esa-sceva/satcom-chunk-collection huggingface dataset:
# Propagation, Scattering and Defect Detection in a Circular Edge with Quasi-Edge Waves
## 2 Numerical Study  
In this section, a 3D FE approach for investigating the effect of the waveguide curvature on the propagation characteristic of \(QES_{0}\) is presented in Section 2.1, followed by the corresponding simulation results in Section 2.2. Section 2.3 details the 3D FE model for investigating the scattering characteristics of the \(QES_{0}\) wave mode at an edge defect.
## 2 Materials and Methods
### Model Description for GPP Estimation
#### 2.1.2 VI-Based Models  
The GPP estimates from TG (\(GPP_{TG}\)) and MTG (\(GPP_{MTG}\)) can be described as:  
\[GPP_{TG}=m_{TG}\times ScaledEVI\times h(LST_{ft}) \tag{22}\]  
\[GPP_{MTG}=m_{MTG}\times ScaledEVI\times h(LST_{mt})\times Frad \tag{23}\]  
\[ScaledEVI=EVI-0.1 \tag{24}\]  
\[h(LST)=\min(\frac{LST-T_{n}}{T_{o}-T_{n}},\frac{T_{n}-LST}{T_{m}-T_{o}}) \tag{25}\]  
where \(m\) (\(m_{TG}\) or \(m_{MTG}\)) is a model parameter associated with GPP and the combination of LST (\(LST_{fh}\) or \(LST_{mt}\)) and EVI scalar (\(ScaledEVI\)); \(Tn\), \(To\), and \(Tm\) are the minimum, optimum, and maximum land surface temperatures for vegetation photosynthesis; the radiation-corrected factor (\(Frad\)) is a function associated with FPAR and the accumulated value of local solar zenith angle from sunrise (\(t_{0}\)) to the satellite overpass (\(t_{s}\)):  
\[Frad=1-\exp(-FPAR\times\sum_{t=t_{0}}^{t_{s}}COS(\theta_{local-mt,t})) \tag{26}\]  
LST over mountainous areas is modified by considering the effect of elevation on temperature:  
\[LST_{mt}=LST_{ft}+Lap\times\Delta ELE \tag{27}\]  
where \(LST_{fh}\) represents the MODIS LST value of a single coarse pixel (\(\sim\)1 km), \(\Delta ELE\) is the elevation difference between the coarse pixel and its subpixel, and \(Lap\) is the temperature lapse rate (set as \(-\)8 \({}^{\circ}\)C km\({}^{-1}\)).
# Performance-Based Evaluation of Courtyard Design in China's Cold-Winter Hot-Summer Climate Regions
## 2 Method for Courtyard Design Optimization
### Courtyard Design Optimization
#### 2.3.1 Adjusting the Spatial Layout of Courtyards  
Orientation has a direct impact on the ecological effect of courtyard buildings. A reasonable spatial layout of a courtyard can control solar heat. Moreover, the natural ventilation system of a courtyard controls convective heat transfer. Designers tend to put an emphasis on the layout between the peripheral landscape environment and the plane function of courtyards, but pay little attention to their ventilation effect. Ecological effects of the courtyard layout have not yet been systematically studied [1; 44].  
This study attempts to adjust the courtyard orientation of the Expert Building Area of the Jiangnan Furniture Museum to ascertain the ecological effect of its spatial layout. As shown in Figure 3, in the initial design, the entire courtyard opening of the Expert Building faces west; enclosure strength on the northwestern corner is not high. In the improved design, the courtyard is opened toward the southeast to maximize ventilation in summer using wind pressure--and it closes access toward the northwest to avoid cold winter wind currents. In further refinements, to meet other basic requirements, the courtyard opening is adjusted to face the east, and the enclosure degree on the northwestern corner is increased, which improves the courtyard wind environment compared with the initial design.
## 5 Discussion
In this regard, the future research can explore the use of lightweight LLMs (such as DistilGPT and TinyLLM) or model compression technology (such as knowledge distillation and quantification) to reduce the computational overhead and improve the system's deployment in the actual teaching environment. At the same time, the combination of edge computing architecture also helps to alleviate the pressure on the server side. Thirdly, in order to further improve the personalization and accuracy of recommendation, the system can introduce more advanced technologies in the future, such as self-supervised learning, to reduce the dependence on labeled data, and the knowledge map to model the relationship between knowledge points, so as to realize more intelligent knowledge path planning. In addition, when discussing the personalized learning path recommendation system based on LLM technology, this paper has to face the ethical dilemma and practical consideration presented by using physiological signals and emotional variables. First of all, at the ethical level, privacy protection has become the primary concern. Because highly sensitive personal information is involved, it is very important to ensure that these data are stored safely and used only for the intended purpose. It is an essential step to formulate a clear data use policy and obtain the informed consent of students or their guardians. At the same time, there is also the risk of data misuse, which requires the establishment of a strict supervision mechanism to prevent such abuse and prevent physiological and emotional data from being used for non-educational evaluation or discriminatory purposes. Continuous monitoring of students' emotional state may also place extra psychological pressure to students, so we must consider the possible negative impact of this practice on mental health and take measures to alleviate any potential harm. From the practical feasibility point of view, although the system shows great potential, it faces technical challenges to collect high-quality physiological and emotional data in the actual educational environment. Therefore, it is particularly important to understand and solve these concerns before popularizing this kind of system. Implementing such a complex personalized learning path recommendation system requires not only advanced technical support, but also adequate training and support services to help educators make effective use of these tools. Therefore, before popularizing the system, it is necessary to carry out field experiments to verify its feasibility in real teaching scenarios. In addition, a supporting teacher training and technical support system is needed to help educators better understand and use these intelligent tools.
# Sliding Mode Control of Servo Feed System Based on Fuzzy Reaching Law
## 2 Flexible Dynamics Modeling of the Feed System
### Fuzzy Convergence Law Sliding Mode Controller Design  
#### 3.2.1 Design of a Novel Fuzzy Exponential Convergence Law  
In the traditional exponential convergence law sliding mode controller, the convergence law parameters \(\varepsilon\) and \(h\) are scalars, and the values are fixed. The different values of the convergence law parameters affect the arrival time of the system at the sliding mode surface and the state after arrival, specifically \(\varepsilon\) affects the arrival time and \(h\) affects the convergence speed. To enhance system robustness and anti-interference capability, \(\varepsilon\) and \(h\) should be adaptively adjusted according to system changes to meet the requirements of high-performance controllers. The expression of the traditional exponential convergence law is:  
\[\stackrel{{\bullet}}{{\sigma}}=-\varepsilon{\rm sgn}(\sigma)-h \sigma\;\;(\varepsilon,h>0) \tag{12}\]  
From the expressions, the values of \(\varepsilon\) and \(h\) can be dynamically adjusted to weaken the system chattering, with \(\varepsilon\) ensuring a finite arrival time and \(h\) ensuring a fast arrival. Therefore, to ensure the rapidity of the convergence motion and to suppress the chattering, the parameters \(\varepsilon\) and \(h\) need to be selected appropriately. With \(\sigma\) and \(\stackrel{{\bullet}}{{\sigma}}\) as the input fuzzy variables of the system and \(\varepsilon\) and \(h\) as the fuzzy outputs, define \(\varepsilon=fuzzy_{n}(\sigma,\stackrel{{\bullet}}{{\sigma}})\), \(h=fuzzy_{n}(\sigma,\stackrel{{\bullet}}{{\sigma}})\), such that \(\varepsilon\) and h can be adaptively adjusted according to the system changes.  
The fuzzy convergence law can effectively weaken the jitter of the system, but the traditional exponential convergence law uses the discontinuous symbolic function \({\rm sgn}()\) to make the system state variables switch back and forth under the sliding mode surface, and as long as the control contains the symbolic function of \({\rm sgn}()\), chattering is inevitably present in the control output. The saturation function can effectively weaken the chattering and make the output smooth and bounded. In this paper, the convergence law is designed using hyperbolic tangent function (a saturation function) instead of the sign function, and the hyperbolic tangent function is as follows:  
\[{\rm tanh}(s)=\frac{{\rm exp}(\sigma)-{\rm exp}(-\sigma)}{{\rm exp}(\sigma)+{ \rm exp}(-\sigma)} \tag{13}\]  
A comparison of the symbolic and hyperbolic tangent functions is shown in Figure 3. The hyperbolic tangent function can make the switching process continuous and smooth, which is very important to reduce chattering.  
Then the new fuzzy exponential convergence law is designed as follows:  
\[\overset{\bullet}{\sigma}=-fuzzy_{n}(\sigma,\overset{\bullet}{\sigma})\tanh( \sigma)-fuzzy_{n}(\sigma,\overset{\bullet}{\sigma})\,\sigma \tag{14}\]
## 4 Results and Discussion
### Analysis of the Subsoiling Resistance
#### 4.1.2 The Effect of Subsoiler Tip Type on Subsoiling Resistance  
The data with vibration frequency above 120 Hz were summarized. These subsoiling resistances were the smallest at these frequencies, and the variation law of the minimum subsoiling resistances of six subsoiler tips under three speeds was obtained (Figure 12). From the data, it could be observed that the subsoiler tip type had a more significant influence on the subsoiling resistance. Combining the three speeds and comparing shovel tip D, shovel tip E, and shovel tip F, the shovel tip E had the minimum resistance, followed by the shovel tip F, and the shovel tip D had the maximum resistance. Compared with shovel tip D, shovel tip E could reduce resistance by 33.8% on average, and shovel tip F could reduce resistance by 25.3%. Compared with shovel tip A, shovel tip B could reduce resistance by 29.4%, and shovel tip C could reduce resistance by 10.4% on average.  
In this experiment, the shovel tip E with a non-smooth surface of bionic antilo showed an excellent resistance reduction effect. Because the contact state between soil and the subsoiler surface was changed by means of the surface reshaping, the non-smooth subsoiler tip surface would cause soil particle disturbance during movement so that the soil adhesion force changed from anisotropic to isotropic trend and reduced resistance. Compared with shovel tip E and shovel tip F, both non-smooth surfaces showed better resistance reduction.  
Compared with tip D, tip E, tip F and tip A, tip B and tip C, the upper convex surface subsoiler tip had less resistance, and the tip resistance could be reduced by, at most, 24.2%. The up-convex curved shovel tip was able to deflect the soil on both sides during the subsoiling process, making it less likely to form soil nuclear and reducing resistance. The  
Figure 12: The minimum resistance variation law of vibration subsoiling at three speeds for six subsoiler tips.  
results of six types of subsoilers at three speeds and 30 cm depth conditions showed that the non-smooth surface had a better resistance reduction effect with a maximum resistance reduction rate of 33.8%.
## 5 Conclusions  
We used geographic object-based image analysis (GEOBIA) methods to classify guano stains from very high spatial resolution satellite imagery and closely examined the transferability of knowledge-based GEOBIA rules across different study sites focusing on the same semantic class. We find that object-based methods incorporating spectral, textural, spatial, and contextual characteristics from very high spatial resolution imagery are capable of successful detection of guano stains. Reapplication of the master ruleset on candidate scenes without modifications produced inferior classification results, while tasking of adapted rules produced comparable or superior results compared to the reference image at the expense of operator involvement, time, and transferability. Our findings reflect the necessity of employing a systematic experiment to not only gauge the potential of GEOBIA in a certain classification task but also to identify possible shortcomings, especially from an operational perspective. As EO becomes a more routine method of surveying Antarctic wildlife, findings of this exploratory study will serve as baseline information for Antarctic wildlife researchers to identify opportunities and challenges of GEOBIA as a best-available mapping tool in automated wildlife censusing. In future research, we aim to transform the proposed conceptual sketch--\"image-to-assessment pipeline\"--into an operational architecture.  
[PERSON] and [PERSON] would like to acknowledge funding provided by the National Science foundation's Office of Polar Programs (PLR) and Geography and Spatial Sciences (GSS) divisions (Award No. 1255058). We would also like to thank the University of Minnesota's Polar Geospatial Center for imagery processing support.  
[PERSON] designed research plan, processed image data, performed analysis. [PERSON] involved manuscript preparation and classification accuracy assessment as a domain expert.  
The authors declare no conflict of interest.
# Adaptive Energy Balanced Routing Strategy for Wireless Reschargeable Sensor Networks
## 6 Simulation and Evaluation
### The Impact of Network Size on Network Performances  
In Figure 4, \(Tc=10\), nodes number varies from 200 to 1000, and the number of charging points on \(r_{\rm v}\) is \(4v\), \(5v\), \(6v\), \(7v\), \(8v\). As nodes number increases, the network lifetime comes to a maximum value decreases. The increase of nodes number means more routing load, more energy during charging part and more nodes with high charging efficiency. Before nodes number increase to a certain value, such as 400 for \(r_{\rm v}=6v\), routing load is not that heavy, and node distribution is more uniform, which makes network more balanced. More nodes with high charging efficiency help to lower the average hop number for choosing nodes closer to sink node. However, nodes number continues to increase after the certain value, the heavy routing tasks makes nodes consume energy quickly. Nodes with low charging efficiency, which consume more, will increase hop number and reduce the whole charging efficiency and network balance. Figure 4a shows that the certain number gets larger when \(r_{\rm v}\) changes from \(4v\) to \(8v\). This is due to that the number of nodes needed for uniform distribution around charging points to obtain more nodes with high charging efficiency is larger.  
Figure 3: (**a**) Network lifetime over charging cycle; (**b**) network balance over charging cycle; (**c**) average hops over charging cycle.
# A Deep Reinforcement Learning-Based Decision-Making Approach for Routing Problems
## 4 Experiments
### Ablation Study  
To evaluate the effectiveness of the proposed DRL-DM architecture, we conduct an ablation study focusing on its three key components: the BNF module, the GT block, and the dynamic-aware context embedding. Tables 5 and 6 present the routing performance resulting from the incremental integration of these components into a vanilla attention model (i.e., AM [22]) across both TSPs and CVRPs. For the TSP100 benchmark, incorporating the BNF module, GT block, and dynamic-aware context embedding individually results in average route length reductions of 0.26%, 0.78%, and 1.03%, respectively. When all components are combined, the optimality gap is reduced to 0.52%, representing a 2.06% improvement over the baseline. A similar trend is observed for CVRPs. Specifically, the complete integration of all three components yields optimality gap reductions of 4.59%, 5.30%, and 6.65% on CVRP20, CVRP50, and CVRP100, respectively, when compared with the vanilla version. These results clearly demonstrate the effectiveness and scalability of the proposed architectural enhancements across routing problems of varying complexity.  
Figure 7: Visualization of the routing performance between three comparative learning-based methods and our proposed DRL-DM in the real-life logistics case in Xi’an, China.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Components of Our DRL-DM**} & \multicolumn{3}{c}{**TSP20**} & \multicolumn{3}{c}{**TSP50**} & \multicolumn{3}{c}{**TSP100**} \\ \hline
**BNF** & **GT** & **Context** & **Length** & **Gap** & **Time** & **Length** & **Gap** & **Time** & **Length** & **Gap** & **Time** \\ \hline  & & & 3.86 & 0.52\% & 2 s & 5.80 & 1.75\% & 5 s & 7.96 & 2.58\% & 14 s \\ \hline ✓ & & & 3.86 & 0.52\% & 2 s & 5.79 & 1.58\% & 8 s & 7.94 & 2.32\% & 21 s \\  & ✓ & & 3.85 & 0.26\% & 3 s & 5.77 & 1.23\% & 10 s & 7.90 & 1.80\% & 26 s \\  & & ✓ & 3.85 & 0.26\% & 3 s & 5.76 & 1.05\% & 11 s & 7.88 & 1.55\% & 30 s \\ \hline ✓ & ✓ & & 3.85 & 0.26\% & 4 s & 5.76 & 1.05\% & 13 s & 7.87 & 1.42\% & 33 s \\ ✓ & & ✓ & 3.85 & 0.26\% & 4 s & 5.74 & 0.70\% & 14 s & 7.86 & 1.29\% & 37 s \\  & ✓ & ✓ & 3.84 & 0.00\% & 5 s & 5.73 & 0.53\% & 16 s & 7.83 & 0.90\% & 42 s \\ \hline ✓ & ✓ & ✓ & 3.84 & 0.00\% & 5 s & 5.71 & 0.18\% & 19 s & 7.80 & 0.52\% & 49 s \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation study of different components of our DRL-DM on TSPs.
# Sensitivity of Cell Cultures on Time-Varying Low-Frequency Magnetic Field Changes
## 3 Results and Discussion
### The Validity of Resonance Point  
The experimental data achieved during the first series of experiments were initially analyzed using descriptive statistics. Considering the biological response of all five exposed samples to be affected by the same LF MF at a time (the same logic is applied to control non-affected samples), the average effect rate is calculated, resulting in the maximum average effect rate of S = 50.4 \(\pm\) 9.2% at a frequency of 34.89 Hz. The minimum average effect rate of S is 10.8 \(\pm\) 3.9% at a frequency of 27.39 Hz. The influence of the frequency on the response rate parameter S [%] can be addressed using a one-factor analysis of variance. The resulting \(p\)-value for the F-test is 0.0338, thus rejecting the null hypothesis, which means that variances are statistically significant. Based on the ANOVA results at the \(\alpha=0.05\) significance level, it can be argued that different frequencies have a significant effect on the mean value of the response rate parameter \(S\) [%].  
The Bonferroni method of multiple comparisons presented in Figures 6 and 7, identified four homogeneous subgroups within which the response rate of the system shows no statistically significant differences in terms of biological effect; that is subgroups 1, 2, 3, and 4 (Table 3). This means, that the corresponding subgroup contains frequencies causing a similar or the same biological response.  
Figure 6: Box plot of 1 series of experiments.  
Figure 7: Means and 95% Bonferroni intervals of 1 series of experiments.  
Within the investigated 11 frequencies, there are 6 pairs, within which there is a statistically significant difference between the mean values of the effect rate S [%]: pairs 5, 6, 7, 8, 9, and 10 (Table 3).  
The hypothesis, Section 3.4 letter (a), that the maximum response rate parameter \(S\) [%] is at a frequency of 29.89 Hz (f6), with respect to conducted experimental data, could not be confirmed. Instead, according to the results, the maximum effect is at a frequency of 34.89 Hz (f11) and a frequency of 24.89 Hz (f1).
###### Abstract  
Providing accurate information about bus travel times can help passengers plan their itinerary and reduce waiting time. However, due to various uncertainty factors and the sparsity of single-route data, traditional travel time predictions cannot accurately describe the credibility of the prediction results, which is not conducive to passengers waiting based on the predicted results. To address the above issues, this paper proposes a bus travel time prediction intervals model based on shared road segments, multiple routes' driving style similarity, and the bootstrap method. The model first divides the predicted route into segments, dividing adjacent stations shared by multiple routes into one section. Then, the hierarchical clustering algorithm is used to group all drivers in multiple bus routes in this section according to their driving styles. Finally, the bootstrap method is used to construct a bus travel time prediction interval for different categories of drivers. The travel time data sets of Shenyang 239, 134, and New Area Line 1 were selected for experimental verification. The experimental results indicate that the quality of the prediction interval constructed using a data set fused with multiple routes is better than that constructed using a single-route data set. In the two cases studied, the MPIW of the three time periods decreased by 101.04 s, 151.72 s, 33.87 s, and 126.58 s, 127.47 s, 17.06 s, respectively.  
bus travel time prediction; prediction intervals; multiple bus routes; hierarchical clustering; driving style similarity
# Design of Mega-Constellations for Global Uniform Coverage with Inter-Satellite Links
## 2 Mega-Constellation Design Model
### Constraints on Collision Avoidance
\begin{array}{c}\cos\rho=(k_{1}\sin u_{1}-k_{4}\sin\Delta u)\sin^{2}u_{1}+(k _{2}\cos\Delta u+k_{3}\sin\Delta u)\cos^{2}u_{1}\\ +(k_{1}\sin\Delta u-k_{2}\sin\Delta u+k_{3}\cos\Delta u+k_{4}\cos\Delta u)\sin u _{1}\cdot\cos u_{1}\end{array}  \tag{27}\]  
Figure 3: Schematic diagram of the spatial geometry of any two satellites in the ECI coordinate system. where  
\[\left\{ \begin{array}{c}a=k_{1}\cos\Delta u-k_{4}\sin\Delta u\\ b=k_{2}\cos\Delta u+k_{3}\sin\Delta u\\ c=k_{1}\sin\Delta u-k_{2}\sin\Delta u+k_{3}\cos\Delta u+k_{4}\cos\Delta u\\ \end{array} \right.\]  
The final equation for \(\cos\rho\) concerning \(u_{1}\) is as follows:  
\[\cos\rho=a\sin^{2}u_{1}+b\cos^{2}u_{1}+c\sin u_{1}\cdot\cos u_{1} \tag{28}\]  
The \(\cos\rho\) in Equation (28) can be transformed as:  
\[\cos\rho=\frac{a+b}{2}+\frac{b-a}{2}\cos(2u_{1})+\frac{c}{2}\sin(2u_{1}) \tag{29}\]  
For the expression \(f(x,y)=x\cos\theta+y\sin\theta\) with \(\theta\in[0,2\pi]\), the \(f(x,y)\) can take the maximum value when the unit vector \((\cos\theta,\sin\theta)\) and the vector \((x,y)\) are parallel and in the same direction. At this point, the vector \((\cos\theta,\sin\theta)\) can be expressed as:  
\[(\cos\theta,\sin\theta)=\left(\frac{x}{\sqrt{x^{2}+y^{2}}},\frac{y}{\sqrt{x^{ 2}+y^{2}}}\right)\]  
At this particular point, the \(f(x,y)\) is expressed as:  
\[x\cdot\frac{x}{\sqrt{x^{2}+y^{2}}}+y\cdot\frac{y}{\sqrt{x^{2}+y^{2}}}=\sqrt{x ^{2}+y^{2}}\]  
Applying this result to our maximization problem yields, the maximum value of \(\cos\rho\) is:  
\[\max(\cos\rho)=\frac{a+b}{2}+\frac{\sqrt{\left(b-a\right)^{2}+c^{2}}}{2} \tag{30}\]  
Similarly, the minimum value of \(\cos\rho\) can be obtained as follows:  
\[\min(\cos\rho)=\frac{a+b}{2}-\frac{\sqrt{\left(b-a\right)^{2}+c^{2}}}{2} \tag{31}\]  
When \(\cos\rho\) takes the minimum (maximum) value, the geocentric angle between the two satellites reaches the maximum (minimum), and the distance between the two satellites is also the maximum (minimum). The minimum distance between the two satellites is:  
\[d_{min}=2(R_{e}+H)*\sin\left(\frac{1}{2}\arccos\left(\frac{a+b}{2}+\frac{\sqrt{ \left(b-a\right)^{2}+c^{2}}}{2}\right)\right) \tag{32}\]  
It is easy to obtain the maximum distance between any two satellites. \[d_{max}=2(R_{e}+H)*\sin\left(\frac{1}{2}\arccos\left(\frac{a+b}{2}-\frac{\sqrt {\left(b-a\right)^{2}+c^{2}}}{2}\right)\right) \tag{33}\]  
If the minimum distance between two satellites is less than the safe distance. Then, adjust the argument of latitude of either satellite until the minimum distance between it and all other satellites is greater than the safe distance.