esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Fn Application and Involvement in Biomaterials and Medical Device Engineering
### Fn 2D Molecular Coatings
#### 3.1.2 Fn Physical Adsorption
showed that Fn adsorption on TCPS increases monocyte adhesion, but the release of the proinflammatory cytokine TNF\(\alpha\) and the level of foreign body giant cell formation are lower compared to those for TCPS coated with IgG [69]. Fn coatings on specific polyurethanes were also explored in the context of vascular applications. The results showed that the specific cell-binding sequences of the Fn are less exposed to cell receptors that could reduce the immune cell response and inflammation [70]. To enrich biomaterial surfaces with Fn, another strategy consists of the use of aptamers to enhance Fn retention. Aptamers are small single-stranded oligonucleotides that overcome a specific and defined 3D conformation and specifically bind a target molecule. They are thus good candidates for therapeutic purposes in the same way as antibodies but without their disadvantages, which are their immunogenicity and low stability [71]. The functionalization of biomaterials with aptamers could thus control the adsorption of proteins that occurs shortly after implantation by selectively binding a target protein. [PERSON] et al. used aptamers directed against Fn to promote the specific adsorption of the protein and to enhance the cell colonization of chitosan films. They showed that the use of aptamers had no influence on the amount of Fn adsorbed on the surface but improved osteoblastic and human epithelial cell adhesion, proliferation, and spreading [72,73]. Aptamers thus help to maintain Fn in a functional conformation that enhances the cell response at the interface. To obtain multifunctional coatings, Fn can also be co-immobilized with other molecules. In the context of cardiovascular implants, for a family of biomaterials in direct contact with blood, the main issues are long-term implantation and implant failure due to thrombus formation and the absence of endothelialization. Fn is co-adsorbed or co-immobilized on a fluorocarbon polymer or titanium in association with phosphorylcholine or heparin, known for their antithrombogenic and anticoagulant properties. It has been shown that the association of Fn with these molecules could improve the endothelialization process and prevent thrombus formation [74,75]. Fn can also be co-adsorbed with plasma proteins and generate a synergetic effect. This is the case, when Fn is co-adsorbed with albumin; the association of the two proteins modulates preosteoblastic cell adhesion, spreading, and proliferation and may enhance fibroblast adhesion [76,77, 78].
## 3 Results
### Forward Motion  
Version one of the Crawler Unit was tested in performing a forward and backward motion on a flat terrain. This experiment aimed at verifying the effectiveness of the drive systems in concurrently driving the active tracks. Additionally, such an experiment provided preliminary data on the maximum longitudinal velocity that could be achieved. It is worth mentioning that the modular design of the Crawler allows a certain degree of customization. For instance, it is possible to replace the DC motors or the drive system transmission ratio in order to achieve higher torques or higher velocities. Referring to Figure 7, the test consisted in moving the Crawler from its starting position toward the finish line, which was placed 1 m forward. The system covered this distance in \(t=10\) s, achieving an estimated average velocity \(v=0.1\) [m/s].  
As shown in Figure 8, the same experiment was repeated for version two of the Crawler Unit. From data analysis of 3 trials, it was possible to conclude that the new version demonstrated similar results. The active tracks achieved the average forward speed \(v=0.107\) [m/s] with standard deviation of \(\sigma=0.002\) [m/s]. The robot traveled 1 m forward in \(t=9.3\) s.  
During these experiments, both versions demonstrated that the external polyurethane layers of the tracks did not provide a high friction coefficient over the linoleum floor. In addition, the internal friction of passive tracks was higher than expected. As a result, the passive tracks slipped on the ground rather than rotating. However, this represents a minor issue, which can be solved by coating the external layer of each track with a more adhesive material.  
Figure 7: Forward motion sequence. The Crawler Unit version one travels 1 m forward from the starting position, on the left, toward the finish line, on the right. The robot achieves an average speed of \(v=0.1\) [m/s].
# Mapping Winter Wheat with Multi-Temporal SAR and Optical Images in an Urban Agricultural Region
## 4 Results and Discussion
### Analysis of Temporal Variables Used for Classification
#### 4.1.2 Temporal Variables Extracted from Optical Data  
Figure 4 shows the time series of the mean SR and NDVI for each land cover class. As observed in this figure, the SR values of wheat increased first and then decreased (in the range of 3.0 to 4.1), with the highest value of 7.4 occurring on 28 March 2016. The trends of SR and NDVI curves were basically the same; at the end of March there was a peak for winter wheat, and rapeseed had a valley. During this period, rapeseed entered the flowering stage, which contributed to the two vegetation indices' values decreasing, while after the flowering period the two vegetation indices' values increased, indicating that winter wheat had the highest separability at the end of March. The vegetation indices' values over the water body and urban areas were very low. On the other hand, the forest indices' values were less than that of the corresponding winter wheat. Our results indicate that the time series of the SR and NDVI values of each land cover class were obviously different, which has been widely used in crop remote sensing classification [47].  
Figure 4: Average SR and NDVI values for each land cover class on four image acquisition dates.  
Figure 3: Average coherence values for each land cover class. Note: the meaning of the notations 1, 2, 3, 4, and 5 can be found in Section 3.2.2.
## 2 Materials and Methods  
### Crack Sensor Data  
To evaluate our approach, we aggregate crack sensor data from various locations in South Korea. We consider two kinds of infrastructure for this study: an underground facility and a bridge. We collect the sensor data within different time frames and with different sampling rates. The data used in this research had been provided by Infranics Co., Ltd. in Seoul, Korea, a software company working with KT Corporation (formerly Korea Telecom, Korea's largest telecommunications company). Due to confidential reasons, we do not disclose the types and the positions of the sensors.  
In this paper, we report the data in millimeter measurements with varying sample rates. Due to the continuous form of the data, we perform several time-series related data preprocessing methods, described in Section 3.1.
# Reference Models for Digital Manufacturing Platforms
## 3 Reference Models Alignment
### Reference Model for Industrie 4.0 (RAMI 4.0)
#### Administration Shell Specifications  
The German Federal Ministry of Economic Affairs and Energy [34] provides specifications for the exchange of information with the administration shell. The Open Platform Communications Unified Architecture (OPC UA) is the core communication standard for I4.0 compliant communications. Bothclient/server and publish/subscribe communication patterns are supported, as highlighted in [34]. Figure 2 shows the I4.0 communication protocol stack.  
The administration shell also defines the data models for the exchange of information between partners in the value chain. The specification defines the structure of the administration data models. The main components and standards used are:  
* Concept: ISO 13584 [35] Industrial automation systems and integration standard is the basis for the conceptual definition of the asset and its parts.
* Design: AutomationML [36] (a standard-based mark-up language to exchange design data) and the corresponding mapping to OPC UA [37] is used to share product engineering data, particularly during the product type development phase.
* Production: OPC UA Information models are used to exchange production operations data during the product instance Production and Usage phases.
* Serialization: Both eXtensible Markup Language (XML) and JavaScript Object Notation (JSON) schemas are derived from the models described above.
* Mapping: Resource Description Framework (RDF) is used to map this information and enable the exchange of information using semantic technologies.  
Regarding information access control, the specifications define an Attribute Based and Role Based Access (ABAC) model for access control to information. ABAC allows establishing different access policies for different user roles for the different elements of the information model of an administration shell.  
Additionally, the specification defines a package file format, the Asset Administration Shell Package (AASX), to exchange the full or partial structure of the administration shell.
###### Abstract  
As green energy technology develops, so too grows research interest in topics such as solar power forecasting. The output of solar power generation is uncontrollable, which makes accurate prediction of output an important task in the management of power grids. Despite a plethora of theoretical models, most frameworks encounter problems in practice because they assume that received data is error-free, which is unlikely, as this type of data is gathered by outdoor sensors. We thus designed a robust solar power forecasting model and methodology based on the concept of ensembling, with three key design elements. First, as models established using the ensembling concept typically have high computational costs, we pruned the deep learning model architecture to reduce the size of the model. Second, the mediation model often used for pruning is not suitable for solar power forecasting problems, so we designed a numerical-categorical radial basis function deep neural network (NC-RBF-DNN) to replace the mediation model. Third, existing pruning methods can only establish one model at a time, but the ensembling concept involves the establishment of multiple sub-models simultaneously. We therefore designed a factor combination search algorithm, which can identify the most suitable factor combinations for the sub-models of ensemble models using very few experiments, thereby ensuring that we can establish the target ensemble model with the smallest architecture and minimal error. Experiments using a dataset from real-world solar power plants verified that the proposed method could be used to build an ensemble model of the target within ten attempts. Furthermore, despite considerable error in the model inputs (two inputs contained 10% error), the predicted NRMSE of our model is still over 10 times better than the recent model.  
solar power forecasting; robust prediction; lightweight deep learning model +
Footnote †: journal: applied sciences
## 2 Methods  
### Thin Film Thickness Uniformity Simulation  
The optical simulation software LightTools is used to reconstruct the 3-D model to restore the geometric position of the vapor deposition machine to the same scale. The geometric position is converted into a coordinate system. The flux intensity is obtained through a large number of light tracing methods which can equivalent to the thin film deposition process of electron gun vapor deposition. The simulation results of 50 million rays are compared with the experimental results. The coating holder is divided into five different segments. The coating dome with 900 mm in diameter is rotated one circle to take 16 sampling points as a cycle based on an optical flux mapping method. The thin film thickness distribution and deposition curve for all sampling points can be analyzed. In the simulation program, the film thickness deposition curve and the coating mask are definite integrals by polynomial calculations. The result after subtracting the two is the deposition thickness of the dome per revolution. By the ratio of the simulation values to the experimental results, the thickness of the deposition film is adjusted when the dome rotates for one circle. If the difference between the two results is too large, the intensity of the apodized light source needs to be readjusted for the simulation. If the simulation result matches the experimental result, then the simulation is complete. We compare the simulation and experimental results. The geometric size of the mask can be adjusted to reduce the cost and save the experimental time. Figure 1 shows the flow chart of film thickness uniformity simulation for the coating experiment.  
The uniformity of film thickness can be characterized by the following Equation [20]:  
\[\mathrm{U}=\frac{\mathrm{t}_{\mathrm{max}}-\mathrm{t}_{\mathrm{min}}}{ \mathrm{t}_{\mathrm{max}}+\mathrm{t}_{\mathrm{min}}}\times 100(\%) \tag{1}\]  
where U is the uniformity of film thickness. \(\mathrm{t}_{\mathrm{max}}\) and \(\mathrm{t}_{\mathrm{min}}\) are the maximum and minimum values of film thickness, respectively.
# Port-Related Shipping Gas Emissions--A Systematic Review of Research
## 3 Discussion and Results
### Analysis of the Most Commonly Applied Methodologies and Datasets--Third Layer
#### 3.3.2 US EPA Research  
The purpose of US EPA 20 was to provide guidance for the development of a mobile source port-related air pollution emissions inventory within a designated area in a given time period. This document supersedes the previous April 2009 document US EPA 09 [80]. For the ocean-going vessel (OGV) sector, a bottom-up EB emission estimation methodology was presented, according to which both AIS and traffic statistics data could be applied using Equation (6). According to this document, the information necessary for emission calculations includes engine characteristics (that describe engine power, type, age, speed and category), ship speed, position and course. From the mentioned data, EF and LF can be obtained. In this publication, five different movement activities have been recognised (Transit, Manoeuvring, Restricted Speed Zone, Hotelling, Anchorage) and defined by LF. To obtain the value of LF, the propeller law is used. In the end, when actual activity is recognised, the predefined low load adjustment factors (LLAFs) can be applied [80].  
\[\text{E}=\text{P}\times\text{A}\times\text{EF}\times\text{LLAF} \tag{6}\]  
where:  
E: Emissions per vessel by mode--in grams (g);  
P: Engine operating power--in kilowatts (kW);  
A: Engine operating activity--in hours (h);  
EF: Emission factors of different pollutants in regard to engine group, engine type, fuel type, keel laid--in grams per kilowatt hour (g/kWh);  
LLAF: Low load adjustment factor, a unitless factor that reflects increasing propulsion emissions during low load operations--always 1 for auxiliary engines and boilers.
# A Study on the Softening Shear Model of the Energy Pile-Soil Contact Surface
## 3 Results
### Softening Shear Model of Pile-Soil Contact Surface under Large Shear Displacement Conditions
#### 3.5.2 Model Parameters and Physical Meaning  
According to the characteristics of the softening feature and the model function, the softening function needs to satisfy the following three conditions:  
\[\left\{\begin{array}{l}\tau(s_{p})=\tau_{p}\\ \tau(s_{p})=0\\ \tau(s_{r})=\tau_{r}\end{array}\right. \tag{11}\]  
Figure 15: Softening shear model for pile–soil contact surface.  
By performing a substitution into Equation (10), the following solution is obtained:  
\[a=s_{u}\bigg{(}\frac{1}{4}-c\bigg{)}=s_{u}\bigg{(}\frac{1}{4}-\frac{[2\alpha- \beta(\alpha+1)]-2\alpha\sqrt{1-\beta}}{4\beta(\alpha-1)}\bigg{)} \tag{12}\]  
\[b=\frac{1}{4}+\frac{[2\alpha-\beta(\alpha+1)]-2\alpha\sqrt{1-\beta}}{4\beta( \alpha-1)} \tag{13}\]  
\[c=\frac{[2\alpha-\beta(\alpha+1)]-2\alpha\sqrt{1-\beta}}{4\beta(\alpha-1)} \tag{14}\]  
In the equation, \(\alpha\) represents the ratio of the shear displacement \(s_{r}\) at which the residual strength is reached to the shear displacement \(s_{p}\) corresponding to the peak strength. \(\beta\) represents the ratio of the residual strength \(\tau_{r}\) to the peak strength \(\tau_{p}\).  
From the above analysis, this softening function can be determined by \(s_{p}\), \(\tau_{p}\), \(\alpha\), and \(\beta\), which are the four physical parameters. The peak strength of the pile-soil contact surface can be expressed with the Mohr-Coulomb strength criterion as follows:  
\[\tau_{p}=\sigma_{r}\tan(\delta_{p})+c_{p} \tag{15}\]  
In the equation, \(\sigma_{n}\) indicates the normal effective stress corresponding to the peak shear stress at the contact surface, kPa. \(\delta_{p}\) is the effective friction angle corresponding to the peak shear stress at the contact surface, \({}^{\circ}\). \(c_{p}\) indicates the effective cohesion of the contact surface, kPa.  
Peak strength is directly proportional to the normal effective stress; therefore, the model not only characterizes the shear displacement relationship under the condition of constant normal stress, but also considers the change in normal stress during the shear process.  
The peak stress at the pile-soil contact surface corresponds to a shear displacement \(s_{p}\), which is linearly related to \(\lg(\sigma_{n}/P_{a})\). The relationship can be written as follows:  
\[s_{p}=k_{p}\lg(\sigma_{n}/p_{a})+b_{p} \tag{16}\]  
\(R_{n}\) is the roughness of the pile-soil contact surface. \(D_{r}\) is the compactness of the soil. \(P_{a}\) is the reference pressure which is equal to 101.325 kPa.
# 3D Path Planning Algorithms in UAV-Enabled Communications Systems: A Mapping Study
## 5 Discussion  
This section focuses on the trends and research gaps related to 3D path planning in UAV-enabled communications systems. One notable research gap we identified is the lack of consideration for multiple UAVs in most analyzed articles. While UAV networks have the potential to enhance coverage and communication capabilities, most studies focused on single UAV scenarios. It highlights the need for further research to explore the challenges and opportunities of path-planning algorithms for multiple UAVs, including coordination and collision avoidance. By addressing these aspects, the efficiency and effectiveness of UAV-enabled communications systems can be significantly improved.  
Another research gap is the limited attention given to dynamic obstacles. Many analyzed papers either ignored dynamic obstacles altogether or only considered static obstacles. However, real-world scenarios often involve dynamic elements like other UAVs from different networks. Incorporating the ability to perceive and respond to dynamic obstacles is crucial for safe and reliable UAV operations. Future research should emphasize the development of path-planning algorithms that can dynamically detect and avoid static and dynamic obstacles, enhancing the robustness and adaptability of UAV-enabled communications systems.  
In terms of objectives to be optimized, the trends among researchers primarily focused on minimizing time, cost, and energy. Time optimization is essential for efficient UAV operations, ensuring timely delivery of communication services or completing missions within specified timeframes. Cost optimization considers computational, hardware, and overall mission costs to achieve cost-effective solutions. Energy optimization is paramount as it directly impacts UAVs' endurance and operational range. By minimizing energy consumption, UAVs can prolong flight duration and provide extended communication coverage.  
Furthermore, the trends among researchers regarding the variables to be optimized highlighted minimizing altitude and distance. These objectives align with the need for UAVs to operate efficiently, conserve energy, and optimize communication coverage. Minimizing altitude helps reduce power consumption and enhances flight stability while minimizing distance allows for shorter communication links and faster data transmission. However, it is worth noting that specific objectives related to in-flight communications, such as data rate, outage probability, and power transmission, have not been thoroughly analyzed in the context of path planning, as shown in Figure 15. Future research could explore these objectives' optimization potential and impact on overall communication system performance.  
Figure 15: Objective to be Min-max optimized vs. Optimization Variable
# Texture and Materials Image Classification Based on Wavelet Pooling Layer in CNN
## 3 Materials and Methods
### Wavelet Analysis
#### 3.1.2 Pooling Method  
The pooling method is described by some authors as a subsampling methodology [12; 26; 27; 28]. The pooling method also transforms the activation map into a new feature map. The poolingoperation works on small regions of size \(P_{q}\times P_{q}\), usually after each convolutional layer. The pooling method has two main purposes. The first is to reduce the number of parameters and, thus, reduce the computational cost. The second is to control overfitting [9, 17]. The expectation is that an ideal pooling method extracts only useful information and discards irrelevant details [13]. In general, pooling takes two forms that are most commonly used: Max-Pooling (MaxP) and Ave-Pooling (AveP) [9, 28, 29, 30, 31, 32, 33].  
These forms of pooling are deterministic, efficient, and simple but have shortcomings that hinder the learning potential of CNN. Depending on the data, Max-Pooling can erase details from an image [9]. Hence, this happens if important details have less intensity than insignificant details. Moreover, it generates noise accumulation, and it is not possible to restore lost information [16]. Moreover, Max-Pooling is sensitive to overfitting the dataset used for training and hinders generalization [34]. Average pooling, depending on the data, can dilute the relevant details of an image. Averaging data with values far below important details cause this action [9]. Figure 1 illustrates these shortcomings with the example of a toy image.  
If we consider DWT filters as convolutional filters with predefined weights, then we can observe that DWT is a particular case of FCN (Fully Connected Layers) without the layers of nonlinearity. The original image can be decomposed by DWT and then reconstructed exactly by the DWT inverse without losing information [18]. On the other hand, the wavelet theory opens the possibility to represent the image details inside learning CNNs, thanks to the frequency and location features generated by the wavelet transform (see Figure 1[17]).