esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Test Results and Discussions  
### Crack Pattern and Failure Mode  
[PERSON] et al. [43] showed three different failure modes that were observed in their study during the uniaxial tensile test of fiber-reinforced cementitious matrix coupons. The first failure mode indicates the failure at the transition zone near the anchorage zone. This phenomenon is mainly due to the fact that the compressive action of the clamping force and the applied tensile load occurred simultaneously at the gripping zone. The second failure mode is related to the rupture of warp carbon fiber yarns due to the failure of the mortar matrix. The last failure mode is related to the crack of the mortar matrix, triggering slippage of carbon fiber yarns at the clamping area.  
Figures 9 and 10 show the typical crack patterns and failure modes observed for each series in this study. The results observed from all the TRM coupons exhibit the same failure mode, corresponding to the failure at the transition zone, as described in Figure 10. Meanwhile, the phenomena of the carbon fibers rupture and the carbon fibers slippage were not observed. During the tensile test, cracks appeared at the middle part of the TRM coupons (at the lap-spliced carbon fiber textile zone). Then, more cracks appeared near the transition zone, and continued to widen, until failure. According to [PERSON] et al. [43], this failure mode occurs due to the fact that the compressive action of the clamping force and the applied tensile load occurred simultaneously in the gripping zone. This failure mode is desirable because it indicates the lap-spliced region is strong enough.  
Figure 8: Test setup and measuring instruments: (**a**) schematic of the test setup; (**b**) photograph of the test setup.  
Figure 9: Failure modes of TRM test coupons.
## 5 Application
### Model Testing and Comparison
#### 5.4.2 Performance Comparison  
To further ascertain the efficacy of the model, identical test samples were utilized to compare the accuracy and computational efficiency of four models. In Figure 12, it is evident that the enhanced gray H-convex correlation method exhibits superior accuracy compared to the other three methods, achieving an accuracy rate exceeding 90%. However, owing to its intricate calculation steps, the running time was slightly prolonged, surpassing 0.8 min. Conversely, the other three methods feature straightforward calculation procedures, completing within a model time of less than 0.5 min.  
Figure 11: Visual cognitive vs. layout aesthetics change trend line chart. **(a)** Gaze duration and layout aesthetics; **(b)** gaze count and layout aesthetics; **(c)** average pupil diameter and layout aesthetics; **(d)** gaze shift frequency and layout aesthetics; **(e)** gaze time percentage and layout aesthetics.
# Logistics Distribution Vehicle Routing Problem with Time Window under Pallet 3D Loading Constraint
## 4 Algorithm Design  
### General Design of the Algorithm  
In this study, a hybrid simulated annealing-genetic algorithm is used for the solution algorithm. The algorithm is mainly divided into three parts, program initialization, three-dimensional loading, as well as path and carriage calling. The first step is spatio-temporal clustering of the positions of each logistics node in space and updating the clustering center to obtain better clustering results. Then initialize the initial sequence of goods, carriages, iteration information, and so on. Next comes the 3D loading part. Here, we need to use the loading logic of hierarchy theory to judge the goods in the sequence one by one. The fitness is calculated, and a new sequence is generated by the crossover, variation, and selection operations of the simulated annealing-genetic algorithm. Moreover, a new solution is obtained through testing, the solution corresponding to the highest fitness is selected in the process of continuous annealing iterations, and the goods that are not loaded are packed as new goods to be loaded into the next pallet. The path and carriage call section is responsible for the carriage loading and the output of the optimal solution. Then, the hybrid simulated anneal-genetic algorithm is also used to generate the optimal scheme according to the clustering results, carriage loading, and volume weight constraints. Finally, the results of total distance, loading rate, total cost, and loading and unloading time are obtained. Figure 4 shows the algorithm flow chart.
## 3 Results
### Simulating Different Parameters
#### 3.3.2 Changes in Bus Capacitance  
As stated at the start of the study, a busbar is connected to various client equipment. This results in the busbar filtering high-frequency components of the transient signal. In the simulation, this was represented as a capacitive load to ground, and the capacitance left at a comparatively low value. However, no two busbars have precisely the same equipment connected. This results in different characteristics forming at each busbar under fault conditions and the filtering effect changing. To see the effect of a change on the capacitive load to ground on the fault current, the transmission line lengths were set to 100 km again, and the capacitance of each load increased by 20% and then 40% of the original value. In Figures 14-16, the effect of these changes can be seen. It is important to note that these figures only show the waveforms and not values. After obtaining the transient signal of the changed bus capacitance, it was classified by the trained network. The network was not able to correctly classify any of the faults. This is to be expected as it was never trained with data that includes different bus capacitances.  
Figure 13: Comparison of different line lengths.  
Figure 14: Normal bus capacitance.  
Figure 15: Bus capacitance increased by 20%.  
Figure 16: Bus capacitance increased by 40%.
# Ant-Balanced Multiple Traveling Salesmen: ACO-BmTSP
## 2 Methodology
### The Proposed Approach: ACO-BmTSP Algorithm
Namely, \(m\) is the number of the TSP, \(n\) is the number of cities, and \(k\) is the number of tours. Constraint (12) ensures that exactly \(m\) traveling salesmen start and return to the initial depot (vertex 1). Restrictions (13) and (14) ensure that exactly one edge enters and exits each graph city. The sub-tour elimination constraint (5) defines an integer variable \(u_{i}\) for the position of node \(i\) in a tour, and \(n\) represents the maximum number of nodes visited by any salesman. Finally, (6) defines that the variable \(x_{ij}\) is binary. \[\min\sqrt{\sum_{k=1}^{m}\left(\sum_{j=1}^{n}\sum_{j=1}^{n}c_{ij}x_{ ij}^{k}\right)^{2}} \tag{11}\] \[\sum_{k=1}^{m}\sum_{j=1}^{n}x_{1j}^{k}=\sum_{k=1}^{m}\sum_{j=1}^{ n}x_{j1}^{k}=m\] (12) \[\sum_{k=1}^{m}\sum_{j=2}^{n}x_{ij}^{k}=1,\quad i=1,2,...,n\] (13) \[\sum_{k=1}^{m}\sum_{i=2}^{n}x_{ij}^{k}=1,\quad j=1,2,...,n\] (14) \[u_{i}^{k}-u_{j}^{k}+nx_{ij}^{k}\leq n-1,\quad 2\leq i\
eq j\leq n, \ k=1,2,\ldots,m\] (15) \[x_{ij}^{k}\in\{0,1\},\quad i,j=1,2,\ldots,n,\ k=1,2,\ldots,m \tag{16}\]  
Consider a problem with three traveling salesmen/ants, one deposit, and 16 cities. Figure 4 presents one state in which the ant-picket algorithm is used. In the following, it is explained how to reach this state with the three ants: {green (\(g\)), red (\(r\)), blue (\(b\))}. The ants randomly select the next city to visit while taking the pheromomes deposited in each arc into account. In the beginning, the ants \((g,r,b)\) are at the deposit. Therefore, the cost is \(c=(0,0,0)\) and \(u_{g}=\{0\},u_{r}=\{0\},u_{b}=\{0\}\). Suppose that the chosen cities are \(11,16\), and \(3\) for ants \(g\), \(r\), and \(b\), respectively, leading to \(c=(25.49,22.36,7.07)\), \(u_{g}=\{0,11\},u_{r}=\{0,16\},u_{b}=\{0,3\}\). Since the ant \(b\) has the smallest distance cost, minimum distance traveled by an ant is flagged as boldface, it selects the next city--for example, city \(5\), reaching \(c=(25.49,\mathbf{22.36},30.48)\), \(u_{b}=\{0,3,5\}\). Suppose that ant \(r\) selects city \(12\) (\(c=(\mathbf{25.49},42.08,30.48)\), \(u_{r}=\{0,16,12\}\)), \(g\) selects city \(8\) (\(c=(41.09,42.08,\mathbf{30.48})\), \(u_{g}=\{0,11,8\}\)), and \(b\) selects city \(4\) (\(c=(41.09,42.08,\mathbf{40.78})\), \(u_{b}=\{0,3,5,4\}\)). At this point,since the ant \(b\) has the lowest cost, it selects the next city considering only the unpicked cities \(P=\{1,2,6,7,9,10,13,14,15\}\). Figure 4 illustrates this state. Circles or cities with colors: green, red, and blue were selected by the ant with the respective color. Additionally, the filled edges indicate the order of the visited cities. On the other hand, gray circles represent unpicked cities. In this stage, ant \(b\) at node 4 will select one of those cities. Dotted lines with a \"?\" highlight those possibilities. The algorithm continues until the set of all of the unpicked cities is empty, and the ants return to their deposit.
## 3 Results and Discussion
### Effects of SO\({}_{2}\) and H\({}_{2}\)O on NO Removal Activity
#### 3.3.2 Xps
The concentration of Ce\({}^{3+}\) was used to evaluate the number of formed oxygen vacancies. The oxygen vacancies were driven by the transition fromCe\({}^{4+}\)to Ce\({}^{3+}\), which accelerated the transport of active oxygen species [18] and facilitated the reaction. The ratio of Ce\({}^{3+}\)/(Ce\({}^{3+}\)+Ce\({}^{4+}\)) over the used sample(12.7%) was slightly greater than that of the fresh sample (11.6%) (Table 1), due to the following reaction [34; 35]: 2 CeO\({}_{2}\) + 3 SO\({}_{2}\) + O\({}_{2}\)\(\rightarrow\)Ce\({}_{2}\)(SO\({}_{4}\))\({}_{3}\). Since the generated Ce\({}_{2}\)(SO\({}_{4}\))\({}_{3}\) on the surface of the Mn-Ce/Al\({}_{2}\)O\({}_{3}\) catalyst does not decompose at 100 \({}^{\circ}\)C, the Ce\({}_{2}\)(SO\({}_{4}\))\({}_{3}\) would lead to a reduction of the NO conversion (Figure 2). Figure 7c shows the O1s XPS spectra of the fresh and used Mn-Ce/Al\({}_{2}\)O\({}_{3}\) samples. The spectra could be fitted into two sub-bands, the first sub-band at 531.3-532.2 eV corresponding to the surface chemisorbed oxygen (O\({}^{\prime}\)), similar to O\({}_{2}\)\({}^{2-}\) pertaining to the defect-oxide or hydroxyl-like groups [36], and the second sub-band at 528.5-530.5 eV corresponding to the lattice oxygen O\({}^{2-}\) (O\({}^{\prime\prime}\)) [37]. After sulfation, there was a significant increase in the ratio of O\({}^{\prime}\)/(O\({}^{\prime}\) + O\({}^{\prime\prime}\)) in S-Mn-Ce/Al\({}_{2}\)O\({}_{3}\), which was due to the oxygen of the sulfate or the hydroxyl on the surface. Moreover, the accumulation of O\({}^{\prime}\) might result in a suppressed effect in the adsorption and desorption of NO [38]; the adsorption, transformation, and desorption of NO were the controlling steps of the SCR reaction. Consequently, the catalyst activity significantly decreased after SO\({}_{2}\) poisoning. The S 2p XPS of the sulphated Mn-Ce/Al\({}_{2}\)O\({}_{3}\) catalyst (Figure 7d) had two sub-peaks at 168.7 eV and 169.7 eV, assigned to the SO\({}_{3}\)\({}^{2-}\) and SO\({}_{4}\)\({}^{2-}\) species, respectively [39; 40]. This result confirmed that sulfate species were formed over the sulphated Mn-Ce/Al\({}_{2}\)O\({}_{3}\).
# GeoLocator: A Location-Integrated Large Multimodal Model (LMM) for Inferring Geo-Privacy
## 2 Related Work
### Geography-Related LLMs  
The use of LLMs in the spatial science domain has been relatively limited until quite recently. Earlier this year, [PERSON] et al. explored the geographical knowledge and reasoning skills of GPT-4 through a series of experiments, ranging from basic tasks like location estimation to complex applications like route planning and itinerary creation. Their study highlights GPT-4's capability in geospatial reasoning and its potential for diverse applications in geography-related fields [23]. Then [PERSON] et al. developed K2, a specialized language model for geoscience, trained on a tailored corpus, showing enhanced performance in geoscience-specific tasks like question answering and knowledge reasoning, setting a new standard for domain-specific language models [24]. [PERSON] et al. introduced GeoLM, a language model integrating geospatial data with linguistic information, using geo-entity anchors and spatial coordinate embeddings for enhanced geo-entity understanding [25]. [PERSON] et al. developed a method that combines geo-knowledge with GPT models for improved extraction of location descriptions from social media messages during disasters. This approach, using only 22 training examples, achieved over 40% improvement in accuracy compared to standard named-entity recognition methods, significantly aiding in the rapid and efficient response to disaster scenarios [26]. Recently, [PERSON] et al. assessed the geospatial knowledge and reasoning capabilities of LLMs, using experiments on geoconodinate prediction, geospatial preposition analysis, and multidimensional scaling, revealing their potential in geospatial reasoning tasks [27]. Extending from the existing research, we reformulated the regular LMMs to create our location-integrated model, GeoLocator, and tested out its capacity to infer geospatial information and geo-privacy.
###### Abstract  
In the fields of physics and engineering, it is crucial to understand phase transition dynamics. This field involves fundamental partial differential equations (PDEs) such as the Allen-Cahn, Burgers, and two-dimensional (2D) wave equations. In alloys, the evolution of the phase transition interface is described by the Allen-Cahn equation. Vibrational and wave phenomena during phase transitions are modeled using the Burgers and 2D wave equations. The combination of these equations gives comprehensive information about the dynamic behavior during a phase transition. Numerical modeling methods such as finite difference method (FDM), finite volume method (FVM) and finite element method (FEM) are often applied to solve phase transition problems that involve many partial differential equations (PDEs). However, physical problems can lead to computational complexity, increasing computational costs dramatically. Physics-informed neural networks (PINNs), as new neural network algorithms, can integrate physical law constraints with neural network algorithms to solve partial differential equations (PDEs), providing a new way to solve PDEs in addition to the traditional numerical modeling methods. In this paper, a hard-constraint wide-body PINN (HWPINN) model based on PINN is proposed. This model improves the effectiveness of the approximation by adding a wide-body structure to the approximation neural network part of the PINN architecture. A hard constraint is used in the physically driven part instead of the traditional practice of PINN constituting a residual network with boundary or initial conditions. The high accuracy of HWPINN for solving PDEs is verified through numerical experiments. One-dimensional (1D) Allen-Cahn, one-dimensional Burgers, and two-dimensional wave equation cases are set up for numerical experiments. The properties of the HWPINN model are inferred from the experimental data. The solution predicted by the model is compared with the FDM solution for evaluating the experimental error in the numerical experiments. HWPINN shows great potential for solving the PDE forward problem and provides a new approach for solving PDEs.  
Keywords:physics-informed neural networks; numerical modeling; PDEs; hard constraint; phase transition dynamics +
Footnote †: journal: Applied sciences
# The Thermal Analysis and Heat Dissipation Structure Optimization of a Propeller Driver System
## 2 Thermal Analysis of Propeller Driver
### Model Experiment Verification  
In order to verify the accuracy of the model used in numerical analysis, experiments were carried out for the propeller driver with full power. The surrounding temperature of testing was set as 25 \({}^{\circ}\)C. The experimental system consists of two power supplies, one thermal imaging instrument and the test propeller drivers. A schematic diagram of the experimental system is shown in Figure 9. As shown in Figure 9, a low-voltage DC power supply (A-BF SS-6020 KDS) was used to supply a voltage of 12 V for the upper three driver PCB layers. A high-voltage DC power supply (ANS-JP60020D) was used to provide a voltage of 300 V for the 4 th PCB layer. A thermal imager was used to measure the surface temperature of the 1 st PCB layer, the 2 nd PCB layer, and the 3 rd PCB layer. Since the surface temperature of the 4 th PCB layer is difficult to measure accurately, the temperature at the junction of the base and the heat sink was measured.  
Figure 8: Temperature distribution of heat sink and base.  
Figure 7: The temperature distribution of the 4 th PCB layer components.  
Figure 9: Schematic diagram of the experimental system.  
The maximum surface temperature of the 1 st PCB layer, the 2 nd PCB layer, the 3 rd PCB layer, and the connection between the base and the heat sink in the experimental measurement results shown in Figure 11 are compared with the maximum temperature calculated via simulation. The comparison results are shown in Table 4. It shows that the experimental results are consistent with the simulation results. The maximum temperature error is 2 \({}^{\circ}\)C, and the average absolute error is about 0.75 \({}^{\circ}\)C.
## 4 Simulation Results and Analysis
### Real Data Tests  
To confirm the feasibility of the proposed algorithm, real data tests are conducted. The BeiDou B1I receiver used was developed by the Navigation Research Center, Nanjing University of Aeronautics and Astronautics (NRC, NUAA) and the hardware structure was developed by Shanghai Yuzhi. As shown in Figure 14, the receiver test platform consists of a DSP-TMS320C6713B and an FPGA-EP4 CE115F23, and an AD8347 is used as the quadrature down-conversion mixer. The parameters used are provided in Table 3. The antenna is placed on the roof of College of Automation Engineering building of NUAA and the signal is processed in real time.  
In the BeiDou B1I receiver, repeated acquisition based on 1-2 ms of coherent integration is used for signal acquisition. DLL and PLL assisted by FLL are used for signal tracking. The proposed differential coherent bit synchronization algorithm is processed after signal acquisition and tracking.  
The number of visible satellites is ten, including five GEO, three IGSO and two MEO. Take satellite #9 as an example and the result of tracking loop is shown in Figure 15.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline IF frequency & 4.098 MHz \\ Sampling frequency & 62 MHz \\ DLL bandwidth & 5 Hz \\ PLL bandwidth & 20 Hz \\ FLL bandwith & 40 Hz \\ Differential coherent delay time & 20 ms \\ Differential coherent integration time & 40 ms \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters used in real data tests.  
Figure 14: The BeiDou B1I receiver test platform.  
The proposed bit synchronization algorithm is processed based on the coherent integration results of tracking loops. Figure 16 is the result of differential coherent algorithm with a delay time of 20 ms and the signs are consistent with transitions of the navigation data. The amplitudes of DCML algorithm at all possible bit boundaries are shown in Figure 17 and the bit boundary estimation is one. After bit synchronization, the bits are decoded and position calculation is realized.  
Figure 16: The differential coherent result of satellite #9.  
Figure 15: The tracking result of satellite #9.
## 4 System Presentation and Effectiveness Analysis
### System Effectiveness Analysis
In summary, in the two experiments of OntoCBRA, the effectiveness at Level 1 improved by 7.2% ((53.8 - 50.2)/50.2 \(\times\) 100% = 7.2%), the number of inquiries at Level 2 increased by 2.5% ((1342.6 - 1310.4)/1310.4 \(\times\) 100% = 2.5%), the number of data trimming at level was reduced 1.4% ((901.8 - 914.6)/914.6 \(\times\) 100% = -1.4%), and the external query also decreased 3.0% ((236.2 - 243.4)/243.4 \(\times\) 100% = -3.0%). Although the effectiveness was slightly improved, the improvements in effectiveness of Level 1 and Level 2 meant the system accumulated more experience in processing. In addition, the decreases of case trimming and external query at Level 3 and Level 4 proved the learning effectiveness of this system. This study believes that the ability of the system's case solving will get better over time. As the content of the case base becomes more comprehensive, the ability to solve problems of this system will increase. If the burdens at Level 3 and Level 4 can be reduced, then the \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Test** & **Total** & **Level 1** & **Level 2** & **Level 3** & **Level 4** & **Total Number** & **The Amount** \\
**Order** & **Number of** & **Lotential** & **Case** & **Case** & **Experts** & **Inquiries of** & **Conditioned** \\
1 & 6301 & 51 & 1288 & 913 & 270 & 2522 & 40.0 \\
2 & 6227 & 67 & 1339 & 901 & 210 & 2517 & 40.4 \\
3 & 6111 & 49 & 1298 & 927 & 243 & 2517 & 41.2 \\
4 & 6289 & 35 & 1339 & 897 & 249 & 2520 & 40.1 \\
5 & 6157 & 49 & 1297 & 935 & 245 & 2526 & 41.0 \\ \hline \hline average & 6217 & 50.2 & 1310.4 & 914.6 & 243.4 & 2520.4 & 40.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Analysis results of performance effectiveness. Figure 20: Original system case database. effectiveness of OntoCBRA will be better, and the operational burden of the back-end database could be diminished simultaneously.
## 2 Related Work
### The Case of a Single Interfering Node  
In networks that are not very dense, the number of interfering nodes around a receiver is usually small. Consider the case of a single interfering node, e.g., the one that is the closest to the tagged receiver among all interfering notes. The interfering power of this node may dominate the overall interfering power from all other interfering nodes. Letting the distance between the single interfering transmitter and the target receiver be \(r\), the success probability in (7) is reduced to:  
\[P_{succ}=\Pr\left\{\frac{Y_{i}}{Y_{r}}<\frac{1}{T_{SIR}}\left(\frac{r}{d} \right)^{\beta}\right\}=\Pr\left\{Z<\frac{1}{T_{SIR}}\left(\frac{r}{d}\right)^ {\beta}\right\} \tag{10}\]where \(Z=Y_{i}/Y_{r}\) represents a log-normal random variable with zero mean and variance \(2\sigma^{2}\). Applying the logistic distribution, we find the success probability to be:  
\[P_{succ}=\left\{\left[T_{SIR}\left(\frac{d}{r}\right)^{\beta}\right]^{\pi/( \sigma\sqrt{6})}+1\right\}^{-1} \tag{11}\]  
As an example, we plot the success probability \(P_{succ}\) for different \(\sigma\)'s by evaluating (11) in Figure 2. The parameters are selected as \(T_{SIR}=10\) and \(\beta=4\) to emulate a typical wireless sensor network scenario. The mean interference range \(R_{I}=d\sqrt[3]{T_{SIR}}=35.6\) m. We find that \(P_{succ}\) is a decreasing function of \(\sigma\) when \(r>R_{I}\), but an increasing function of \(\sigma\) when \(r<R_{I}\). For example, consider the two extreme cases when \(\sigma=0\) and \(\sigma=\infty\). When \(\sigma=0\), the channel is a deterministic one; the received power depends on the distance only and does not have any randomness. Equation (11) becomes a _step function_ as: \(P_{succ}=\left\{\begin{array}{ll}0,&r<R_{I}\\ 1,&r>R_{I}.\end{array}\right.\) That is, there is a perfect channel with the distance is within \(R_{I}\), and there is no connection at all when the distance is larger than \(R_{I}\). This is the deterministic disk model used in many prior studies. On the other hand, when \(\sigma=\infty\), the success probability tends to be \(1/2\) in all cases. It means that the success probability is no longer dependent on the distances \(d\) and \(r\).