esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Comparison of the Proposed Method with the Weighted Sum Methods
### Loads Are Borne by the Entire Structure
#### 5.1.1 Problem 1
Figure 14: Structure performance using the weighted sum (WS) methods and the bisection constraint method (BCM) for Problem 1 with \(F_{1}=0.9\) and \(F_{2}=1.0\): (**a**) compliance; (**b**) stress level. Figure 15: Structure obtained using the traditional equally weighted sum (TEWS) method (Stage 1) and the bisection constraint method (BCM) for Problem 1 with \(F_{1}=1.0\) and \(F_{2}=1.0\) (same result). The results for Problem 1 are summarized in Table 4 to provide a broad view of this problem. In this table, the results of Stage 1 (the TEWS method) and Stage 2 of the proposed framework are presented, including the obtained topologies, compliances under LC1 and LC2, and stress levels under LC1 and LC2. By showing the results of the two stages, an improvement of compliance (maximum compliance being reduced) can be seen from Stage 1 to Stage 2, and the change in stress levels can also be observed. For example, when \(F_{1}=0.5\) and \(F_{2}=1.0\), the compliance under LC1 and LC2 in the TEWS structure is 15.64 and 45.07, respectively. After Stage 2, the compliances under the two loading conditions became the same and equal to 42.43, which means the maximum compliance was reduced from 45.07 (TEWS) to 42.43 (BCM) (an approximately 6% reduction). At the same time, the stress levels in the TEWS structure under LC1 and LC2 are 0.118 and 0.158, respectively. After Stage 2, these stress levels were 0.135 and 0.152, respectively. These results show the effectiveness of the proposed method not only in reducing the maximum compliance but also the stress level, considering both loading conditions. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Load Combination** & **Method** & **Topology** & \(c_{1}\) & \(c_{2}\) & \ \begin{tabular}{c} **Stress** \\ **Level 1** \\ \end{tabular}  & \begin{tabular}{c} **Stress** \\ **Level 2** \\ \end{tabular}  \\ \hline \multirow{4}{*}{\(F_{1}=0.2\), \(F_{2}=1.0\)} & TEWS & 3.44 & **43.37** & 0.050 & **0.153** \\  & (Stage 1) & 3.44 & **43.37** & 0.050 & **0.153** \\ \cline{1-1}  & & & & & \\ \cline{1-  
Overall, in most cases of Problem 1, the proposed BCM method was more effective in finding structures with better performance (low stress level) than the TEWS method. In addition, by integrating the TEWS method in Stage 1, the proposed framework can result in a symmetric topology in a special loading scenario, that is, symmetric loading conditions.
# Influence of Social Overhead Capital Facilities on Housing Prices Using Machine Learning
## 3 The Proposed Scheme
### Preprocessing Data
#### 3.2.8 Addressing Missing Data and Incorporating Building Age  
The concurrent development of housing and associated infrastructure, such as roads and sewage systems, provides distinct efficiency benefits compared to sequential development. This integrated approach in housing projects streamlines approval processes, eliminating the need for multiple approval stages and accelerating the overall development timeline [62; 63]. Against this backdrop, it is logical to infer missing values by observing analogous developmental patterns within the dataset.  
In relation to the 'BLDG_YEAR' variable from Table 1, we identified a total of 845 missing values across both the training and testing datasets. To address this, we employed the KNN imputer method [64], leveraging inherent data similarities. The KNN imputer works by pinpointing the K-nearest neighboring data points and then imputing the missing value based on the average of these neighbors. For our study, we utilized the KNN Imputer from scikit-learn, setting it to consider the five nearest data points for imputation purposes. The age of a building is undeniably a pivotal factor in real estate purchasing decisions. To encapsulate this aspect, we introduced the 'AGE_BLDG' variable, which is derived by subtracting the building's construction year from the contract year.
## 3 Results
### Characterization of Construction Materials
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample (\({}^{\text{(I)}}\)**} & \multirow{2}{*}{**M (\({}^{\text{(I)}}\)**} & \multicolumn{6}{c}{**Aggregate (\({}^{\text{(I)}}\)**} & \multicolumn{1}{c}{**Binder (\({}^{\text{(I)}}\)**} & \multicolumn{1}{c}{**Aggregate/**} \\ \cline{3-10}  & & \multicolumn{1}{c}{**>4 mm**} & \multicolumn{1}{c}{**4–2 mm**} & \multicolumn{1}{c}{**2–1 mm**} & \multicolumn{1}{c}{**1–0.5 mm**} & \multicolumn{1}{c}{**0.5–0.25 mm**} & \multicolumn{1}{c}{**0.25–0.10 mm**} & \multicolumn{1}{c}{**Binder Ratio**} \\ \hline
1O & 1.40 & 0.00 & 15.50 & 31.60 & 26.90 & 15.58 & 10.42 & 11.47 & 7.72 \\
2O & 0.70 & 8.39 & 23.35 & 37.66 & 18.94 & 6.39 & 5.27 & 8.75 & 10.42 \\
3O & 1.68 & 5.06 & 16.06 & 32.68 & 31.74 & 9.35 & 5.12 & 8.34 & 10.99 \\
4O & 1.63 & 3.47 & 20.85 & 34.26 & 30.13 & 7.36 & 3.93 & 6.36 & 14.72 \\
5O & 1.66 & 7.25 & 19.72 & 31.94 & 28.21 & 7.88 & 4.99 & 8.18 & 11.22 \\
6I & 1.90 & 0.88 & 14.93 & 26.95 & 25.91 & 18.52 & 12.81 & 21.24 & 3.70 \\
7I & 0.96 & 7.24 & 36.14 & 23.47 & 16.11 & 9.49 & 7.55 & 10.13 & 8.87 \\
8I & 1.05 & 8.10 & 25.44 & 24.14 & 19.96 & 12.71 & 9.66 & 11.73 & 7.52 \\
9I & 1.65 & 1.30 & 21.82 & 26.87 & 22.78 & 17.11 & 10.11 & 14.65 & 5.82 \\
10I & 1.37 & 0.00 & 17.17 & 28.47 & 23.51 & 17.87 & 12.98 & 16.45 & 5.08 \\ \hline \hline \end{tabular} \({}^{\text{(I)}}\) O: from the outer face of the wall. I: from the inner face of the wall. \({}^{\text{(I)}}\) M: moisture content (in percentage). \({}^{\text{(I)}}\) Percentage.  
\end{table} 
Table 4: Moisture content, particle size distribution of the aggregates and aggregate/binder ratio of the mortar samples.
# Reconstruction and Measurement of Irregular Karst Caves Using BLST along the Shield Metro Line
## 2 Multiwavelength Laser Attenuation Characteristics Test in Water-Filled Karst Caves
### Laser Attenuation Characteristics Test System
#### 2.1.2 Simulation Module of Karst Cave Water Environment  
This module can simulate the water turbidity under different suspended media conditions. According to the exploration data of spring strata, it was found that most of the suspended media in water-filled caverns are silt, clay, fine sand, and calcium carbonate. In this test, four kinds of suspended media were used: silt, clay, fine sand, and CaCO\({}_{3}\). Each medium was evaluated as one of five grades according to solubility, and the turbidity index was measured by a turbidity tester.  
\begin{table}
\begin{tabular}{c c c} \hline
**Type** & **Wavelength (nm)** & **Wavelength Selected (nm)** \\ \hline Blue-violet laser & 405 & 405 \\ Blue laser & 450, 457, 473 & 450 \\ Green laser & 532 & 532 \\ Near infrared laser & 635, 660, 670, 671 & 650 \\ Far infrared laser & 808, 946, 980, 1047, 1064 & 808 \\ \hline \end{tabular}
\end{table}
Table 1: Laser wavelength.  
Figure 1: Testing system: (**a**) design; (**b**) actual device graph.
## 4 Results and Discussion  
### Case Study  
The integration of advanced information and communication technologies along with Internet of Things (IoT) devices has brought a significant transformation to the Intelligent Transportation System (ITS) in smart cities. The incorporation of these technologies has resulted in synchronized transport networks, improved driving experience, optimized traffic management, and facilitated intelligent vehicular applications [1; 2]. The image communication system lies at the core of the ITS, enabling applications that directly connect vehicles with traffic infrastructures and management systems [3]. One such application of traffic sign recognition in ITS was considered in this experiment. Traffic sign recognition is an essential CV application of an ITS environment and the collection and recognition of these signs are critical to ensure smooth traffic flow and prevention of accidents [3]. Effective traffic sign recognition in ITS requires both the collection of real-time images using high data rate communication systems, and the accurate recognition of these images using AI-enabled systems, respectively. In this experiment, the performance analysis of both these aspects was considered.  
Furthermore, to effectively deploy AI-enabled system in an ITS environment for CV applications, a robust computing infrastructure is required for handling large amounts of image data, processing it in real-time, and performing complex computations [70; 71; 72]. Edge servers and cloud servers are two potential solutions that can meet these requirements [72]. Edge servers are located close to the data source, and can process data locally, leading to faster processing, reduced latency, and improved reliability. Cloud servers, on the other hand, provide vast computing power and storage capacity, making them ideal for data-intensive tasks such as DL model training. By combining the computing power of edge and cloud servers, it is possible to process and analyze data in real-time while minimizing latency and reducing communication costs forming and edge-cloud collaboration [72]. Figure 4 shows such an infrastructure for the traffic sign recognition application in ITS, as considered in this experiment. Images are first transmitted to the edge server through various OFDM-based image communication systems, where they are introduced to noise and distortions from the system. The edge server then carries out inferencing on the received distorted images using a trained model, which were trained in the cloud server using a public dataset. The cloud center has several models that are task specific to enable Machine Learning as a Service (MLaaS) [72; 73]. In this scenario, the edge server requested a model for classification task to carry out traffic sign recognition in the ITS environment.
## 2 Models and Problem Formulation
### Models
#### 2.1.2 Interference Model  
We propose an interference model, which extends the _Protocol Model_ in [24] to directional antennas. Our model only considers directional transmission and directional reception, which can maximize the benefits of directional antennas.  
Two nodes \(X_{i}\) and \(X_{j}\) can establish a bi-directional _link_ denoted by \(l_{ij}\) if and only if the following conditions are satisfied.  
Figure 1: The Antenna Model.  
1. \(X_{j}\) is within the _transmission range_ of \(X_{i}\) and \(X_{i}\) is within the _transmission range_ of \(X_{j}\).
2. \(X_{j}\) is covered by the antenna beam of \(X_{i}\). Similarly, \(X_{i}\) is also covered by the antenna beam of \(X_{j}\).
3. No other node within the _interference range_(the interference range is used to denote the maximum distance within which a node can be interfered by an interfering signal) is simultaneously transmitting over the same channel and in the same direction toward \(X_{j}\).  
We call two nodes in _conflict_ with each other if they are located within the interference range of each other and their antenna beams are pointed toward each other. For example, in Figure 2, node \(X_{k}\) within the interference range of node \(X_{j}\) may conflict with \(X_{j}\). Link \(l_{ij}\)_conflicts_ with link \(l_{kl}\) if either node of one link conflicts with either node of the other link.
## 4 Conclusions  
This study confirms the technical, nutritional, and functional viability of producing two complementary soy-based ingredients--full-fat soy (FFS) and expanded soybean cake (ESC)--using a continuous, hydrothermal-pressing process specifically adapted for Central European agro-climatic conditions. The application of the P1 cultivation system, integrating mechanical weed control and optimized agronomic management, supported stable yields, improved plant performance, and consistent seed composition across variable weather conditions.  
ESC showed distinct nutritional benefits, characterized by increased protein levels, decreased lipid content, and a higher dietary fiber fraction, and lowered anti-nutritional factors such as trypsin inhibitors and phytic acid. Its favorable oxidative stability and microbiological safety over a 6-month storage period further enhance its suitability for both food and feed applications. These attributes position ESC as a promising ingredient for clean-label, high-protein formulations, including extruded snacks, functional foods, and hybrid protein products.  
From an agroecological perspective, the semi-organic P1 model offers a scalable, glyphosate-free alternative to conventional soybean cultivation, supporting EU Green Deal objectives for sustainable agriculture and regional protein self-sufficiency. The circular processing platform developed herein enables efficient partitioning of protein, lipid, and fiber fractions, contributing to resource-efficient and economically viable local supply chains.  
Future research should focus on further valorization pathways for ESC in specialized food sectors, optimization of fatty acid profiles via oil blending to enhance omega-3 content, and comprehensive techno-economic assessments of full-scale implementation across diverse European agro-climatic zones.
# Erosion Resistant Hydrophobic Coatings for Passive Ice Protection of Aircraft
## 1 Introduction
Among these, erosion resistance is the most important, since the coating applied to the leading edge of a wing or helicopter rotor blades, where icing tends to occur and cause the most negative impact on aircraft operation, may experience excessive wear due to the high-speed (e.g., 100-300 m/s) impact of sand, airborne dust, rain droplets and hail. To mitigate erosion damage, shield materials such as metal strips or caps made of nickel, titanium and stainless steel, as well as polymeric erosion protective tapes, are typically applied to the leading edges. Although studies have been carried out to understand the environment and surface properties in relation to IAS, and ceramic erosion-resistant coatings have been investigated for icing protection [23; 24], the effectiveness of metals and ceramic coatings is limited due to their low ice adhesion reduction factor (e.g., \(<\)2) and poor applicability on large curved surfaces. In addition, erosion resistant polymeric coatings with reduced ice adhesion strength remain unknown. Thus, it is the objective of this study to develop highly robust icephobic polymer coatings suitable for aircraft ice protection at leading edges, which have not only high icephobicity and superior erosion resistance, but also excellent thermal and mechanical properties, along with easy applicability on large curved surfaces. To this end, surface modification of a highly erosion resistant coating, namely PU-R, was carried out by introducing new surface-modifying polymers (SMPs) into the coating formulation, thereby producing novel slippery polyurethane (SPU) coatings with high surface hydrophobicity. The PU-R is a high solid, 2-part, solvent-borne aliphatic polyurethane coating developed at the National Research Council Canada. The coating demonstrated superior erosion resistance against both high-speed solid particles and rain droplets, outperforming commercial aircraft top-coat and erosion protective tapes [25]. It is expected that the resulting SPU coatings could retain the exceptional erosion resistance of PU-R while exhibiting improved icephoicity to mitigate the hazards caused by icing on the leading edge surfaces of aircraft wings and helicopter rotor blades.
## Materials and Methods  
### Experiment  
A data flow diagram is shown in Figure 1 that illustrates the process of obtaining GAN to improve the target model using augmented FBB amyloid brain PET image data and measuring the reality of the generated data and its suitability for use in data augmentation. First, raw PET images obtained from a PACS running at DAUH undergo pre-processing. The pre-processed images were examined, and 3D images from patients that were A\(\beta\) negative or A\(\beta\) positive were divided using a 1:1 ratio into training and test sets. The training set was used to select and train GAN models that generated images of both groups. In this experiment, the similarity between the images generated from the trained GAN model and the real images was evaluated using a Visual Turing test, distribution with t-SNE, and 3 quantitative metrics. The metrics selected to measure a similarity of a given data distribution in this experiment used recently reported model-agnostic metrics [30; 33] including Maximum mean discrepancy (MMD), Frechet inception distance (FID), and The 1-nearest neighbor classifier (1-NN) leave-one-out (LOO) accuracy instead of traditional approaches in which the their limits are reported [34]. Finally, the generalization performance of the target model was measured by comparing the performance of a target model that was trained using only the training set with the augmented target model that was trained using both the training and generated sets.  
Figure 1: Data flow diagram summarizing the experimental process.  
The tool used in this experiment was written using Python 3.6.9 (Python Software Foundation, Wilmington, DE, USA), and Keras 2.2.4, and OpenCV 4.1.2.30 libraries were mainly used. DenseNet was used as a feature extractor, and finetuned weights were provided by the Keras library. The experimental environment ran on Linux Ubuntu 16.04 LTS with 4 NVIDIA GeForce GTX TITAN XP GPU.
# A Short Review of Through-Silicon via (TSV) Interconnects: Metrology and Analysis
## 3 TSV Reliability
### Stresses
For instance, the tensile radial stresses in copper can cause void formation, which can lead to the degradation of TSV electrical performance. Additionally, the compressive circumferential stresses can influence the mechanical strength of TSVs and their adhesion to the silicon substrate [7; 51; 54]. Due to the small size of through-silicon vias (TSVs), it is crucial to use microscopic characterization techniques to test their reliability. Although several experimental methods have been employed to analyze the stress distribution on TSVs, including micro-Raman spectroscopy and synchrotron X-ray microdiffraction techniques, these techniques tend to be expensive and have limitations. In contrast, numerical simulations are a popular and cost-effective method for evaluating the reliability of TSVs, such as using finite element analysis [35; 55]. One study conducted by [PERSON] et al. [14] found that the silicon near Cu TSVs undergoes significant stresses during processing steps. To characterize the local stress of Si around Cu TSVs of sizes ranging from 4 to 8 \(\upmu\)m, micro-Raman spectroscopy was utilized as a function of processing steps. The measured stresses were then used to determine the keep-off-zone that can be used in device design to ensure reliability. It is worth noting that the Raman displacement is proportional to the sum of the stresses. Therefore, even if the actual stress is relatively high, it may lead to a low Raman shift. Hence, assuming that the region has no stress due to the low Raman shift is incorrect and can result in inaccurate predictions. It is important to verify finite element simulations of stress distribution in TSVs by conducting micro-Raman spectroscopy and vice versa. By generating a model and using appropriate material data, finite element analysis can be used to simulate the stress behavior in a chip with integrated TSV. With numerical simulations, it is possible to assess the reliability issues of  
Figure 10: (**a**) Radial stresses around a TSV; (**b**) circumferential stresses around a TSV [51]; (**c**) variation in TSV stress with distance; (**d**) variation in TSV stress with temperature and TSV density [53]. TSVs without incurring significant costs or limitations, unlike experimental methods, such as micro-Raman spectroscopy and synchrotron X-ray microdiffraction techniques. In addition to the conventional methods mentioned above, an ion beam delamination (ILR) method was proposed by [PERSON] et al. to determine the residual stresses at the TSV-Cu/TiW/SiO\({}_{2}\)/Si interface on the nanoscale [56]. This method measures the corresponding deflection \(\delta\)(t) from a high magnification SEM image of the microcantilever tip region, calculates the curvature \(\kappa\) from the microcantilever length and the original cantilever deflection soriginal, and then solves for the stress gradient. Additionally, [PERSON] et al. introduced optical second-harmonic generation (SHG) scanning microscopy to characterize strain fields around TSVs while maintaining micrometer spatial resolution [57].
## 3 Seismic Damage Model
### Seismic Damage Model Considering Biaxial Coupling Load Effect
#### 3.3.1 Damage Index Considering Biaxial Coupling Load Effect  
The above damage models are used to represent damage of the components only in the \(X\)-direction or in the \(Y\)-direction. Therefore, it is useful to establish a comprehensive index for consideration of a biaxial coupling effect by the combination of the damage indexes in both the \(X\) and \(Y\) directions. The biaxial damage index of RC bridge pier under biaxial cyclic loading is thus defined:  
\[D\ =\ D_{x}+D_{y}-\varepsilon\sqrt{D_{x}D_{y}} \tag{10}\]  
where \(\varepsilon\) is the coupling factor of biaxial damage and has a defined range of \(0\leq\varepsilon\leq 1\). It is assumed that \(D\ =\ 1\) at the point of pier collapsing, and in doing so, the values of \(D_{x}\) and \(D_{y}\) as obtained from Equation (4) are used to determine \(\varepsilon\). The coupling factors for all conditions were determined, from which the minimum value of 0.915 was chosen to be the biaxial coupling factor as it would be a conservative selection to represent real damage.  
Figure 11: Double straight-line energy equivalent area method for displacement ductility.
# An Adaptive Linear Programming Algorithm with Parameter Learning
## 1 Introduction
### Frame of Reference  
There are special requirements for engineering design methods. Engineering design is a task that occupies multiple designers with different fields of expertise to make decisions that meet a variety of requirements [10]. Designers may define objective functions using different methods--utility theory [11], game theory [12], analytic hierarchy process (AHP) [13], Pareto-optimal methods [14], etc. For optimization problems with one or multiple objective function(s) to be minimized or maximized, one can define such problems based on maximization of expected utility. Designers accept assumptions and simplifications when using utility theory to manage a problem, sometimes without realizing it. Such assumptions include the abstraction of mathematical relationships between variables to be 100% accurate and not evolve, the levels of fidelity of different sub-models or segments of a model are the same, the design preferences among multiple objectives are fully captured, etc. [15]. For many problems, these assumptions are wrong and this may cause problems. For example, when implementing an optimal solution, a system may not give optimal output, because of the inaccurate equations used in the model, or because uncertainty breaks the equilibrium of any Karush-Kuhn-Tucker (KKT) conditions, thus destroying the optimality of the solution [16].  
In addition, when the problem is nonconvex, specifically, when any nonzero linear combination of the constraints is more convex than the objective function [17], Category (i) methods cannot return a feasible solution because the sufficient KKT condition cannot be satisfied [15]. In this case, formulating the problem using the compromise decision support problem (cDSP) and solving it using the adaptive linear programming (ALP) algorithm is a way to identify satisficing solutions that meet the necessary KKT condition [15].