esa-sceva/satcom-chunk-collection huggingface dataset:
# Three Dimensional CS-FEM Phase-Field Modeling Technique for Brittle Fracture in Elastic Solids
## 3 Numerical Modeling
### Single Edge Notched Tensile Sample  
We simulated crack propagation in a rectangular bar, with a finite opening, under far-field tension. The bottom surface was fixed and a tensile pull was applied on the top surface of the rectangular block (Figure 3). The crack was located at an edge and the material parameters were: \(E=210\) kN/mm\({}^{2}\), \(\
u=0.3\), and \(G_{c}=5\times 10^{-4}\) kN/mm. The length scale parameter was \(l_{c}=0.08\) mm. We used a uniform mesh of 45,000 hexahedral elements, which was refined near the expected crack propagation zone (edge side of 0.02 mm) to successfully resolve the length scale parameter for better reproduction of the ultimate strength of the sample [25; 39]. Since this was a diffused fracture representation, we did not need to use any special crack-tip elements; the length scale parameter provided the transition from the damaged to intact zone. As expected, we saw the contour plot of crack evolution; it propagated along the straight path (Figure 4). We also studied the influence of the Griffith's energy release rate parameter \(G_{c}\) on the crack pattern. We tested the model for \(G_{c}=5\times 10^{-4},\ 4\times 10^{-4},\ 2\times 10^{-4}\), and \(1\times 10^{-4}\) kN/mm, and observd that this parameter had no influence on the ultimate crack path. The resultant load-displacement curve is presented in Figure 5. It shows that the slope of the curve is same for small displacements for different energy release rates. However, as displacement increased, the force reduced before it reached the ultimate load, which itself increased with \(G_{c}\). There was a sudden drop of the load after the crack initiation, which signified decrease in strength and ultimate failure. We also performed experiments with further reduction of mesh size near the expected crack path, however that did not affect crack pattern and the force-displacement curve, thereby proving that the damage pattern was convergent.  
Figure 3: A single edge notched tensile specimen: (**a**) representational 2D geometry; thickness = 2.0 mm; and (**b**) hexahedral mesh with smaller element size near the expected propagation zone.
# Reliable Visual Exploration System with Fault Tolerance Structure
## 3 Active Controller Based on Semantic Segmentation
### Goal Point Planner in Image Space  
After obtaining the segmentation result, goal selection is conducted in the segmented image, by following the procedure shown in Algorithm 2.  
```
1:Segmented ground, represented as \(G\)
2:Gaussian expression of \(G\) along the \(x\)-axis and \(y\)-axis
3:Main searching line generated from the Gaussian expression
4:Set of searching line \(I_{search}\) from the main searching line
5:Find the farthest ground point \(p_{goal}\) along all the lines in \(I_{search}\)
6:return\(p_{goal}\)
```  
**Algorithm 2** Goal point planning from segmented ground  
In our study, we approximate the ground region as a 2D Gaussian function, and compute the mean and variance of the ground pixels along the \(x\) and \(y\)-axes, represented as \(m_{x}\), \(m_{y}\) and \(v_{x}\), \(v_{y}\). Then, a line \(l_{m}\) that passes through point \((m_{x},m_{y})\) is regarded as the main searching line and its direction is determined by:  
\[d_{m}=v_{y}\cdot v_{x}^{-1}, \tag{1}\]  
with \(l_{m}\) a set of line is drawn which is centered on \(m_{x}\), \(m_{y}\), and in the direction of \(d_{m}+i*d\), where \(i\) is the index of line within the line set and \(d\) is a given interval for searching lines generation. Each line is represented as \(l_{i}\), combined with \(l_{m}\), we get the complete searching line set, represented as \(I_{search}\).  
For the image with a size of \(h*w\), where \(h\) is the height and \(w\) is the width, the original point \(o_{image}\) is defined as the point at \((h*2^{-1},0)\) in image coordinates (the \(y\)-axis lies in the image height direction). With \(I_{search}\), we search the ground pixels \(p_{i}\) along each line, and record the pixel which has the biggest Euclidean distance \(d()\) from \(o_{image}\) in the image space, represented as \(p_{goal}\) in Equation (2). Such a strategy increases the ability of exploring by driving to the most promising unexplored space:  
\[p_{goal}=\operatorname*{arg\,}_{p_{i}\in I_{search}}\max d(p_{i},\ o_{image}). \tag{2}\]  
Examples of the obtained \(p_{goal}\) are illustrated in Figure 4, where the red point is the goal, the blue point is the mean of the segmented result, and the blue lines are the searching lines. When no ground can be recognized, pure rotation by a random among is executed as the means of recovery.  
For motion control, PID controller is used for command generation. The error that is fed to the PID controller is defined as the distance between \(o_{image}\) and \(p_{goal}\). Under such a motion command, the robot is able to move continuously even if it is not able to localize itself globally.  
Figure 4: Example of goal point planning, the red point is the goal point and is projected to the raw image in the left figure.  
Figure 3: Example of segmentation result, where a cylinder-style obstacle is detected and segmented from the ground. The segmented ground is shown as the green region at the bottom in the right image.
# Chaotic Pulse-Shaping Filter Based on Root-Raised-Cosine Division
## 3 RRC-Divided Chaotic Filter
### Hybrid Dynamical System  
Taking Equation (8) as the basis function and carrying out a linear convolution with the random bi-polar information symbol \(s(t)\) as the analytical solution of the differential equation [23], we construct a new hybrid dynamical system to discuss the dynamic behavior of the generated chaotic signal, as follows:  
\[\ddot{x}(t)-2B\dot{x}(t)+[B^{2}+\pi^{2}(\beta+1)^{2}]x(t)=As(t), \tag{10}\]  
\[t\ddot{x}(t)-2 Bt\dot{x}(t)+2\dot{x}(t)+[B^{2}+\pi^{2}(\beta-1)^{2}]tx(t)-2 Bx(t) =s(t), \tag{11}\]  
where \(x(t)\), \(\dot{x}(t)\) and \(\ddot{x}(t)\) are the encoded signal and the first and second derivative of \(x(t)\), respectively. In this dynamical system, \(T_{s}\) is normalized to \(1\). The new hybrid dynamical system contains two linear differential equations. A chaotic signal can be obtained by summing the solutions of these two differential equations. Parameters \(A\) and \(B\) have two sets of values according to \(|t|=\frac{T_{s}}{4B}\). For \(|t|>\frac{T_{s}}{4B}\), \(A=168e^{-3.755B}\), \(B=4.8\ (t<0)\), \(-4.8\ (t>0)\), and for \(|t|\leq\frac{T_{s}}{4B}\), \(A=6.5\times 10^{-4}e^{13.63B}\), \(B=-0.1\ (t<0),0.1\ (t>0)\).  
The generated chaotic signal has an exact analytic solution given by:  
\[x(t)=\sum_{m=-\infty}^{\infty}s(m)\cdot p(t-m). \tag{12}\]
## 2 Related Work and Our Contributions
The aggregation delays obtained with our centralized benchmarking algorithm will serve as a lower bound for the aggregation delays determined with the distributed algorithms proposed in the literature. Second, for a given WSN graph, we observe the bottleneck link-weight based data gathering trees to incur a lower aggregation delay, but a larger diameter; on the other hand, the bottleneck node-weight based data gathering trees incur a larger aggregation delay but a smaller diameter. Ours is the first work to comprehensively analyze the diameter-aggregation delay tradeoff for sensor networks and show that the tradeoff depends on the type of the underlying data gathering trees used (bottleneck node-weight based or bottleneck link-weight based).
## 3 Signal Extraction Algorithms
implemented in our real experimental data to find the target distance, and they are shown in Figures 8-10. Figure 8: Average raw data (**top**) and processed data (**bottom**). The range found with our own LRF is 1879 m. The number of averaged raw data frame, N is 2000. Figure 9: Average raw data (**top**) and processed data (**bottom**). The range found with our own LRF is 2952 m. The number of averaged raw data frame, N is 4000.
## 5 Applications and Services
### UAV Design
#### 5.2.2 RISeUAV  
RISeUAV, as an emerging integrated approach, has gained significant popularity in the community due to its ability to combine the advantages of both UAVs and RIS [145; 146]. RISeUAV can be utilized in various applications such as joint wireless power transfer (WPT) and wireless information transfer (WIT) [83], for ergodic communication [147], mobile ground nodes [148], IoTs [149], vehicles [93], multi-layer MEC [150], disaster management [121], anti-jamming maritime [113], free-space optical communications [151], and aerial LoS links for intelligent vehicles [33; 41; 136]. See Table 1 for more papers that have used RISeUAV.  
A theoretical model for analyzing the uplink performance of RISeUAV communication systems with a random 3D mobile pattern is presented in [152]. In this model, a random 3D mobility pattern for UAVs is considered, incorporating both random way-point and uniform mobility models. Subsequently, an end-to-end transmission model for the RISeUAV communication system is developed, accounting for the effects of channel fading, RIS configuration, UAV mobility, and association policies. By combining these models, analytical expressions for uplink transmission metrics are derived, along with a performance-bound analysis.  
The RISeUAV may experience random fluctuations due to wind disturbances and propeller rotations, leading to misalignment of reflected signal phases [54]. A 3D RISeUAV channel model, proposed in [153], simulates UAV fluctuations using random processes. Key statistics, including the temporal correlation function (TCF), Doppler power spectral density (PSD), and ergodic achievable rate, are derived. The results indicate that even minor vibrations can introduce additional Doppler frequency components, significantly reducing the temporal correlation and achievable rate of the RISeUAV-assisted channel, particularly when operating at high-frequency bands.  
The RISeUAV may experience random fluctuations due to wind disturbances and propeller rotations. This leads to the misalignment of reflected signal phases [54]. A 3D RISeUAV channel model has been proposed in [153] where UAV fluctuations are simulated by random processes. Statistics including temporal correlation function (TCF), Doppler power spectral density (PSD), and ergodic achievable rate are obtained. Results show that even for a minor vibration, the UAV wobbling can introduce extra Doppler frequency components and significantly reduce the temporal correlation and achievable rate of the RISeUAV-assisted channel when systems operating at high-frequency bands.
# Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives
## 2 Historical Review
### Characteristics of Research Output  
#### 2.1.1 Criteria Used to Select Literature  
We searched for research articles published in peer-reviewed journals in English from 1972 to August 2018 using the ISI Web of Science and Google Scholar databases. The following two combinations of keywords were used to collect the literature: (1) \"urban heat island OR urban thermal environment OR urban climate\" AND \"remote sensing OR satellite OR MODIS OR Landsat OR AVHRR OR ASTER OR ATSR OR SEVIRI OR HCMM\", and (2) \"land surface temperature OR thermal environment\" AND \"urbanization OR urban land OR land use change OR land cover change OR cities OR city OR metropolitan area\". The search yielded a total of >3000 journal publications. These were screened to check whether they studied urban surface temperature or thermal environment by reading the publication title and abstract. The screening resulted in 778 publications. All these papers were then downloaded and more carefully read to examine whether the publications (1) used satellite images to derive LST, and (2) explored the temperature variations in an urban environment. Studies that focused solely on LST retrieval (see reviews by [PERSON] et al. [38] and [PERSON] et al. [39]) and downscaling methods [40,41], air UHI (see reviews by [PERSON] [42], [PERSON] [43], [PERSON] [44], and [PERSON], et al. [45]), and UHI mitigation strategies (see reviews by [PERSON] et al. [46], [PERSON] [47], [PERSON] [48], and [PERSON] et al. [49]) were not included in our literature analysis. This full-text check resulted in 492 eligible publications (references of all studies are listed in the Supplementary Materials and Table S1). All of the information, including the title, author, source journal, publication year, sensor types, study area, study time, and research foci, was extracted for further analysis.
## 3 Results and Discussion
The results shown in Figures 7-10 reveal slight deviations between the experimental data and the theoretical predictions, with such (very moderate) deviations being more significant in RVNSCB specimens. Predictions are accurate for the whole range of sample sizes analyzed in this work and for the two criteria applied in this research. In addition, the two criteria provide, in practical terms, the same predictions in RVNBD specimens, and very similar predictions in RVNSCB specimens. Since Equation (9) is simpler than Equation (12) for calculating the parameter \(\mathrm{d_{c}}\), this formula may be preferred for the design or analysis of notched structures manufactured from PMMA or GPPS materials. Significantly larger differences between the fracture loads predicted by the MS-Schmidt and the MMS criteria can be expected for those engineering materials having generally large FPZs, such as concrete, rock, etc. Figure 6: Variations of \(1/\mathrm{d_{c}}\) versus \(1/\mathrm{R}\) considering both the MS-Schmidt’s and MMS criteria, together with two linear fittings. (**a**) BD specimens made of PMMA; (**b**) BD specimens made of GPPS; (**c**) SCB specimens made of PMMA; (**d**) SCB specimens made of GPPS. Figure 8: Variation in K\({}_{\rm{k}}\)\({}^{\rm{V,\rho}}\) with specimen size for RVNBD specimens made of GPPS. Predictions provided by the MS–Schmidt and the MMS criteria, and experimental data. Figure 7: Variation in K\({}_{\rm{k}}\)\({}^{\rm{V,\rho}}\) with specimen size for RVNBD specimens made of PMMA. Predictions provided by the MS–Schmidt and the MMS criteria, and experimental data. Figure 10: Variation in \(\rm K_{lc}\)\({}^{V,\rho}\) with specimen size for RVNSCB specimens made of GPPS. Predictions provided by the MS–Schmidt and the MMS criteria, and experimental data. Figure 9: Variation in \(\rm K_{lc}\)\({}^{V,\rho}\) with specimen size for RVNSCB specimens made of PMMA. Predictions provided by the MS–Schmidt and the MMS criteria, and experimental data.
## 2 Biosynthesis and Key Regulatory Networks of Volatile Terpenoids
### Biosynthetic Pathways of Volatile Terpenoids
In the presence of ATP, CDP-ME undergoes phosphorylation by 4-diphosphocytidyl-2-C-methyl-D-erythnitol kinase (CMK), yielding CDP-ME 2-phosphate (CDP-ME2P). CDP-ME2P is then cyclized by 2-C-methyl-D-erythnitol 2,4-cyclodiphosphate synthase (MECPS) to form 2-C-methyl-D-erythnitol 2,4-cyclodiphosphate (ME-cPP), accompanied by the release of cytidine monophosphate (CMP). Hydroxy-methylbutenyl diphosphate synthase (HDS) subsequently catalyzes the conversion of ME-cPP into 1-hydroxy-2-methyl-2-butenyl-4-diphosphate (HMBPP). Finally,hydroxy-methylbutenyl diphosphate reductase (HDR) reduces HMBPP, generating the essential isoprenoid precursors, IPP and DMAPP. The IPP/DMAPP pool is channeled into three prenyltransferase systems: firstly, geranyl diphosphate synthase (GPPs) mediates the head-to-tail condensation of DMAPP with two IPP units to produce geranyl diphosphate (GPP, C10) or its cis-isomer neryl diphosphate (NPP, C10) depending on tissue-specific GPPS isoform expression; secondly, famesyl diphosphate synthase (FPPs) sequentially adds three IPP units to DMAPP to generate the C15 backbone farnesyl diphosphate (FPP) through precise chain-length control; thirdly, geranylgeranyl diphosphate synthase (GGPPs) extends FPP with additional IPP units to form geranylgeranyl diphosphate (GGPP, C20), which serves as the precursor for diterpenes (C20) via diterpene synthases and carotenoids through phytoene synthase-mediated cyclization [15; 18; 19; 20; 21; 22]. It should be noted that GPP/NPP and FPP act as precursors for monoterepnes and sesquiterpenes, respectively, in reactions catalyzed by terpene synthases (TPSs) [18]. GGPP is also the precursor for carotenoid formation. Catalyzed by carotenoid cleavage dioxygenenes (CCDs), it generates volatile C\({}_{13}\) apocarotonoids. Figure 1: Biosynthesis of plant volatile terpenoids via MVA and MEP pathways catalyzed by distinct terpene synthases. Sources of the specific cases cited in this figure are as follows: _Red River_[23], _F. refracta_[24], _Aquilegia_[25], _Morella rubra_[26], _Pruuus persica_ [PERSON]. [PERSON][PERSON]_[27], _Lilium ‘ Siberia’_[28], _Salvia sclarea_ (L.) [29], _Tetradenia riparia_[30], and _Thuja occidentals_[31]. The following abbreviations are used in this figure: TPS-M, monoterpene synthase; TPS-ST, sesquiterpene synthase; TPS-DT, diterpene synthase.
## 4 Discussion  
After presentation of the obtained results, it can be concluded that both classification approaches are suitable for the application of specific plausibility cause detection in FE simulations. This answers the first research question as both presented classification approaches provide very good results, and principal detection of the cause of the error is possible with both of them. However, a detailed comparison of the two approaches reveals the different advantages and disadvantages of the procedures.  
The advantage of multilabel classification via an adapted regression model is the reduced training time since only one model needs to be trained instead of three in the case of binary classification. Therefore, the model can theoretically be adapted to a new dataset faster. Furthermore, integration of all three labels into the dataset reduces class imbalance compared to a plain binary classifier, as can be seen in Figure 11. Nevertheless, training  
Figure 14: Results of the adapted class weights with the Inception-V3 derivate and binary classification.  
Figure 13: Comparison of different CNN architectures in terms of G-Mean metric for plausibility detection.  
also showed that there was high sensitivity to the appropriate choice of hyper parameters, which made the training time-consuming in some cases.  
In contrast, the advantage of binary classifiers lies in the learning of a specific problem, which thus allows it to predict that problem with higher accuracy at the expense of training time. Additionally, they are much more flexible since the CNN architecture can be chosen specifically for the class. In the case of application as an assistance system for plausibility checking in industry, new data could be brought into the system more easily because only the recognition of an error would cause temporarily interruption.  
In addition to the multilabel concept comparison, it is noticeable when comparing the CNN architectures that all networks achieve very good results. However, to answer the second research question, the Inception-V3 adaptation achieves especially high classification results. The other three network types mostly have their strengths in a certain class and are less capable of detecting the other two. The use of specific class weights resulted in a further general improvement, which leads to the conclusion that the combination of binary classifiers with the Inception-V3 network is best suited for the dataset and the application task at hand.
## 4 Case Analysis
### Initial Network Environment and Parameter Settings
#### 4.1.1 Network Description  
The Sioux Falls network, depicted in Figure 3, includes 24 nodes, 76 directed roads, and 552 origin-destination (OD) pairs. Developed based on research by [PERSON] et al. [47], this network also incorporates 10 two-way bus routes and five metro lines, providing a comprehensive representation of both private and public transportation modes. The network features a mix of high-traffic roads and public transportation routes, making it ideal for evaluating the impact of the TRS on shifts between private vehicle and public transport usage. Its manageable size, along with the availability of detailed multimodal data, allows for effective simulation of the TRS model and testing of the incentive and penalty mechanisms. Table 2 provides the detailed transit line stop sequence and departure intervals. Additionally, specific transfer points have been designated for combined modes of travel, such as C+B and C+M, with the station numbers for these transfer points provided in Table 3. The basic parameters for each road are outlined in Table 4.  
\begin{table}
\begin{tabular}{l l l l} \hline
**Transit Lines** & **Line No.** & **Departure Interval (min)** & **Stop Sequence** \\ \hline \multirow{8}{*}{Bus lines} & 1 & 10 & 4, 11, 14, 23, 24 \\  & 2 & 10 & 1, 3, 12, 13, 24 \\  & 3 & 10 & 11, 14, 23, 24, 13 \\  & 4 & 10 & 18, 20, 21, 22, 23 \\  & 5 & 10 & 7, 8, 16, 18, 20 \\  & 6 & 10 & 14, 15, 19, 20, 22, 23 \\  & 7 & 10 & 2, 6, 8, 9, 10, 11, 12 \\  & 8 & 10 & 4, 5, 9, 10, 17, 19, 20 \\  & 9 & 10 & 10, 16, 17, 19, 20, 21, 24 \\  & 10 & 10 & 1, 3, 4, 5, 9, 10, 15, 19, 20 \\ \hline \end{tabular}
\end{table}
Table 2: The stop sequence of transit lines.  
Figure 3: The case study network.
## 3 Common Latitude Power Series Expansions with Geodetic Latitude as Variable  
Geodetic latitude is one of the most used earth science latitudes in geodetic surveys and map projection theory. To implement various projection features, five other types of auxiliary latitudes are used: geocentric latitude, reduced latitude, rectifying latitude,conformal latitude, and authalic latitude [29]. These are all functions of geodetic latitude. Practical applications often require the transformation between the five auxiliary latitudes and geodetic latitudes, which often involves complex power series expansion and the calculation of complex higher-order derivatives. Heretofore, most of these derivatives were obtained manually, which is a long, complicated process. Partial approximations were thus used to facilitate the derivation, which also led to the deviation of higher-order terms. With the help of the symbolic calculation capabilities of Mathematica, this problem can now be efficiently solved. The power series of the common latitude with geodetic latitude as a variable based on eccentricity \(e\) and the third flattening \(n\) are obtained as described below.
## 5 Discussion
### Effect of the Initial Temperature  
Temperature was also a key parameter for gas extraction that cannot be neglected. Figure 15 illustrates that the effect of the initial temperature on extraction completion time. The figures shows that increasing the initial temperature of the coal seam could decrease the time needed to complete extraction, by 3.8% when the initial temperature was increased from 298 K to 318 K. The data demonstrated that higher temperatures were conductive to faster gas extraction from a coal seam. If gas was extracted from a higher temperature coal seam, more methane desorption and temperature reduction would cause the coal matrix to shrink and increased the cleat permeability. Methane would move into boreholes more easily.