esa-sceva/satcom-chunk-collection huggingface dataset:
# New Taipei City Smart Pavement Management Center and Road Maintenance Analysis
## 5 Conclusions and Recommendations  
### Conclusions  
1. The New Taipei City Government's Maintenance Engineering Division has established standards and repair solutions for the seven main types of road damage in the city, providing appropriate maintenance measures based on the severity of damage, such as potholes, alligator cracks, and depressions.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Damage Type** & **Repair Method** & **Repair Area** & **Unit Price** & **Total Cost** & **Damage Photo** \\ \hline Single Block: & & & & & \\ Multiple Cracks, & & Full Milling & 18,655.8 & 440 & 8,208,552 & \\ Alligator Cracks & & & & & \\ Single Block: & & & & & \\ Block Cracks, Partial & Square Milling & 1397 & 650 & 908,050 & \\ Alligator Cracks & & & & & \\ Single Block: & & & & & \\ Depressions, Ruts, & & Cold Mix & & & \\ Partial Alligator & & Subgrade & 8.14 & 1378 & 11,217 & \\ Cracks & Improvement & & & & \\ Single Block: & & & & & \\ Local Cracks, & & Hot Recycled & & & \\ Longitudinal Cracks & Material Repair & 3.42 & 668 & 2285 & \\ Single Block: & & & & & \\ Longitudinal Cracks & & & & & \\ Single Block: & & & & & \\  & & & & \\  & & & & & \\ Single Block: & & & & & \\  & & & & \\  & & & & & \\  & & & & \\  &  
2. This study presents several real-world repair cases and statistical analysis of repair areas, unit prices, and costs. These data will be integrated into the Road Management Center's information platform for optimizing the work order module.
3. To enhance the quality of roadworks, contractors independently monitor road performance and implement pavement performance indicator management. This approach uses an intelligent platform to ensure road maintenance service quality, offering the most effective solutions while maximizing the utilization of limited funds.
4. By integrating intelligent recognition systems, contractors can independently manage quality control, develop a clear separation mechanism for inspection and repair, and maintain road safety to prevent patholes. The inspection records are stored in the road inspection center platform, categorizing frequently repaired and secondary repair sections to guide decisions on whether milling is necessary.
5. The continued improvement of data integration and module development will apply maintenance data to the work order module, enabling better cost prediction for repairs, improving the efficiency of the work order system, and enhancing the accuracy of resource allocation.
# Effect Mechanism of Connection Joints in Fabricated Station Structures
## 3 Structure Introduction and Ground Motion Input
### The Constitutive Model Introduction of the Structure and Soil  
(1) Constitutive model of the soil  
Since this research focused on the dynamic characteristics of underground structures, the equivalent linearization method [44] was adopted to consider the nonlinear behavior of soil under strong earthquakes. The same method was applied and verified by relevant studies [45; 46]. Rayleigh damping was used to simulate the hysteresis characteristics and nonlinear characteristics of the soil under dynamic cyclic loading, and the damping matrix is shown in Equation (7), where \(\alpha_{0}\) and \(\alpha_{1}\) are the damping proportionality coefficients, \(\zeta\) is the damping ratio (5% in this study), \(\omega_{i}\) is the ith order natural frequency of the soil layer, and \(\omega_{j}\) is the maximum Fourier amplitude for the input ground motion. Related applications and validation are in [47; 48].  
\[\left[C\right]=\alpha_{0}[M]+\alpha_{1}[K]\] \[\left\{\begin{array}{c}\alpha_{0}\\ \alpha_{1}\end{array}\right\}=\frac{2\zeta}{\omega_{i}+\omega_{j}}\left\{ \begin{array}{c}\omega_{i}\omega_{j}\\ 1\end{array}\right\}. \tag{7}\]  
Figure 6: Contact simulation of prefabricated components (take the D–E joint as an example).  
Figure 7: The numerical model with a viscoelastic boundary.  
(2) Constitutive model of the concrete  
Concrete damage plastic (CDP) material is a material in the ABAQUS material library, which is used to describe the deformation behavior and strength characteristics of concrete. The CDP model is typical and widely used in the literature [19; 39], and its stress-strain curve can be described using the damage factor and plastic strain. The CDP model parameters of the prefabricated components are shown in Table 3.
## 2 Thrust Vector Description and Mathematical Model
### Analytical Thrust Vector Model of Sun-Facing Diffractive Sailcraft
A possible improvement of the maneuverability could be obtained by abandoning the Sun-facing attitude hypothesis and, perhaps, employing a  
Figure 1: Conceptual scheme of the Sun-facing diffractive sailcraft with controlled binary metamaterial arrayed grating. The thrust vector direction is fixed in the body reference frame, while its heliocentric direction varies throughout the interplanetary flight. particular model of diffractive sail with a Littrow transmission grating [2], whose potential in an orbit-to-orbit interplanetary transfer has been recently investigated in [47]. As for the value of the characteristic acceleration, in this work, we consider a low-performance diffractive sailcraft, so the value of this important performance parameter is assumed to vary in the range \(a_{c}\in\left[0.06,\,0.12\right]\,\mathrm{mm/s^{2}}\). This specific interval was chosen, taking into account the actual performances of two RSS-propelled interplanetary spacecraft recently designed or even already launched. In fact, the value of \(0.06\,\mathrm{mm/s^{2}}\) is consistent with the characteristic acceleration of the (ill-fated) NASA's scientific mission Near-Earth Asteroid Scout (NEA Scout) [48], which was lost shortly after launch in November 2022. The upper value of the range, \(0.12\,\mathrm{mm/s^{2}}\), refers instead to the NASA's Solar Cruiser demonstration mission [49], which was recently canceled at an advanced stage of planning. It should be noted, however, that these performance values refer to a more classic RSS and therefore consider the current state of the technology required for the construction of a reflective sail. A diffractive sailcraft could require different construction solutions that would imply, for the same exposed surface, a membrane of different mass compared with an RSS. A discussion in this sense is certainly beyond the scope of this work and is currently analyzed in the context of dedicated studies [50]. Considering this range of variation for the characteristic acceleration, and taking a solar distance \(r\in\left[0.3,\,1.2\right]\,\mathrm{AU}\), Equation (1) gives the graph reported in Figure 2, which shows the variation of the propulsive acceleration magnitude with \(r\) and \(a_{c}\). In particular, the red (or green) area in that figure indicates the range of solar distance that is obtained if one considers a Sun-facing diffractive sailcraft moving in the vicinity of the elliptical orbit of Mercury (or of the Earth), that is, of the two reference elliptical orbits that will be considered in the numerical simulations illustrated in Section 3. For example, Figure 2 shows that a low-performance diffractive sailcraft with \(a_{c}\simeq 0.6\,\mathrm{mm/s^{2}}\) gives a propulsive acceleration magnitude of roughly \(0.3\,\mathrm{mm/s^{2}}-0.6\,\mathrm{mm/s^{2}}\) in a Mercury-based mission scenario.
## 2 Proposed Method
### Shape Similarity Measure  
Although not unique in a cluttered background, the DP's corner edges are still useful for localizing. The same shape metrics as those of SBM are employed. For each point participating in matching, the angle is calculated between the gradient vector in the template and, first, the corresponding point in the image, and then, the cosine, to obtain the point similarity. Finally, the similarity is summed up across the template to obtain a candidate score as follows:  
\[F_{shape}(x)=\frac{1}{n}\sum_{i=1}^{n}\cos(\theta-\overline{\theta_{i}}) \tag{1}\]  
where \(\theta\) and \(\overline{\theta_{i}}\) are the angles of the gradient vectors belonging to point \(i\) in the template and the corresponding one in the image, and \(n\) is the number of points in the template.  
In this study, some implementation changes were made. The Canny algorithm was used to extract the one-pixel edge of the template image to obtain the gradient orientation image:  
\[\mathbf{T}(u,v) =0.25(\boldsymbol{\theta}(u,v)+360), \tag{2}\] \[\boldsymbol{\theta} =\arctan(\mathbf{g}_{u},\mathbf{g}_{v}). \tag{3}\]  
Here, \(\mathbf{g}_{u}\) and \(\mathbf{g}_{v}\) are the gradient amplitudes obtained by applying the \(2\times 2\) Sobel operators over the image \(\mathbf{I}\):  
\[\mathbf{g}_{u}=\left[\begin{array}{ccc}-1&0&+1\\ -2&0&+2\\ -1&0&+1\end{array}\right]\ast\mathbf{I}, \tag{4}\]  
\[\mathbf{g}_{v}=\left[\begin{array}{ccc}-1&-2&-1\\ 0&0&0\\ +1&+2&+1\end{array}\right]\ast\mathbf{I}. \tag{5}\]  
Each point in \(\boldsymbol{\theta}\) is the original gradient orientation. The orientation values are squeezed from a range of 0-360 to 0-90 with a minimum resolution of 4 degrees, enabling their storage within a single-channel 8-bit image to obtain a gradient orientation image. When building templates offline, a 360 bias is added to the angle of each point in advance to ensure that it remains greater than zero when subtracting the angle of the matching point during online matching. As shown in Figure 3, in this way, the positive angle value can be directly utilized as an index for a 2D array to search entries in a look-up table (LUT). A value of 255 is also assumed to indicate the absence of gradient orientation for a point, indicating that the gradient amplitude is zero.  
During template building, it is necessary to record the coordinate offset of each template edge point relative to the template center, the gradient angle of each point, and an LUT, which is essentially an array used to directly index the cosine value of the difference angle between two matching gradient vectors. When performing online matching, the template slides across the entire image at the center point and uses the coordinate offsets to find corresponding points for point-to-point matching. The cosine score of each point is then accumulated to obtain the similarity score of the target at the current position. Finally, the extreme similarity score is obtained for the entire image to determine the final detection position.
## 3 Methodology
### Collaborative Clustering
#### 3.2.6 Collaborative Scenarios  
In this section, some collaborative scenarios are presented in which the collaborative method could be used, together with the most critical collaborative applications of these scenarios and a description of the collaborative workflow process in each case. * Parallel Scenario  
Figure 2 illustrates the parallelism scenario in collaborative clustering; several clustering algorithms run in parallel and improve each other's results. This process underlies the previously discussed horizontal collaborative method. The most important applications of this type of collaboration are several algorithms that operate on (i) different feature spaces searching for a different number of clusters, i.e., multi-view clustering; (ii) distributed data sets; and (iii) a problematic data set, whether distributed or undistributed. In all three applications, mutual improvement in the results would be beneficial, i.e., the outcome of collaborative clustering. This collaborative method can work with all previous applications, but in this research, the first application is of interest. Qualitative and quantitative features of the heart disease data are processed with suitable clustering algorithms in an attempt to enhance their results by sending and receiving information determined by the collaborative method. In Algorithm 2, the workflow demonstrates how the collaborative process can be implemented with clustering algorithms running in parallel. However, one should note that in this case, all collaborative algorithms must satisfy the collaborative method hypothesis, which is an objective functional optimisation similar to Equation (2). * Reinforcement Scenario  
Figure 3 shows the reinforcement scenario, which is another possible scenario that could be handled by the collaborative method. In this case, the information is transmitted from one side only. In Algorithm 3, the performance of an EM clustering algorithm is enhanced by the use of information from other algorithms. One-sided information transfer would be beneficial if the latter were specialised algorithms capable of detecting particular elements of the observed data. In this case, the process would be analogous to the reinforcing rather than the collaborative learning process. In the current context, since the observed heart disease data were processed using clustering algorithms which specialised in qualitative and quantitative features, the collaborative method could be used to reinforce a qualitative algorithm with information from other quantitative algorithms and vice versa (Figure 3). Figure 2: Collaborative clustering for parallel scenario. However, in this case, the EM algorithm must satisfy the premise of the collaborative method, which is an objective function optimisation similar to Equation (2). Although the remaining clustering algorithms can optimise different objective functions only, their solution vectors are of interest. Equation (9) is computed for the algorithm being reinforced only, as shown in Equation (10).
# Interference Alignment in Multi-Hop Cognitive Radio Networks under Interference Leakage
## 1 Introduction
### Related Works  
There are various IA techniques that are trying to provide interference-free communications in CR networks. In the linear IA technique, the channel matrix is assumed to be perfectly known at the transmitter and receiver side of the PN [11; 12; 13; 14]. In the literature, linear IA was adopted in CR interference channels in [15; 16; 17; 18] and the references therein. In [15], the adaptive power allocation schemes were considered for linear IA-based CR networks where the outage probability and sum rate were derived. In [16], the adaptive power allocation was studied for linear IA-based CR using antenna selection at the receiver side, whereas [17] enhanced the security of CR networks by using a zero-forcing precoder. A similar work was proposed in [18] to improve the overall outage performance of the interference channel by using power allocation optimization. These studies show that interference management is an important issue for all multi-user wireless networks.  
Most recently, multi-hop relaying, which is an effective way of enhancing reliability, connectivity, and coverage area, was introduced in CR networks [19; 20; 21]. In these papers, the authors studied the multi-hop cognitive relay networks under interference power constraints and provided a comprehensive performance study including the closed-form expressions for the outage probability, bit error rate (BER), and ergodic capacity. The paper [22] considered the performance of multi-hop CR networks with imperfect channel state information (CSI). Besides, the performance metrics of the secondary multi-hop networks covering outage probability, BER, and ergodic capacity were derived over Rayleigh fading channels. In [23; 24], the authors considered the cognitive multi-hop system model and analyzed the performances over a generalized-\(K\) distribution. The outage probability analysis of a single-hop CR network was studied in [25] by considering multi-hop relaying in PN. [PERSON] et al. in [26] and the authors in [27] considered a multi-input multi-output (MIMO) multi-hop CR network and investigated its detailed performance. Finally, [28] demonstrated the effect of cluster-based relaying in the implementation complexity and provided the performance of a multi-hop cognitive relaying system in terms of outage probability, symbol error rate (SER), and ergodic capacity.
## 3 Results and Discussion
### Development of Hybrid Mixes  
With the results of each individual additive at hand, hybrid SCC mixes using optimum doses of CWCA, CWFA, CWP, and EAFS were developed. In total, three hybrid mixes were put forward and tested for their rheological properties as well as their compressive strength. The testing matrix showing the content of the hybrid mixes is shown in Table 4.
# Towards Online Personalized-Monitoring of Human Thermal Sensation Using Machine Learning Approach
## 2 Methods
### Experiments and Experimental Setup
#### 2.2.3. Measurements and Gold Standards  
During the course of the experiments, participants' heart rate \(H_{R}\), metabolic rate \(M_{r}\), average skin temperature \(T_{sk}\), heat flux \(q_{sk}\) between the skin and the ambient air, core body temperature \(T_{c}\) represented by the aural temperature\(T_{cr}\)were measured continuously. The heart rate of each participants was monitored with a sampling rate of 128 Hz using the Polar H7 ECG strap that is placed under the chest. The metabolic rate as metabolic equivalent tasks (METs) of each participant was calculated based on indirect calorimetry using MetaMAX 3B spiroergometer sensor. The average skin temperature was calculated based on measurements from three body-places, namely, scapu \(T_{cap}\), chest \(T_{ch}\) and arm \(T_{arm}\) (Figure 3). The skin temperature measurements were performed using one Shimmer temperature sensor and two gSKIN(r) bodyTEMP patches. Two heat flux gSKIN(r) patches were placed on both the chest and the left arm (Figure 3). The skin temperatures and heat flux measurements were acquired at sampling frequency of 1 Hz. Core body temperature was estimated based on aural temperature measure measurements, which was performed using in-ear wireless (Bluetooth) temperature sensors (Cosinuss One) with a sampling rate if 1 Hz. At the end of each applied temperature level during the course of both experimental phases, a thermal sensation questionnaire, based on ASHRAE 7-poins thermal scale, was performed for each test subject.  
Figure 3: Sensor placement. (**A**) Ear channel for aural temperature measurement via the Cosinuss One, (**B**) upper arm where skin temperature and heat flux are measured with the gSKIN patch, (**C**) middle upper chest where skin temperature and heat flux are measured with the gSKIN patch, (**D**) lower chest where heart rate is measured with the Polar H7, (**E**) Scapula where skin temperature is measured with the shimmer, (**F**) mouth and nose where metabolic rate is measured via the MetaMAX-3B spiroergometer sensor.  
Figure 2: Photographic picture of the three climate-controlled rooms (from right to left, A–C).
# Fatigue of Friction Stir Welded Aluminum Alloy Joints: A Review
## 2 Fatigue Failure Mechanism of FSW Weld
### Characteristics of Weld Zones  
Metallographic experiments using optical microscopy and scanning electron microscope found that the weld cross-section could be divided into nugget zone (NZ), thermomechanically affected zone (TMAZ) and heat affected zone (HAZ) [21; 22], as shown in Figure 2. Analysis of each zone revealed that the material in NZ region underwent dynamic recrystallization process and redistribution of the strengthening phase. The transformation of the microstructure into finer equiaxed grains caused the preexisting cracks to disappear [23]. According to the Hall-Petch relationship, finer grains have better fatigue resistance, higher tensile properties, less misalignment, and better mechanical properties [24]. For the TMAZ on both sides of the friction stir welded joint material, TMAZ (advancing side) has obvious contour boundary, and the TMAZ (retreating side) contour boundary is relatively blurred. It is generally believed that this phenomenon is related to the material flow direction [22]. The heat affected zone (HAZ) is located between the base metal (BM) and the TMAZ. The HAZ is only affected by thermal cycling, leading to coarsening of the strengthening precipitates and decreased yield and ultimate strengths [25; 26].  
Figure 1: Illustration of friction stir welding (FSW) process.  
Figure 2: Metallographic cross-section of the FSW weld [23].
# Assessment of Carbon Emissions at the Logistics and Transportation Stage of Prefabricated Buildings
## 2 Research Boundary and Key Parameters
### Calculation of Carbon Emissions from the Transportation of Building Materials  
The transportation phase of building material logistics is the transportation of materials from the factory to the construction site, without considering the carbon emissions generated by the transportation of raw materials from the mining site to the material processing plant. Differences in carbon emissions caused by road conditions and transporters' operation of the means of transport were not considered. The transportation of building materials to a construction site leads to greenhouse gas emissions. The carbon emissions from the transportation of building materials were calculated using the distance method, which is mainly related to the transportation distance, the weight and type of materials, the transportation method, and the transportation time [20]. Its calculation formula is as follows:  
\[E_{tu}=\sum\limits_{i=1}^{m}{(\sum\limits_{i=1}^{n}{Q_{ci}\times D_{ci,j}}) \times f_{Tj}} \tag{6}\]  
\(Q_{Ci}\): Demand for building material \(i\).  
\(D_{ci,j}\): Distance that building material \(i\) is transported by transport mode \(j\).  
\(i\): Building material type.  
\(j\): Mode of transport of building materials.  
\(\sum\limits_{i=1}^{n}{Q_{ci}\times D_{ci,j}}\): Summation of distances of all construction materials transported using the transport method \(j\).  
\(f_{Tj}\): Carbon emissions factor of transportation mode \(j\).
## 1 Introduction
An ELPW allows not only range information but also 3-D shape in the direction of laser beam propagation by analyzing the features of the scattering signals, without an optical scanning system [25]. In this study, a new method based on the ELPW analysis was proposed for orientation determination of space debris and its feasibility was also investigated by numerical simulations of two space targets (i.e., defunct satellite and rocket body). Unlike the passive and SLR technologies, the proposed method is free of the measurement constraints such as sun-illumination and CCRs requirement. Approximating that the reflected surface of a space target consists of small grids, a mathematical model was derived to generate the ELPW for the fully reflected surface, which takes into consideration temporal beam broadening and spatial beam spreading due to the atmospheric turbulence. The simulation took into account detector noise and amplifier noise by assuming that the noises are independent and of Gaussian distribution in the optical power domain. A genetic algorithm was also introduced to determine the Gaussian parameters of Gaussian decomposition and to analyze the relationship between ELPW features and orientation of the space target in terms of Gaussian parameters. It was shown from the numerical simulations that the proposed method was a feasible alternative to achieve orientation information because the ELPW had a distinctive shape characterizing the orientation of space debris.