esa-sceva/satcom-chunk-collection huggingface dataset:
# Perceptions and Resistance to Accept Smart Clothing:
## 2 Literature Review
### Factors Affecting the Resistance to Innovation
#### 2.3.4 Perceived Risk  
[PERSON] [14] argued that innovation resistance arises according to the risks involved in innovation, and that the higher the risk, the higher the innovation resistance. Until now, consumers' perception of risk has been measured in various dimensions [34]. For many consumers, the judgment they want to trust mostly refers to safety standards [35]. For other consumers, this confidence can be extended to include judgments about ethical considerations as to whether a product should be available in the marketplace. Smart clothing is still early in development, and there are no safety standards related to the manufacture and sale of smart and e-textiles and smart clothing. Under these circumstances, consumers are expected to perceive several risks to smart clothing, and consumers' perceived risks to smart clothing are expected to have an impact on determining whether smart clothing is accepted or not.  
In this study, performance risks and physical concerns about the risks that products can pose to health or safety, which are perceived by many consumers in relation to smart clothing [26], are among various types of risks. Physical risk arises from concerns about the risks a product may pose to health or safety [36], and many studies on consumer resistance have demonstrated that the higher the physical risk, the higher the consumer's resistance [37; 38]. Next, functional risks stem from concerns about whether the product will perform as expected [36; 39]. [PERSON] and [PERSON] [15] empirically verified that the higher the functional risk, the higher the resistance to innovation. Thus:  
**Hypothesis 4**.: _Perceived risk of smart clothing positively influences innovation resistance._
## 3 Results and Discussion
### Scavengers Method  
The photo-generated holes and electrons participate in the Rh-B degradation in the P25/Zn\({}_{0.15}\)Cd\({}_{0.85}\)S nanocomposite photocatalytic system. To confirm the generation of electron-hole pair and explore the mechanism of Rh-B degradation, some scavengers are used in the reaction mixture. Sodium oxalate is used as holes scavenger, KI is used as hole and \({}^{\bullet}\)OH scavengers and K\({}_{2}\)Cr\({}_{2}\)O\({}_{7}\) is used for electrons scavenger [44]. Figure 6 shows the photo-degradation efficiency of Rh-B over P25/Zn\({}_{0.15}\)Cd\({}_{0.85}\)S nanocomposite with P25:Cd ratio of 1:5 in the presence of different scavengers. It can be seen that with the presence of Na\({}_{2}\)C\({}_{2}\)O\({}_{4}\) and Cr (VI) under visible light, the photo-degradation  
Figure 5: Photo-degradation of Rh-B (100 mL, 30 mg/L) by different photocatalysts amounts (**a**) 80 mg and (**b**) 50 mg of various samples  
of Rh-B declines to 48% and 22% after 120 min irradiation, respectively. While in the presence of KI, the photo-degradation of Rh-B decreases to 70%. Moreover, to suppress the electron process Cr(VI) was used as diagnostic tool for the formation of reactive species, the removal efficiency of Rh-B is lower than Na\({}_{2}\)C\({}_{2}\)O\({}_{4}\). From Figure 6, it is observed that the photo catalytically generated electrons and holes radicals that are formed during the reaction in P25/Zn\({}_{0.15}\)Cd\({}_{0.85}\)S nanocomposites are responsible for the dye degradation. There is a high probability for the e\({}^{-}\) generated from Zn\({}_{0.15}\)Cd\({}_{0.85}\)S excitation to migrate towards TiO\({}_{2}\), and thus the generation of h\({}^{+}\) radicals is highly favorable. The excited \({}^{\bullet}\)OH also takes part in the generation of \({}^{\bullet}\)O\({}^{2-}\) radicals. Therefore, the photo-generated electrons play significant role in the degradation system under visible light.
# Blind Recognition of Forward Error Correction Codes Based on Recurrent Neural Network
## 3 The Proposed Approach
### Proposed Deep Neural Network Methodology
#### 3.2.1 Introduction of Simple RNN
Despite these outstanding advantages, the convergence speed of Simple RNN is slow in the training process and there is still a lot of room for improvement in the training speed.
## Appendix B Phase Matching Condition  
With the assumption \(\chi^{(2)}_{zxy}\
eq 0\), the SHG arises from the coupling of the x and y-components of the fundamental mode with the z-component of the SH mode according to:  
\[E^{SH}_{z}=\chi^{(2)}_{zxy}E^{f}_{x}E^{f}_{y} \tag{1}\]  
The Cartesian coordinates are not adapted to the cylindrical symmetry of the system; thus, it is useful to use the circular polarizations instead. A x-linearly polarized photon state can be written as a linear combination of two circularly polarized photon states: \(|x\rangle=A(|+\rangle+|-\rangle)\). For a y-polarization \(|y\rangle=B(|+\rangle-|-\rangle)\), where A and B are normalization constants. Hence, the two-photon state \(|xy\rangle\) used to generate the SH can be rewritten as: \(|xy\rangle=B(|++\rangle-|--\rangle)\). This means that the SHG uses either two photons \(|+\rangle\) or two photons \(|-\rangle\). In the following, we study the case where two \(|+\rangle\) photons interact. The angular momentum conservation, illustrated in Figure 11, leads to the phase-matching condition: \(m_{SH}=2m_{f}+2\).
# Mapping Temperate Forest Phenology Using Tower, UAV, and Ground-Based Sensors
## 1 Introduction
Do above- and below-canopy measures provide similar phenological transition dates as continuous phenological observational data, including oblique-perspective PhenoCam data, and spaceborne, MODIS, and Landsat data?
## 6 Discussion
### Interaction with the Sys-IMF as Decision Support for Concept Decisions  
The application of the Sys-IMF was carried out as part of a single case study using the example of a product family of bearing assemblies of medium voltage electric motors. Here, the interaction with Sys-IMF could be checked and evaluated with experts from industry. A major advantage of the presented method lies in particular in its ease of use and direct access to an extensive database. This allowed users to consider impact chains when evaluating the modular concepts that would not have been taken into account in other assessments. In particular, non-monetary savings are often difficult to measure, in many cases ambiguous, and often only available as tacit knowledge. By applying the method using the IMF as knowledge base, these implicit aspects become explicitly available. This also represents a significant advantage over existing methods of decision support, which either focus only on existing cost accounting types or remain too unspecific.  
A shortcoming of the current use of the Sys-IMF lies in the sole consideration and evaluation of already existing modular product architecture concepts. The feedback of the assessment results to derive specific measures to influence the product architecture is currently not directly possible. This is a multiobjective optimization problem, which has several solutions and could be solved using suitable algorithms (e.g., genetical algorithms) or machine learning methods. The basis for this has already been created with the SysML model, since consistent modeling and traceability of the economic effects to the modular product architecture properties is possible here. However, it is already possible to make initial statements regarding the preferred modular product structuring strategy (commonality = tendency towards standardization/platform; combinability = tendency towards building kit).  
Challenges in using the IMF as a decision support tool include the duplication of process costs and associated process times. The integration of KPIs, for example, as well as the application has shown that some effects influence each other in the sense of the TB-ABC method. Thus, many cost effects are generated directly from the process times (e.g., 3020 and 3030), which are partly not stored consistently in the previous model. This circumstance should be taken into account in further investigations in order to reduce redundant values and the respective calculation efforts. In this context, a clearer distinction should be made between one-time costs (investment costs) and recurring costs (process costs). Both types of costs are included in the extended IMF but are not currently identified as such. Consideration of both types and the cumulative calculation are important as final decision support and should be maintained. Overall, the developed procedure using the Sys-IMF thus provides essential support for the selection of relevant assessment criteria and the evaluation of modular product architecture concepts.
## 3 Results
### Detection with MODIS Images  
The dust storms of 21 February 2015 and 14 February 2018 were analyzed using the Ackerman index on the MODIS images. The results of the analysis of MODIS images of dust storms are shown in Figure 4.  
Figure 3: Horizontal visibility of some synoptic stations in the study area in the 14 February 2018 and 21 February 2015 storms.  
Figure 4: Ackerman Index of Moderate Resolution Imaging Spectroradiometer (MODIS) images of Dust storm on 09:00 UTC, 21 February 2015 (**a**) and 09:00 UTC, 14 February 2018 (**b**) by Ackerman Index.  
The MODIS images show that the dust phenomenon occurred on 21 February 2015 and 14 February 2018 in the study area. These images clearly show the spread of dust in the area. The Ackerman index reaches \(-3.7\) on 21 February 2015, indicating the high intensity and volume of dust concentration in the study area. However, this index reaches \(-2.7\) on 14 February 2018, which indicates a lower volume of dust concentration in the Central Plateau region of Iran.  
Back trajectory analysis supports the origin of air reaching Central Iran during these days (Figure 5). The results show that Arabian deserts affect Iran's atmosphere during dust storms. Figure 5 shows that the storm on 21 February 2015 originated in the Arabian Desert, which can be identified as a storm of outside origin. Additionally, the storm of 14 February 2018, which was observed in the eastern stations of the study area, originated from the southwest of Iran (probably the Arabian deserts).
## 4 Numerical Simulation
### Mechanism Analysis on Noise Reduction
However, for the wavy cylinder, there are two main differences. Firstly, the turbulence intensity is significantly reduced, especially at the trough and peak span locations. Secondly, the location of the peak turbulence intensity zone moves far away from the cylinder surface. Figure 21: Iso-surfaces of the Q-criterion for the baseline cylinder and wavy cylinder. Figure 22: Instantaneous vorticity distribution in the vertical plane. Figure 23: Instantaneous vorticity distribution in different spanwise locations. Figure 17:0 Comparison of the turbulence intensity between the baseline cylinder and wavy cylinder at three different spanwise locations. To further discuss this phenomenon, the velocity fluctuation profile along the streamwise direction and along the transverse direction was presented both for the baseline cylinder and wavy cylinder in Figure 17:1 It can be seen from Figure 25a that, the peak value of the velocity fluctuation locates at about 1.4D downstream of the cylinder for the baseline cylinder and the peak value of the velocity fluctuation is about 90% of the far-field velocity. However, for the wavy cylinder, the peak value of the velocity fluctuation locates at about 3D downstream of the cylinder and the peak value of the velocity fluctuation is about 60% of the far-field velocity. Figure 25b shows the velocity fluctuation distribution in the transverse direction just downstream of the cylinder. It can be seen that the wavy cylinder can also greatly reduce the velocity fluctuation by as much as 44%. For the wavy cylinder, the peak span location has the highest velocity fluctuation and the trough span location has the lowest velocity fluctuation, which is consistent with Figure 17:2 However, irrespective of the peak span location or trough span location, the velocity fluctuation of the wavy cylinder is much lower than that of the baseline cylinder. The reduced velocity fluctuation and the moving far away of the peak velocity fluctuation from the cylinder surface also help to reduction the noise radiation.
## 3 Method  
### Main Process  
In this paper, a new NAS algorithm called CAM-NAS is proposed. Unlike traditional NAS algorithms, CAM-NAS reduces the training steps in the model search process and takes only 0.08 seconds to retrieve, compute, and evaluate the model. The main steps are shown in Figure 1.  
First, a SOTA model is selected as a training model and a small fraction of the images in the dataset is tested with this model. At the same time, the saliency map of the last layer of this model is extracted using the CAM technique. Next, the same image is tested using the model obtained from the search, and the last layer of the saliency map of the submodel is extracted using the same CAM technique. Then, the CAM image of the teacher's model is compared with the CAM image of the submodel and the similarity score is calculated. The CAM technique is able to visualize the model's focus on different features as heat maps by visualizing the activation mapping of the last layer of the neural network. This makes it possible to visualize the distribution of the model's attention on different images, which helps to understand the model's decision-making process.  
Teacher models are typically larger, more complex models, such as deep neural networks, e.g., ResNet-152, BERT, etc. These models are trained on large datasets and are capable of extracting rich feature representations. Student models are typically relatively small, lightweight models, such as shallow neural networks. Teacher and student models are used to transfer knowledge from a complex, large-scale model to a simplified, small-scale model during training, capturing the knowledge of the teacher model in the student model for better generalization performance, higher efficiency, or lower computational costs.  
By comparing the CAM maps of the teacher model and the submodel, the performance advantage of the submodel over the teacher model can be assessed. If the CAM maps of the submodel on the same images have a high similarity to the CAM maps of the teacher model, it means that the submodel is able to capture similar important features as the teacher model and it performs better. Conversely, if the similarity is low, it indicates that the submodel has some performance shortcomings. By evaluating the similarity scores of different submodels on key features, the better-performing submodels can be filtered out and used for the further neural network architecture search and optimization process. In this way, excellent network architectures can be found efficiently without the need to fully train the submodels, saving time and computational resources.
## 4 Optimal Mapping In Dima
### Solution of Single Objective Optimization
#### 4.1.2 Boolean Satisfiability Theory  
Boolean optimization problem, based on boolean satisfiability theory, aims to find a satisfying solution for a given Conjunctive Normal Form (CNF) formula subject to minimizing a given cost function. Constraints can be expressed by means of a propositional boolean formula, which is  
Figure 3: DIMA optimization: mathematical programming framework with incorporation of functional layers profile, system objectives and constraints.  
determined to be satisfiable or not. Typically, SAT-based linear search algorithms and SAT-Based branch and bound algorithms are two common approaches to finding the solution of the optimization problem [51]. A solver for Boolean satisfiability theory is SAT4J [32], which is a mature open-source library in Java. It has been claimed that SAT-based methods are in general faster than generic ILP solvers [52], for example, for the problems of graph coloring and Field Programmable Gate Array (FPGA) routing [53], by capturing the boolean nature of the problem.  
[PERSON] et al. [54] leveraged the SAT4J solver to find resource allocation in IMA system to satisfy safety, performance, maintainability and extensibility requirements; the formulation and method can be adapted in the DIMA architecture. Specifically, the authors considered four objective functions individually to be minimized, including unavailability of devices, bandwidth utilization, number of nodes and number of edges, subject to various constraints, such as availability, bandwidth and segregation constraints:  
* Availability optimization: two formulations are considered, that is, 1) to minimize the sum of route unavailability of data flow (referred to as minsum formulation); 2) to minimize the maximum route unavailability (referred to as minmax formulation).
* Bandwidth utilization optimization: minsum and minmax formulations are considered for the utilization of bandwidth, similar to the availability optimization.
* Number of nodes optimization: to minimize the sum of binary variables for nodes, where the binary variable is defined as an indicator whether a node is being used by data flow.
* Number of edges optimization: to minimize the sum of binary variables for edges, where the binary variable is defined as an indicator whether an edge is being used by data flow.  
SAT4J based on SAT theory is leveraged to solve the minsum formulation, while lp_solve as a Mixed Integer Linear Programming (MILP) solver is leveraged to solve the minmax formulation. The solutions for the two formulations can be different. Regarding the bandwidth consumption optimization, the minsum formulation for bandwidth consumption optimization may result in unbalanced load, while the minmax formulation can be effective to distribute the load among the network. Its performance evaluation will be discussed with details in Section 5.
# Visualization of Distributed Plasticity in Concrete Piles Using OpenSeesPy
## 4 Conclusions  
This study presents a practical and extensible fiber-based modeling framework for RC wharf piles, incorporating 3D nonlinear soil-structure interaction and high-fidelity damage tracking. Implementation of OpenSeesPy, offers theoretical rigor with Python-based post-processing to support performance-based design workflows. The key features of the proposed approach can be summarized as follows:  
* The fiber-element formulation enables distributed plasticity and axial-flexural interaction, which lumped hinge models inherently miss.
* The enhanced nonlinear spring model captures realistic p-y, t-z, and q-z behavior, improving accuracy in structural member demands.
* Post-processing tools allow for detailed extraction and visualization of strain and stress in each individual fiber, demand/capacity ratios (DCRs) at the material level, and curvature and strain profiles along the pile length.
* Visualization capabilities make it possible to directly identify plastic hinge formation, reinforcement yielding, and core concrete crushing with spatial resolution and without reliance on interpretations.  
OpenSeesPy can be seamlessly integrated into engineering workflows. Pile geometries and material properties defined in commercial platforms such as SAP2000 can be transferred into OpenSeesPy using translation scripts, facilitating interoperability. Moreover, Python's flexibility enables integration with optimization routines, parametric studies, and automated documentation. This adaptability opens a pathway for future extensions of the proposed workflow to simulate complex systems such as those that use viscoelastic materials governed by polymer chain dynamics or hybrid energy dissipation devices. The open-source nature of both OpenSeesPy and its associated visualization libraries (e.g., matplotlib and pyvista) further enhances the accessibility and practical adoption of the framework in research and professional practice.
## 3 Results and Discussion
### Soldering Interconnection of the ZnO Nanowires and Ag Solder  
Based on the experimental results in Section 3.1, the laser fluence of 55.25 mJ/cm\({}^{2}\) and laser irradiation time of 30 s is used to interconnect ZnO nanowires. The results are demonstrated in Figure 3. The nano-solder is accurately positioned at the joint of the nanowire using the nanomanipulation platform based on the SEM, as shown in Figure 3b. Due to the high melting point, Figure 3c shows ZnO nanowires undergo no morphological changes after femtosecond laser irradiation, which proves that the nanowires have not been damaged to a large extent. In contrast, Ag nano-solder undergoes a significant morphological change. The nanoparticles are transformed from individual stand-alone to sticky as a whole and bond well, indicating that the Ag nano-solder has melted sufficiently. Meanwhile, the Ag nano-solder is found to be attached to the nanowires with the absence of position change. This phenomenon ensures good soldering interconnection of the ZnO nanowires and Ag nano-solder.  
To better explore the interconnection between nanowires and nano-solder, Figure 4 shows the SEM image of longer irradiation time of 45 s, 60 s, and 75 s, respectively. Figure 4a-c shows the morphology of the interconnection joint before irradiation. Figure 4 shows that the Ag solders have all melted to a greater degree, and the morphological change of the nanoparticles becomes more evident as the irradiation time increases. As shown in Figure 4d,e, the better soldering interconnections have been realized. However, Figure 4f  
Figure 3: SEM images of the interconnection melting state at 55.25 mJ/cm\({}^{2}\). (**a**,**c**) are SEM images of ZnO nanowires-Ag solder interconnection after irradiation; (**b**) SEM images of ZnO nanowires and Ag solder interconnection before irradiation.  
Figure 2: SEM images of Ag nano-solder before and after femtosecond laser irradiation with different fluence: (**a**,**e**): 29.25 mJ/cm\({}^{2}\); (**b**,**f**): 42.25 mJ/cm\({}^{2}\); (**c**,**g**): 55.25 mJ/cm\({}^{2}\); (**d**,**h**): 68.25 mJ/cm\({}^{2}\).  
shows that the ZnO nanowires are broken after 75 s of femtosecond laser irradiation. The analysis goes that the plasmon effect between the Ag nanoparticles is stronger under the long-term irradiation of the femtosecond laser, resulting in the transmission of a considerable amount of heat to the nanowires through Ag nanoparticles. The nanowires appear to be ablated and cracked after being heated. The phenomenon also indicates that, even if the ZnO nanowire's melting point is high, the photothermal effect is sufficient to destroy the nanowires when exposed to a long-term irradiation time. By comparing the melting state of the Ag solder at different irradiation times, it can be discovered that the melting of Figure 4e is more pronounced, and the Ag solder achieves better melt fluidity, which is more favorable for nanowire soldering. The above experiments prove that a femtosecond laser fluence of 55.25 mJ/cm\({}^{2}\) and an irradiation time of 60 s are more effective for soldering interconnection of ZnO nanowires.