esa-sceva/satcom-chunk-collection huggingface dataset:
# Utilizing Soft Computing Techniques to Estimate the Axial Permanent Deformation of Asphalt Concrete
## 6 Conclusions  
This study aimed to utilize the ANN prediction tool to evaluate the influence of various mixing characteristics and testing conditions on the axial permanent strain (\(\varepsilon\)p) of asphalt mixes by developing a robust model and conducting sensitivity analysis. To measure the mixes 'axial permanent deformation, a pneumatic repeated load system (PRLS) was used to test 306 laboratory-produced asphalt concrete specimens of different asphalt cement types and aggregate gradations (\(\varepsilon\)p). Twelve input variables were utilized to develop the model. They include the number of applied cyclic normal stress (n), testing temperature (Temp.), applied normal stress (Stress), asphalt contents (AC), asphalt viscosity (Vis), percentage of aggregate passing \(\
icefrac{{1}}{{2}}\)\" sieve (PP1/2) and 4.75 mm sieve (PPNo.4), air void contents (AV), percentage of voids in mineral aggregate (VMA), theoretical maximum specific gravity of asphalt mixes (Gmm), bulk specific gravity of asphalt mixes (Gmb), and bulk specific gravity of combined aggregate (Gsb). Some findings and insights from the study are summarized as follows:  
1. The ANN model accurately predicted axial permanent strain (\(\varepsilon\)p) in asphalt mixtures, achieving an R\({}^{2}\) value of 0.9305 and an MSE of 0.0039.
2. Seven hidden neurons were included in the optimal ANN model, resulting in reliable and consistent outcomes, making the model effective in predicting permanent deformation under various conditions.
3. Based on the sensitivity analysis, aggregate passing the 4.75 mm (PPNo.4) sieve and Gmm were the most influential factors in predicting rutting, with 13% and 11.8% contribution rates, respectively.
4. Permanent deformation increased with an increase in the number of applied loads and high testing temperatures, demonstrating the importance of designing asphalt mixtures based on temperature and load resistance.
5. Input impact analysis showed that the normal stress levels, air voids (AV), and aggregate bulk specific gravity (Gsb) had an adverse impact on permanent deformation, while factors like temperature, asphalt content (AC), and gradation (PP1/2, PPNo.4) contributed positively to \(\varepsilon\)p values.  
Figure 8: Impact of input variables in the model.
# Haze-Aware Attention Network for Single-Image Dehazing
## 2 Related Work  
### Prior-Based Image Dehazing  
Research on single-image dehazing in computer vision and computer graphics has been widely explored. Traditional methods have relied on priors such as the dark channel prior (DCP) [12], color attenuation prior [20], and nonlocal prior [21] to estimate scattering light, atmospheric light, depth, and transmission map [12, 15, 20, 21, 22]. These methods are backed by strong principles and are interpretable. However, they may not perform well in real-world image dehazing scenarios because they only extract features based on the atmospheric scattering model at the image level, without accessing deep latent features. The Atmospheric Scattering Model (ASM) has been the cornerstone for many previous works in constructing image dehazing networks. These methods have explicitly incorporated the ASM to enhance the generalization capability of their models, thus thoroughly validating the effectiveness of the ASM. In contrast to these approaches, we introduced the ASM at the feature level, thus leveraging it to learn more latent features. Additionally, we allocateda substantial number of channels for the atmospheric light value A, thus aiming to better adapt to the complexities of real-world scenarios.
# Additively Manufactured Polymers for Electronic Components
## 4 Applications in the Electronic Field of Additively Manufactured Polymers
### Organic Solar Cells  
Organic solar cells (OSCs) are a promising class of photovoltaic devices that utilise conjugated polymers and small molecules as photoactive materials and electrodes. Their intrinsic advantages include mechanical flexibility, lightweight architecture, and the possibility of low-temperature, solution-based processing, enabling scalable fabrication on plastic substrates through printing and roll-to-roll techniques [166]. Typical OSC architectures employ polymer donors such as P3 HT blended with fullerene-based materials (PCBM), forming bulk heterojunction (BHJ) active layers, while transparent conducting electrodes can be produced with printable materials like PEDOT:PSS or printed metallic grids [167].  
AM methods have recently been explored to leverage the solution-processable nature of OSC materials for cost-effective and patternable device fabrication. Inkjet printing and EHD printing are among the techniques capable of depositing functional layers with controlled thickness and morphology. For instance, [PERSON] et al. demonstrated the use of EHD printing for a controlled deposition of P3 HT:PCBM active layers, enabling microstructured photoactive films via high-resolution spraying [168]. Similarly, fully inkjet-printed OSC stacks have been reported, where all layers, including the ITO-free transparent electrode, hole transport layer (PEDOT:PSS), BHJ layer, and top electrode, were patterned in air at room temperature [169]. Moreover, AM technologies can enable the fabrication of unconventional device geometries and 3D-structured electrodes. Despite progress, printed  
Figure 14: (**a**) Optical image of 3D-printed transparent OLED pixels (scale bar: 40 \(\upmu\)m). (**b**–**e**) Atomic force microscopy (AFM) images of the red-boxed area in (**a**) after each fabrication step: (**b**) pixel patterning, (**c**) deposition of PEDOT:PSS, (**d**) coating with SPW-111 co-polymer, and (**e**) 3D printing of polyphosphazene fluoroelastomer (scale bars: 10 \(\upmu\)m). (**f**) Cross-sectional profiles corresponding to the AFM images shown in (**b**–**e**). (**g**) FIB-scanning electron microscopy (SEM) images of the 3D-printed transparent OLEDs. The right image is a magnified view of the blue boxed region from the left image (scale bars: 1 \(\upmu\)m and 500 nm, respectively). Adapted and reprinted with permission from ref. [165].  
OSCs still lag in efficiency and durability. Bench-top OSCs have reached ~19-20% of power conversion efficiency (PCE), but fully printed devices usually show much lower PCE (a few percent) [136; 170].
## 4 Buried Electrode Scheme Design and Performance Analysis  
The current field of the through-the-earth communication system uses electric dipoles to form a loop with the ground, applies a voltage to the loop to obtain an alternating current, and generates a current field in the ground, thereby achieving through-the-earth communication. In the information transmission method based on the ground electrode current field, the grounding method of the antenna is a key factor affecting the information transmission performance. The current intensity formula of the system can be expressed as: [29]  
\[I=\frac{U}{R}=\frac{P}{U}=\sqrt{\frac{P}{R}} \tag{5}\]  
In Formula (5):  
\(I\) is the emission current (A);  
\(U\) is the emission voltage (V);  
\(R\) is the point dipole ground impedance (\(\Omega\)); and  
\(P\) is the transmit power (W).  
It can be seen from Equation (5) that signal enhancement can be achieved by increasing the power and reducing the grounding resistance. Since the power applied to the electric dipole cannot be infinitely increased, and the safety standard in the tunnel also imposes strict requirements on the transmission power, excessive power cannot be used for transmission. Therefore, a feasible signal enhancement solution is to reduce the ground impedance of the electric dipole antenna.  
Its single-ended ground impedance is mainly composed of the impedance of the wire connecting the transmitting device and the electric dipole electrode \(Z_{w}\), the contact impedance \(Z_{c}\), and the ground impedance \(Z_{e}\) in series, as shown in Formula (6): [30]  
\[Z=Z_{e}+Z_{w}+Z_{c}=R+jX \tag{6}\]  
Among the above resistors, the wire impedance corresponding to electric dipole electrodes \(Z_{w}\) can be optimized by using high-performance transmission lines. The earth impedance \(Z_{e}\) is related to the medium resistivity, which is not a man-made controllable factor. Therefore, reducing the ground impedance of the electric dipole antenna should mainly start with contact impedance \(Z_{c}\).  
There are many ways to reduce \(Z_{c}\). The purpose of reducing the grounding impedance can be achieved by increasing the number of grounding electrodes and increasing the depth of soil penetration.
## 4 Results and Discussions
### Effect of CO\({}_{2}\) Injection on Coalled Methane Production
#### 4.1.2 Variation of CH\({}_{4}\) Concentration  
Figure 10 illustrates the distribution of CH\({}_{4}\) concentration. During the direct exploitation (Figure 10a), the CH\({}_{4}\) concentration first changes in the area near the production well and continues to extend into the reservoir. The CH\({}_{4}\) in the reservoir will continuously migrate to the production well because of the pressure differential. However, the pressure differential between the reservoir and the production well is relatively small, the gas migration speed is slow, and the CH\({}_{4}\) concentration changes gradually.  
Figure 8: Distribution curve of reservoir gas pressure along the model diagonal (**a**) _CBM_ (**b**) _CO\({}_{2}\)-ECBM_.  
During CO\({}_{2}\) injection mining (Figure 10b), due to the injection of CO\({}_{2}\), the pressure differential from the injection well to the coal seam, to the production well is higher than that of the direct exploitation. Therefore, at the same time, the change in CH\({}_{4}\) concentration alone with CO\({}_{2}\) injection exploitation is higher than that of direct exploitation. Near the injection well, preferential adsorption of CO\({}_{2}\) to the coal seam after injection forces the desorption of adsorbed CH\({}_{4}\). The desorbed CH\({}_{4}\) migrates to the production well under the effect of pressure differential, thus the CH\({}_{4}\) concentration near the injection well is also reduced.
## 4 Application
### Knowledge Representation  
Figure 12 shows an excerpt from the ontological knowledge representation visualized as a graph. Every annotation is of the Design Process or Design Task type, since a design process consists of different design tasks executed here by solution principles represented in a python script. For instance, the individual Anthropometric measures represent an annotation of the Design Task type, which links the individual FitCrankLength with an individual CrankLength of the Terminal State type. As described above, the input of the python script is input rationale derived from the requirements, in this case the individual's Femur and Fibula lengths, which are anthropometric data. In this case, an individualized crank length results; thus, the individual CrankLength is of the Terminal State as well as Design Parameter types, since it is the only design parameter transformed in this state. The situation is different to the individual Slot, which is an extrusion feature in CAD and, thus, is composed of a 2D sketch and material removal of a certain depth. Therefore, for instance, the individual's SlotLength, SlotDepth, SlotRadius, and CrankDepth of the Design Parameter type are transformed by the python script to fit the slot extrusion. In this way, information about the transition from one state to another is represented as linked data.  
Figure 11: Illustration of an example of the instantiation of a Design Task from data tables.  
Figure 12: Excerpt from the visualization of the ontological knowledge representation.  
The resulting exemplary knowledge representation shown in Figure 12 enables the automation of the entire MC process from a standard crank (Initial State) to an custom bicycle crank (Terminal State). By utilizing Design Rationale (Input and Output Rationale for Transition), the relational conditions of Design Processes, and the solution principles of Design Tasks (FitCrankLength and FitSlotParameter), new individual values for requirements (e.g., Femur, Fibula,...) are linked with Design Parameters (e.g.,: CrankLength). Thus, every modification is traceable in the hierarchical axis of the MC process, and the design intent is explicable. In addition to this, relating the Design Parameters to Design Rationale makes the sequence of the modifications applicable for design automation, as the modifications can be filtered by the related Design Tasks, which are executed by the underlying scripts. As an example, removing all relations associated with the second Design Task (Lightweight), results in the Intermediate State presented in Figure 8. Therefore, the prescribed order of States forms the chronological axis of the MC process. The advantage and difference to previous approaches is that not only are the individual Design Tasks automated but their context is used to ensure their seamless linkage.
## 4 Analysis of the Results
### Analysis of the Experimental Results
#### 4.2.2 Quantitative Analysis
In summary, this article compares the evaluation results of the four methods of segmentation results, as shown in Table 1.  
The experimental results demonstrated that for the segmentation of juxtapleural nodules, both the method of this paper and the rolling-ball method were more efficient than the regional growth and watershed methods. In addition, the segmentation time of the method proposed in this paper was much lower than that of the other three methods.
# Non-Uniform Clustering Algorithm for UWSNs Based on Energy Equalization
## 5 Simulation Results  
The effectiveness of this algorithm is verified through simulation experiments. A comparison is made between this algorithm and UCUBG, LEACH and DBR algorithms on the MATLAB platform. In reference [25], the MATLAB software is described in detail. Simulation is conducted in the form of \"rounds\", each of which is classified into network cluster formation and data transmission phases, as well as network maintenance nodes. The network data transmission phase involves collecting information from nodes that are within the cluster, transmitting them to the cluster head, and completing the multi-hop forwarding of data between clusters. The network maintenance phase mainly re-clusters problematic clusters. Table 2 shows the parameters of simulation experiments.
## 2 Preliminaries
### Examples for Choice Functions
_Weak hierarchical choice function_: Agent \(v\) has a weak preference order (ties are allowed) over the contracts, and there is a downward closed set system, \(\mathcal{I}\), of subsets of \(E(v)\). Let \(k\) be the greatest number, such that the set of \(k\) best contracts of set \(A\) is in \(\mathcal{I}\), and among equally good contracts, we choose all or none. Now, \(C(A)\) is the set of these \(k\) best applicants. **Statement 1**. : _If the costs are increasing (a contract with a lower score is more expensive), then the weighted scoring choice function is path independent._  
Proof. : From set \(A\), the college's preference order is \(c_{1}\geq c_{2}\cdots\geq c_{n}\) (contract \(c_{1}\) has the best score; \(c_{n}\) has the worst). If \(F(A)\subseteq B\subseteq A\) and \(F(A)=\{c_{1},c_{2}\ldots c_{k}\}\), then by definition, the college cannot add the next best applicant, \(c_{k+1}\), to \(F(A)\). Other contracts in \(B\setminus F(A)\) are more expensive than \(c_{k+1}\), so the college cannot choose any of them. Therefore, \(F(B)=F(A)\). **Definition 6**. : Assume that each contract, \(c\), has some score, \(s(c)\). A choice function, \(F\), is _loser-free_ if any rejected contract has a lower score than any accepted contract. That is, \(s(c^{\prime})<s(c)\) holds whenever \(c\in F(X)\) and \(c^{\prime}\in X\setminus F(X)\). Note that the above Examples 1, 2, 4 and 7 are path independent. All of the above examples are substitutable and loser-free. **Remark 1**. : _Any weighted scoring choice function is weak hierarchical, and any weak hierarchical is loser-free. However, not every loser-free choice function is weak hierarchical:_  
**Example 1**. : _If \(a>b>c\) and \(F(A)=A\) if \(|A|\leq 2\) and \(F(\{a,b,c\})=\{a\}\), then F is loser-free and substitutable. However: \(F(\{a,b\})=\{a,b\}\), so \(\{a,b\}\) is supposed to be a set in \(\mathcal{I}\); however, \(F(\{a,b,c\})\
eq\ \{a,b\}\), so this function is not hierarchical._  
_Not every weak hierarchical function is a weighted scoring choice function:_  
**Example 2**. : _The set of contracts is \(E=\{a,b,c,d\}\); the preference order is \(a>b>c>d\), and \(\mathcal{I}=\{\emptyset,\{a\},\{b\},\{c\},\{d\},\{a,b\}\{c,d\}\}\). Therefore, for example, \(F(\{a,b,c\})=\{a,b\}\). However, if we want to describe it with weights: the weight limit is \(Q\). Sets \(\{a,b\}\) and \(\{c,d\}\) are under \(Q\), but \(\{a,c\}\) and \(\{b,d\}\) are \"heavier\" than \(Q\). Therefore, \(\{a,b,c,d\}\) are under and above \(2Q\) at the same time. This is a contradiction._
# The Effect of Subgrade Coefficient on Static Work of a Pontoon Made as a Monolithic Closed Tank
## 1 Introduction
It consists of determining an elastic layer, the deflection of which is determined approximately using detailed material data describing this layer [11]. Two-parameter substrate models such as Vlasov, Pasternak or Gorbunov--Posadov systems of equations are much less frequently used, due to the difficulty in making calculations with their use [13, 14, 15, 16]. The differential equation of a plate resting on a one-parameter Winkler substrate is described by Equation (2). \[\mathrm{D}\
abla^{2}\
abla^{2}\mathrm{w}+\mathrm{K_{z}}\mathrm{w}=\mathrm{q}- (1+\mathrm{v})\frac{\mathrm{D}\alpha_{\mathrm{t}}}{\mathrm{h}}\
abla^{2} \Delta\mathrm{T} \tag{2}\]  
where  
Figure 1: Winkler substrate model (drawn by the author). \(\mathrm{D}=\frac{\mathrm{Eh}^{3}}{12(1-\mathrm{u}^{2})^{2}}\)--plate flexural rigidity,  
\(\
abla^{2}=\frac{\partial^{2}}{\partial\mathrm{x}^{2}}+\frac{\partial^{2}}{ \partial\mathrm{y}^{2}}\)--Laplacian,  
\(\
abla^{2}\
abla^{2}=\frac{\partial^{4}}{\partial\mathrm{x}^{4}}+2\frac{ \partial^{4}}{\partial\mathrm{x}^{2}\partial\mathrm{y}^{2}}+\frac{\partial^{4 }}{\partial\mathrm{y}^{4}}\)--bi-Laplacian,  
\(\mathrm{v}\)--[PERSON]'s ratio,  
\(\mathrm{h}\)--plate thickness,  
\(\mathrm{w}\)--plate deflection,  
\(\mathrm{\alpha_{t}}\)--coefficient of thermal expansion,  
\(\mathrm{q}\)--load perpendicular to the central surface of the plate,  
\(\mathrm{\Delta T}\)--difference in temperature between lower plate \(\mathrm{T_{d}}\) and upper plate \(\mathrm{T_{g}}\) determined by correlation: \(\mathrm{\Delta T}=\mathrm{T_{d}}-\mathrm{T_{g}}\), \(\mathrm{\alpha_{t}}\) is the coefficient of thermal expansion of the plate material,  
\(\mathrm{K_{z}}\)--subgrade stiffness reaction. The Winkler-type substrate model may raise doubts in terms of the assumption that the substrate dislocates only at points where the load acts. In fact, if the substrate on which the structure is placed is continuous, displacements also occur outside its load zone. Although other, more accurate models do not have this drawback, they usually lead to the same differential Equation (2), and the difference lies in the method through which their coefficients are determined. The purpose of this article is to analyse the statics of closed rectangular tanks with an internal partition, the distribution of internal forces and their effect on the value of bending moments, assuming the cooperation with the Styrofoam filling treated as an elastic substrate. Additionally, the buoyancy and stability of the pontoon were checked against standard guidelines from different countries. This work is of an application nature, and the idea of this article is to develop a ready-made building element that can be built in without any additional construction work.
## 3 Application Examples
### Analysis of Salicylic Acid Exudation by Hairy Roots of Hyoscyamus niger  
To test the suitability of the bioreactor for comparative studies of salicylic acid exudation by hairy roots, hairy root cultures generated from three accessions of _Hyoscyamus niger_ and _Sesamum indicium_ were grown in 0.5\(\times\) MS medium with 2.5% sucrose in bioreactors operated in bubble mode and in shaken flasks. After 30 days, growth medium was collected, extracted with ethyl acetate and the extracts were analyzed by HPLC-MS. The results are shown in Figure 9.  
Hairy roots of _Hyoscyamus niger_ exuded salicylic acid into growth medium in amounts varying from 2 \(\upmu\)/L to 60 \(\upmu\)g/L, while hairy root exudates of _Sesamum indicium_ has not secreted detectable amounts of salicylic acid (data not shown). Cultivation conditions strongly affected the concentration of salicylic acid accumulating in nutrient solution. Hairy roots of one plant accession secreted larger amounts of salicylic acid in bioreactors, while hairy roots on another accession secreted much large amounts of salicylic acid in shaking flasks (Figure 9). This surprising result indicates that the response of salicylic acid pathway to environmental cues varies among populations of the same plant species. We speculate that these differences reflect selection pressures exerted by different environments on the plants. The result corroborates our assumption that studying differences in salicylic acid production among plants with different life histories may shed light into biological functions of this multifaceted metabolite.  
Figure 9: Salicylic acid accumulated in nutrient solution of hairy root cultures derived from three accessions of _Hyoscyamus niger_ (designated Iraq, Iran and Germany) and grown in bioreactors and in shaking flasks for three weeks. Dotted lines show the means.  
As far as we are aware, secretion of salicylic acid by hairy roots has not been reported yet. Adding salicylic acid to culture medium of hairy roots was used to stimulate the synthesis of alkaloids [64], including hoycosamine and further tropane alkaloids [65; 66], which are also produced by _Hyoscyamus niger_. The interplay between the secretion of salicylic acid and stimulation of alkaloid synthesis by externally added salicylic acid adds to the complexity of biological roles and effects of salicylic acid in plants.