esa-sceva/satcom-chunk-collection huggingface dataset:
# Generalized Kinetic Monte Carlo Framework for Organic Electronics
## 5 Conclusions  
To summarize, we have presented a generalized kinetic Monte Carlo framework for the study of organic semiconductors and optoelectronic devices. New developments on a realistic representation of the organic material by a generalized non-cubic Voronoi set and polymer chains have been emphasized. We have included dopant sites and improved our description of the exciton dynamics by a model of the triplet dynamics for the detailed study of OSCs and OLEDs. A simplified spatial correlated density of states accounting for a correct treatment of the energetic landscape has been presented. Our kMC framework overcomes limitations of existing kMC models due to both a generalized spatial and energetic representation of the underlying multifaceted organic materials, as well as improved treatment of the particles involved in the photocurrent generation of OSCs and the light emission of OLEDs. We presented two applications concerning electrical and optical processes inside an organic semiconductor. The impact of the intra- to inter-chain hopping rate and the directionality of the polymer chains on the charge carrier mobility has been studied. We showed that an enhanced intrachain hopping rate itself does not improve the mobility of charges, but directionality is necessary. Furthermore, the impact of the dopant volume concentration on the effective lifetime and diffusion length of excitons has been studied. The investigated exciton dynamics coincides with reported values of both singlet and triplet lifetimes and diffusion lengths.  
Figure 8: Impact of the dopant volume concentration \(c\) on the average exciton lifetime \(\tau\) (solid curves) and the effective diffusion length \(L_{\text{diff,eff}}\) (dashed curves) for singlet excitons (**a**) and triplet excitons (**b**).  
Figure 9: Impact of the dopant volume concentration \(c\) on (**a**) the effective exciton lifetime and (**b**) intersystem crossing and triplet-triplet annihilation. The red bars show the proportion of converted triplets over the amount of optically-generated singlets, and the black bars represent the percentage of TTA over the amount of converted/generated triplets.  
**Acknowledgments:** This work was supported by the Technical University of Munich (TUM) International Graduate School of Science and Engineering (IGSSE), the excellence cluster Nanosystems Initiative Munich (NIM) of the German Research Foundation (DFG), the DFG Priority Programme FFLexCom SPP 1796 and the DFG and the TUM in the framework of the Open Access Publishing Program.  
**Author Contributions:** [PERSON] proposed the idea of this paper. [PERSON] wrote the paper with support of [PERSON] and [PERSON] for Sections 3 and 4 and [PERSON] for Section 2. [PERSON] implemented the Voronoi tessellation. [PERSON] implemented the chain algorithm and performed simulations for Section 4.1. [PERSON] implemented the exciton and dopant model and performed simulations for Section 4.2. [PERSON] and [PERSON] provided methodology suggestions.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 Modeling and Control
### Milli/Microbot Magnetic Actuation Algorithm  
The torque and force that the milli/microbot should be subjected to at point \(P\) can be realized based on the mapping relationship between the magnetic field contribution and the driven current vector. Assuming that the magnetic field generated at point \(P\) by the \(e_{th}\) coil in the unit current in the global coordinate frame is expressed as \(B_{e}(P)\). The magnetic field flux density \(B(P)\) generated at point \(P\) with eight electromagnetic coils can be represented as follows.  
\[\begin{split} B(P)&=\sum_{e=1}^{8}B_{e}(P)i_{e}\\ &=\left(\begin{array}{cc}B_{1}(P)&\cdots&B_{8}(P)\end{array} \right)\left(\begin{array}{c}i_{1}\\ \vdots\\ i_{8}\end{array}\right)=\beta I\end{split} \tag{21}\]  
The gradient component of the magnetic field generated by the electromagnetic coil set along the coordinates is:  
\[\frac{\partial B}{\partial i}=\left(\begin{array}{cc}\frac{\partial B_{1}}{ \partial i}&\cdots&\frac{\partial B_{8}}{\partial i}\end{array}\right)\left( \begin{array}{c}i_{1}\\ \vdots\\ i_{8}\end{array}\right)=\beta_{i}I \tag{22}\]  
where \(i=x,y,z\). The torque and force of the milli/microbot at position \(P\) in the workspace can be expressed by the actuation Equation (23), where \(A_{(T,F)}(M)\) is a \(6\times 8\) actuation matrix.  
\[\left(\begin{array}{c}T\\ F\end{array}\right)=\left(\begin{array}{c}S_{k}(M)\beta\\ M^{T}\beta_{x}\\ M^{T}\beta_{y}\\ M^{T}\beta_{z}\end{array}\right)\left(\begin{array}{c}i_{1}\\ \vdots\\ i_{8}\end{array}\right)=A_{T,F}(M)I \tag{23}\]  
Figure 6: Magnetic force simulation of each point when the milli/microbot moves along the planned spatial curve trajectory. (**a**) Schematic diagram of force decomposition of magnetic carrier in motion along a predetermined trajectory. (**b**) Schematic diagram of changes in the magnetic force component during the magnetic carrier’s completion of the predetermined trajectory.  
For a milli/microrobot, the desired torque and force that the magnetic field needs to apply are determined and the current vector of the electromagnetic coil set can be obtained by (21). Since the system is over-actuated, the current vector that satisfies the above conditions is not unique. This is a convex optimization problem, and the objective function and constraints are set according to the actual requirements [33]. All movements of the milli/microrobot can be equivalent to the superposition of fixed-point actuation and linear motion actuation assuming that the milli/microrobot moves very slowly. Considering that the energy consumption and heat generation of the system are linear with the quadratic of the coil current, the objective functions in both motion states are set to the minimum of the Euclidean norm of the current vector. The constraints are derived from the deformation of the actuation equation according to the actual actuation requirements and system characteristics and it is mainly divided into two parts as shown below. These two linear optimizations are performed in the Gurobi Optimizer.
# Fault Prediction and Reconfiguration Optimization in Smart Grids: AI-Driven Approach
## 5 Evaluation  
Considering the objective of this article, which aimed to detect and predict errors, or faults more specifically, an error criterion had to be defined first to label the results from the algorithm. Considering that the simulations were configured with a real scenario involving losses and capacity limitations of the links, the decision was made to establish the failure condition based on the presence of excess capacity in an energy exchange between two nodes in the network. These errors were not introduced arbitrarily; rather, we simulated the power balance based on the established configuration of the smart grid. In scenarios where losses occurred due to excess capacity, we denoted that for a given entry in the dataset, under those specific conditions, a fault would be marked accordingly. Subsequently, our objective was to classify whether a fault would occur based on predetermined conditions and configurations. In this step, it was decided to apply a link configuration based on the _IEEE 123 Node Test Feeder_[11] to ensure that the process of assigning capacities to nodes in the algorithm was random. For simplicity, three types of links are defined in Table 4.  
Now that the definition of error is clear, in this section, we evaluate various ML and DL techniques in terms of error prediction. Specifically, we define Random Forest (RF) and Support Vector Machine (SVM) as ML methods, and ANN as a DL method, based on their structural characteristics. Our evaluation follows the pipeline illustrated in Figure 8, which first generates a comprehensive set of random topologies, then sets up the simulation environment using DEN2 DE, and finally performs an extensive analysis of the different ML and DL models, each of which is examined in separate sections.  
\begin{table}
\begin{tabular}{c c c} \hline \hline _R (ohm/km)_ & _I max_ (A) & _Section_ (mm\({}^{2}\)) \\ \hline \hline
0.272 & 185 & 70 \\ \hline
0.78 & 100 & 25 \\ \hline
1.91 & 53 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Link configuration based on the _IEEE 123 Node Test Feeder_.  
Figure 8: Defined pipeline to detect and predict errors during the energy distribution process.
# Performance Analysis of DF Relay-Assisted D2D Communication in a 5G mmWave Network
## 3 Problem Formulation and Analysis
### Performance Analysis
#### 3.3.2 Relay Selection  
In Mode 3, while a D2D user, \(D1\), communicates with another D2D user, say D2, it is assisted by a two-way DF relay node. However, the most prominent task is choosing the best relay node. Thus, there should be a procedure for selecting the relay for overall D2D communication to prevail. This subsection portrays a dynamic relay selection (DRS) method for selecting the best relay node for D2D communication at a frequency of 28 GHz while satisfying the QoS constraints. The steps for the selection of the relay node for two D2D users, \(D1\) and \(D2\), to communicate with each other in the presence of \(R_{ij}\) relays, where \(i,je\mathbb{R}\), are given as follows:  
1. The transmitter D2D user \(D1\) sends a request to all of the relays in its proximity. The respective relay nodes receive the signal and decode it;
2. The receiver relay sends back the acknowledge signal to user \(D1\) along with the information of the instantaneous sum SINR at the relay node, the path loss attenuation, and the distance between the relay node and D2D users \(D1\) and \(D2\). The instantaneous sum SINR at the relay node from Equation (24) can be expressed as follows, \[\gamma_{ij}^{in}=\frac{P_{i}|h_{i}|^{2}d_{i}^{-\alpha}+P_{j}|h_{j}|^{2}d_{j}^ {-\alpha}}{P_{CR}|h_{CR}|^{2}d_{CR}^{-\alpha}+N_{0}}\geq\gamma_{th}\quad\ \ ;\ \ \ i,je\mathbb{R};\] (40)
3. User \(D1\) sorts the relays based on the distance between the relay node and D2D users \(D1\) and \(D2\) in increasing order through a binary search method. The relay link exhibiting a higher sum SINR, a lower distance, and higher channel gain is chosen as the relay for D2D communication in Mode 3 as follows, \[argmax\{\gamma_{ij}^{in}\}\] (41)
###### Abstract  
Accurate and stable wind power prediction is essential for effective wind farm capacity management and grid dispatching. Wind power generation is influenced not only by historical data, but also by turbine conditions and external environmental factors, such as weather. Although deep learning has made significant progress in the field of wind power forecasting, it often fails to account for two key characteristics of the data: dynamic variability and heterogeneity. Specifically, the influence of external variables on wind power changes over time, and due to the diverse nature of the information carried by different variables, simple weighted fusion approaches are insufficient to fully integrate heterogeneous data. To address these challenges, this paper introduces a dynamic heterogeneous graph attention recurrent network (DHGAR), which incorporates dynamic graphs, heterogeneous graph attention mechanisms, and gated recurrent units. Dynamic graphs capture real-time associations between wind power and external variables, while heterogeneous graph attention allows for more effective aggregation of diverse information. These two components are integrated into the gated recurrent units, replacing traditional fully connected layers to better capture temporal dependencies in the wind power time series. Experimental results on three real-world datasets demonstrate the superior performance and practical applicability of the proposed model.  
wind power prediction; multiple variates; flow-aware dynamic graph; heterogeneous graph attention; gated recurrent unit +
Footnote †: journal: Applied Sciences  
1
###### Abstract  
The phosphate glass samples doped with Tb\({}_{2}\)O\({}_{3}\) oxide (general formula: P\({}_{2}\)O\({}_{5}\)-Al\({}_{2}\)O\({}_{3}\)-Na\({}_{2}\)O-Tb\({}_{2}\)O\({}_{3}\)) were synthesized and studied for usage in high-dose radiation dosimetry (for example, in high-activity nuclear waste disposals). The influence of terbium concentration on thermoluminescent (TL) signals was analyzed. TL properties of glasses were investigated using various experimental techniques such as direct measurements of TL response vs. radiation dose, T\({}_{\text{max}}\)-T\({}_{\text{stop}}\) and VHR (various heating rate) methods, and glow curve deconvolution analysis. The thermoluminescence dosimetry (TLD) technique was used as the main investigation tool to study detectors' dose responses. It has been proved that increasing the concentration of terbium oxide in glass matrices significantly increases the thermoluminescence yield of examined material. For the highest dose range (up to 35 KGy), the dependence of the integrated thermoluminescent signals vs. dose can be considered as a saturation-type curve. Additional preheating of samples improves linearity of signal vs. dose dependencies and leads to a decrease of the signal loss over time. All obtained data suggest that investigated material can be used in high-dose radiation dosimetry. Additional advantages of the investigated dosimetric system are its potential ability to re-use the same dosimeters multiple times and the fact that reading dosimeters only requires usage of a basic TL reader without any modifications.  
dosimetry; ionizing radiation; thermoluminescence; dosimeter; glass; phosphate; terbium +
Footnote †: journal: Applied Physics Letters  
1
## 3 Satellites and the Space Law Framework  
As will be outlined in this section, as space objects, satellites are subject to regulation under the five treaties of the United Nations Space Law framework, as well as standards set by the International Telecommunication Union (hereinafter the ITU), both of which aim to influence domestic legal systems and space law standards for State-based space actors.
## 1 Introduction
It proposes an MDP model for the HARQ process over a point-to-point link, optimizing the type and number of IR bits to be sent when failures occur. It then shows how such HARQ scheme can be generalized and adapted to suit a two-hop relay network, where the relay node can be optimized to choose between AF and DF based on the channel state information. Finally, it considers a multi-user broadcast scenario and shows that the optimization of the HARQ can be formulated in a convex form. Numerical simulations verify the derivations, and show that the proposed methods achieve modest improvements against traditional schemes in all three scenarios. The rest of the paper is organized as follows. Section 2 explains the system model and some notation to be used throughout the paper. Section 3 introduces the different types of IR bits being considered and how they can help in the decoding of a given bundle of codewords. Section 4 builds a single-link decision engine optimizing the type and number of IR bits to be sent as a function of the channel SNR, coding rate, and number of failed codewords in the bundle. Section 5 derives the decision engine for the relay, which decides between AF and DF relay strategies as a function of the SNRs on the two links and the code rate on the first link. Section 6 addresses the case of multi-user systems, proposing a combinatorial optimization algorithm for deciding how the failed codewords should be grouped for the generation of IR when failures occur. Finally, Section 7 illustrates the performance of our proposed policies through numerical simulations, and Section 8 concludes the paper.
# Evaluation of Structural Behavior and Fatigue Performance of a KR-Type Rail Clip
## 4 Fatigue Performance Evaluation  
The rail clip is a structure designed in such a way that the initial stress generated during the assembly process is relieved by the trainload during train operation. Stress ranges, due to the relieved stress and high initial stress, render the rail clip susceptible to fatigue cracking. The possibility of fatigue failure can be evaluated by the equation based on the fatigue criteria, which is given as follows [9]:  
Figure 11: Global response of the clip (force vs. displacement).  
Figure 12: Principal stress contours: (**a**) step \(1\), (**b**) stress range between steps 1 and 2 for displacement range of 2 mm.  
Figure 13: Fatigue failure modes: (**a**) mode 1; (**b**) mode 2.  
\[\text{Modified Goodman equation}:\ \frac{\sigma_{m}}{S_{u}}+\frac{\sigma_{a}}{S_{e}}\ \leq 1 \tag{1}\]  
where \(\sigma_{m}\) and \(\sigma_{a}\) are the mean stress and stress amplitudes, respectively. \(S_{u}\) and \(S_{e}\) are the tensile strength and endurance limit of the material, respectively. The endurance limit generally lies within the range of 35-60% of the tensile strength of the material [10; 11]. The endurance limit of 528 MPa (35% of the tensile strength) was conservatively selected. From Equation (1), the fatigue failure (through a planar graph represented by the stress amplitude and average stress) can be determined. The straight line obtained through the fatigue equation, shown in Figure 14, is related to the fatigue limit. If the actual stress amplitude is greater than the fatigue limit, fatigue cracking can occur. The fatigue performance for the two possible failure modes was determined using this approach.  
From Equation (1), the fatigue limit can be determined by both the mean stress and the stress amplitude, and these stresses vary with the maximum stress. Thus, the maximum stress and maximum stress amplitude are two key factors that determine the fatigue limit. The maximum stress and maximum stress amplitude can be determined from step 1 and the difference between steps 1 and 2 (i.e., steps 1-2) during analysis, respectively. The maximum principal stresses for the two failure modes for step 1 and steps 1-2 are tabulated in Table 3. Vertical displacements of 1, 2, and 3 mm are also considered.  
Figure 14 shows the fatigue performance evaluation of the rail clips for various vertical displacements of the rail. When the vertical displacement was 2 mm or larger, the stress amplitudes either approached or were greater than the fatigue limit. However, when the vertical displacement was 1 mm, the stress amplitude was far below the fatigue limit, indicating that if the rail was installed at the site where the vertical displacement was 2 mm or larger, fatigue failure could potentially occur. It should be noted that the measured peak strain after assembly was higher than the analysis result. Thus, actual mean stress could be higher resulting lower fatigue performance.
## 2 Materials and Methods
### Experimental Design
\begin{table}
\begin{tabular}{l l l} \hline
**Treatment** & \multicolumn{2}{c}{**Explanation**} \\ \hline N 1 & 0/0/0 = 0 & Control (no N fertilization) \\ N 2 & 40/30/40 = 0 & Fixed N application \\ N 3 & 40/30/40 = 110 & Fixed N application \\ N 4 & 40/60/40 = 140 & Fixed N application \\ N 5 & 70/30/40 = 140 & Fixed N application \\ N 6 & 160/00/0 = 160 & Fixed N application \\ N 7 & 70/90/0 = 160 & Fixed N application \\ N 8 & 40/90/40 = 170 & Fixed N application \\ N 9 & 70/60/40 = 170 & Fixed N application \\ N 10 & 70/90/40 = 200 & Fixed N application \\ N 11 & 70/120/40 = 230 & Fixed N application \\ N 12 & GFO & Determination of the N fertilizer requirement according to GFO \({}^{1}\) \\ N 13 & GFO \(-\) 10\% & Adjustments of the N fertilizer requirement according to GFO \({}^{1}\) \\ N 14 & GFO \(-\) 20\% & Adjustments of the N fertilizer requirement according to GFO \({}^{1}\) \\ N 15 & GFO \(-\) 30\% & Adjustments of the N fertilizer requirement according to GFO \({}^{1}\) \\ N 16 & GFO \(+\) 10\% & Adjustments of the N fertilizer requirement according to GFO \({}^{1}\) \\ N 17 & BESD/D \(\times\) Max N-Tester & Model BESD/D (consideration of the N content of the crop stand using a chlorophyll-meter. \\ N 18 & Sensor 10 h\({}^{-1}\) & Sensor method \({}^{2}\), target 101 h\({}^{-1}\) \\ N 19 & Sensor \(-\) 10\% & Sensor method \({}^{2}\) (according to treatment 18) \(-\) 10\% targeted N uptake \\ N 20 & Sensor \(-\) 20\% & Sensor method \({}^{2}\) (according to treatment 18) \(-\) 20\% targeted N uptake \\ N 21 & Sensor \(-\) 30\% & Sensor method \({}^{2}\) (according to treatment 18) \(-\) 30\% targeted N uptake \\ N 22 & Sensor \(+\) 10\% & Sensor method \({}^{2}\) (according to treatment 18) \(+\) 10\% targeted N uptake \\ N 23 & Sensor 10 h\({}^{-1}\) VB + 30\% & Sensor method \({}^{2}\), 30\% more N fertilization at the beginning of vegetation, target yield 10 ha\({}^{-1}\) \\ N 24 & Sensor 10 h\({}^{-1}\) VB - 30\% & Sensor method \({}^{2}\), 30\% less N fertilization at the beginning of vegetation, target yield 10 ha\({}^{-1}\) \\ N 25 & Sensor 11 h\({}^{-1}\) & Sensor method \({}^{2}\), target yield 11 h\({}^{-1}\) \\ N 26 & Sensor 8 h\({}^{-1}\) & Sensor method \({}^{2}\), target yield 8 h\({}^{-1}\) \\ N 27 & Sensor balance & Sensor method \({}^{2}\), the mean N uptake ha\({}^{-1}\) with a target yield of 10 ha\({}^{-1}\) is the upper limit of fertilization \\ N 28 & Sensor GFO & Sensor method \({}^{2}\) the fertilizer requirement according to GFO is the upper limit of fertilization \\ \hline \end{tabular}
\end{table} 
Table 2: Explanation of N Fertilization treatments in trials 2021–2023.
# High-Brightness Image Enhancement Algorithm
## 3 Proposed Method
### Optimization and Implementation
Mathematically, the minimum possible window size is \(2\times 2\), as there are three unknowns in each local window, and at least three linear constraints are needed to make the problem well-posed. If the image has a medium resolution, we solve the linear system in (11) using symmetric \(LQ\) or the generalized minimum residual method. However, the above method needs several minutes to converge to process an image with a resolution higher than 3024 \(\times\) 4032. We use a multigrid method [32] to speed up the processing time, which is the same as the method used in [33]. In our experiment, it took only a few seconds to produce satisfactory \(\hat{l}^{\dagger}\). The size of the local window can be adjusted in the system. We set window sizes from \(3\times 3\) to \(11\times 11\) and found that a window size of \(5\times 5\) could produce satisfactory results in  
Figure 2: Illustration of the region containing pixel \(i\). All windows \(w\) with size \(5\times 5\) that have \(i\) must be within the yellow part. Therefore, under the influence of the local window \(w\), there are at most 80 pixels around pixel \(i\). If the image has \(N\) pixels, the total number of nonzero elements in matrix \(D\) is less than \(80\times N\). terms of image clarity and visual effects. Considering the factors of edge definition and computational complexity, we chose a \(5\times 5\) window in our experiments. The algorithm above operates on the brightness channel of the input image. After obtaining the radiance map of the image, the RGB channel was reconstructed using the algorithm mentioned in reference [34]:  
\[l_{k}^{l}(i)=\left[l_{k}^{h}(i)/l^{h}(i)\right]^{s}\times l^{l}(i),\;k\in\{r,g,b \}, \tag{12}\]  
where \(l_{k}^{l}\) is one of the RGB color channels of the input image and \(l_{k}^{h}\) is one of the RGB color channels of the output image. The parameter \(s\) represents the saturation factor. A larger value of \(s\) produces a more saturated result. The introduction of (12) maintains the ratios between the brightness and color channels so that the color of the output image can be similar to that of the original image. In the experiment, we chose \(s\in[1,3]\) to produce images with natural color.