations.  
sparse antenna array; quantum-behaved particle swarm optimization; particle swarm optimization; sidelobe level; genetic algorithm Article