esa-sceva/satcom-chunk-collection huggingface dataset:
# Bus Travel Time Prediction Based on the Similarity in Drivers' Driving Styles
## 5 Conclusions  
In this work, a method for predicting bus travel times by clustering drivers' driving styles was presented. The historical travel times of different bus drivers were first clustered, and then the prediction of the travel time was made using the historical dataset of the corresponding driver, which achieved the objective of improving the speed and precision of the travel time prediction. Our experiments were conducted using a large-scale dataset of real-world data collected in Shenyang, China. The experimental results indicate that the bus travel time prediction method (Model 2) based on the clustering of drivers' driving styles proposed in this research paper increases the average accuracy by 13.4%. In addition, it is found that the performance of Model 2 and Model 3 (the models that are based on the clustering results after the dataset selection) is better than that of Model 1 (the model using only a single driver history dataset) and Model 4 (the traditional method using all the datasets). Among all the models, Model 2 has the best prediction accuracy, which is in contrast to Model 1, which has the worst prediction accuracy. We believe that the main reason for this difference is the sparsity of the dataset of individual drivers. At the same time, it is also found that the prediction accuracy of Model 4 is not the best, indicating that it is not correct to assume that the more data, the better the accuracy of the prediction results. We believe that the main reason for this is that using data from all drivers can mask differences in driver behaviors.  
In our future work, we will consider more behavioral factors, such as bus drivers' acceleration behavior, braking behavior, and overtaking behavior, to further study the impact of bus drivers' driving styles on travel time.  
All authors contributed to the study's conception, the design of the experiments, and the paper's structure. [PERSON] performed the experiment analysis and wrote the first draft of the manuscript. All authors participated in the revision and proofreading of the paper. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Key Project of National Natural Science Foundation of China (U1908212), the Central Government's Guided Local Science and Technology Development Fund Project (1653137155953), and Liaoning Province's \"Takes the Lead\" Science and Technology Research Project (2021 jh1/10400006).  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 4 System Model
### Forwarder Node Selection  
According to our proposed system model, neighbor node is selected on the optimality basis. The criteria for optimality in this scenario for a node is to have efficient energy and must lead towards the sink node. Optimal energy point for a node is calculated using the following formula:  
\[DistSD_{op}=\sigma\sqrt{\frac{B}{(1-2^{(1-\sigma)})}}. \tag{1}\]  
Here, \(DistSD_{op}\) represents the optimal distance from source to the destination node. Where, \(\sigma\) is a path loss constant and calculation for \(B\) is as follows:  
\[B=2\times E_{com}. \tag{2}\]  
where, \(E_{com}\) represents the communication energy. The slope for straight line \(m\) from source node to the destination node is calculated as:  
\[m=\frac{y_{S}-y_{D}}{x_{S}-x_{D}}. \tag{3}\]  
Here, \(y_{S}\), \(y_{D}\), \(x_{S}\) and \(x_{D}\) are the coordinates of sending and receiving nodes. Equation (4) describes the path loss during attenuation of signal. This equation is taken from [22].  
Figure 4: Depth adjustment.  
Figure 5: Structure of hello message.  
\[A(d,f)=DistSD_{op}{}^{k}a(f)^{DistSD_{op}}, \tag{4}\]  
where, \(a(f)\) is the absorption coefficient, \(k\) is a spreading factor and \(DistSD_{op}\) indicates the optimal distance from a source to the destination node. The absorption coefficient is described by [PERSON]'s formula.  
\[10 loga(f)=(\frac{0.11\times f^{2}}{1+f^{2}})+(\frac{44\times f^{2}}{4100+f^{2} })+2.75\times 10^{-4}f^{2}+0.003. \tag{5}\]  
Transmitting probability for any node with distance d is calculated using Equation (6).  
\[P(d,m)=(1-P(DistSD_{op}))^{m}. \tag{6}\]  
Turbulence noise \(Noise_{tur}\), shipping noise \(Noise_{ship}\), wind noise \(Noise_{wind}\) and thermal noise \(Noise_{ther}\) are calculated using Equations (7)-(10), respectively.  
\[Noise_{tur}=10^{((17-30\times log10(f))\times 0.1)}. \tag{7}\]  
\[Noise_{ship}=10^{((40+20\times(shp-0.5)+26\times log10(f)-60\times log10(f+0. 03))\times 0.1}. \tag{8}\]  
\[Noise_{wind}=10^{((50+((75\times wind))^{0.5})+(20\times log10(f))-40\times log 1 0(f+0.4))}. \tag{9}\]  
\[Noise_{ther}=-15+(20\times log10(f)). \tag{10}\]  
Whereas, total noise \(Noise_{total}\) is calculated by adding all noises as:  
\[Noise_{total}=Noise_{Tur}+Noise_{ship}+Noise_{wind}+Noise_{ther}. \tag{11}\]
# TGA-GS: Thermal Geometrically Accurate Gaussian Splatting
## 3 Methods
### Loss  
For the entire training process, we designed multiple loss functions to impose constraints. Based on the characteristics of input images, we adapted the original L1 and Structural Similarity Index (SSIM) losses from the Gaussian Splitting (GS) network. When processing low-light RGB inputs, inspired by the Lit-MSE loss in Aleth-NeRF [83], we modified the standard L1 loss into an \(L_{\text{1-low}}\) loss to enhance adaptability to dark environments. The formulation is given by:  
\[L_{\text{1-low}}=\left\|\Phi(\hat{C}+\epsilon)-\Phi(C+\epsilon)\right\|_{1}\]  
where \(\epsilon=10^{-3}\) follows the configuration in Aleth-NeRF, \(\Phi\) denotes the inverse tone mapping curve, \(C\) represents ground truth pixel values, and \(\hat{C}\) denotes predicted pixel values.  
The SSIM loss evaluates image distortion through three components: luminance (estimated via means \(\mu_{x},\mu_{y}\)), contrast (estimated via standard deviations \(\sigma_{x},\sigma_{y}\)), and structure (measured via covariance \(\sigma_{xy}\)). The SSIM formula remains unchanged for thermal images, as temperature variations can still be assessed through luminance patterns at the image level.  
For the final SR outputs, we designed an enhancement loss \(L_{\text{en}}\), combining perceptual and L1 losses:  
\[L_{\text{en}}=\lambda_{\text{per}}L_{\text{per}}+\lambda_{\text{L1}}L_{\text{ L1}}\]  
where \(L_{\text{per}}\) is computed using a pretrained VGG19 network \(\phi\) to measure feature-level similarity between outputs \(O\) and ground truth \(GT\), while \(\lambda_{\text{per}}\) and \(\lambda_{\text{L1}}\) are weighting hyperparameters.  
To address geometric ambiguity in low-light 3D reconstruction, we propose a Thermal Gradient Alignment Loss. This loss leverages thermal imaging gradients to guide surface geometry optimization. Specifically, thermal edges (corresponding to temperature transitions) provide robust geometric priors under low-light conditions. The loss enforces consistency between the thermal gradient \(\
abla T\) and the geometric surface normal \(n\) derived from the GS model:  
\[L_{\text{thermal-align}}=\sum_{i}\lVert\
abla T_{i}\cdot n_{i}\rVert_{1}\]  
where \(\
abla T_{i}\) is the thermal gradient at position \(i\), and \(n_{i}\) is the normalized surface normal. By aligning thermal and geometric boundaries, this loss mitigates depth estimation errors caused by low-contrast RGB inputs, significantly improving reconstruction fidelity for complex scenes.
# Responses of Water Temperature and Level to Large Earthquakes in Tibet
## 3 Results
### Co-Seismic Response of Underground Fluid Observation Wells at Lhasa Station
#### 3.2.1 Co-Seismic Response Characteristics of Water Level and Water Temperature  
The water level co-seismic response refers to the \"resonance\" phenomenon of the water level when a seismic wave propagates outward after a strong earthquake. When the seismic wave passes through the measurement site, it causes instantaneous deformation of the aquifer, thus stimulating a change in pore pressure and preventing the pore water from having enough time to be exchanged with the outside world. Therefore, the deformation of an aquifer caused by a seismic wave can be considered to represent an approximately undrained aquifer. In open well-aquifer systems, the exchange of water between the aquifer and well is strongly related to the permeability coefficient of the aquifer. When a seismic wave passes through, the larger this permeability coefficient is, the greater the amplitude of water level oscillation is, and the more obvious the resulting seismic reflection effect is [18, 25, 33]. In addition, the well diameter, the water column height and the frequency characteristics of the water level meter are all related to the recorded water seismic waves. The effect of the groundwater level during and after an earthquake can be divided into two types: a rapid step change, mainly characterized by the well water level effect of a nearby earthquake, while the other type, an oscillation change of the water level, is the result from the effect of a water shock wave; this effect is generally common in wells with epicentral distances greater than 2000 km [18, 25, 33].  
Seismic waves can cause certain deformations of the medium in a well-aquifer system and cause corresponding changes in the water transport channel flowing through the aquifer. These changes affect the flow speed and path of groundwater; in addition, they affect the heat exchange between groundwater and the surrounding rock mass, thus affecting the temperature change in the water body in the observation well [34, 35].
# Numerical Simulation Analysis of Wellbore Stability in Weak Interlayer of Igneous Rock
## 5 Analysis of Factors Influencing Wellbore Stability
### Effect of Horizontal In Situ Stress Anisotropy on Wellbore Stability  
The horizontal in situ stress ratio serves as an indicator of the level of anisotropy in the horizontal in situ stress. The maximum and minimum principal stresses on the wellbore were extracted for different horizontal in situ stress ratios (1, 1.1, 1.3, and 1.4), and the variation of the stress difference with the horizontal in situ stress ratio is presented in Figure 13.  
In general, the difference between the principal stresses follows a pattern of initial decreases followed by an increase across different horizontal in situ stress ratios, aligning with the concentration of stress in the direction of maximum and minimum in situ stress. As the horizontal in situ stress ratio increases, the risk of collapse decreases between 0\({}^{\circ}\) and 23\({}^{\circ}\), but gradually increases between 23\({}^{\circ}\) and 90\({}^{\circ}\). Notably, between 60\({}^{\circ}\) and 90\({}^{\circ}\), the risk of  
Figure 12: Variation of plastic strain with extension length under different permeability of the weak interlayer.  
Figure 13: Variation of the maximum and minimum principal stress difference with the well circumference angle.  
collapse experiences a significant increase, particularly when the horizontal in situ stress ratio reaches 1.4. Higher values of the horizontal in situ stress ratio amplify the risk of collapse in the majority of the well perimeter, with the most substantial impact occurring in areas near the direction of maximum in situ stress.  
The plastic strains within the longest extension region were extracted for models with different horizontal in situ stress ratios, and the results are presented in Figure 14.  
The impact of the horizontal in situ stress ratio on the plastic deformation of the wellbore was investigated by examining the peak deformation extension distance. Plastic deformation primarily occurs within the weak interlayer. As the horizontal in situ stress ratio increases, the peak value of plastic strain initially rises and then declines, eventually converging to zero. A higher peak value of plastic strain indicates a greater influence on the normal rock mass on both sides, resulting in an increased risk of instability. In summary, a higher horizontal in situ stress ratio corresponds to a higher degree of plastic deformation, which adversely affects the stability of the wellbore.
## 2 Global Open Earth Observations Satellite Missions for Monitoring Land Surface Phenology
### Moderate Resolution Imaging Spectroradiometer (MODIS)
(2018), who detected mostly short-range degradation of vegetation, less than 1 km buffer from the reservoir. The area range disturbance as a consequence of coal mining activities was studied by [PERSON] et al. (2018) by applying a newly adopted spectral index based on the LSP.
## 4 SSA-ELMAN Neural Network Model  
### Elman Neural Network  
The Elman neural network is a partial feedback neural network with a feed forward connection. It is composed of an input layer, a hidden layer, a context layer, and an output layer. The Elman neural network adds a context layer to the original structure of the BP neural network, and it also propagates the data output from the hidden layer back to the hidden layer, which enhances the dynamic memory ability of the network and the ability to process nonlinear problems [15; 16; 17; 18]. The network's structure is shown in Figure 3.  
Figure 2: The Pearson correlation coefficients.  
The Elman neural network mathematical model is as follows:  
\[\left\{\begin{array}{l}X(k)=f\{W_{1}X_{c}(k)+W_{2}[U(k-1)+b_{1}]\}\\ X_{c}(k)=X(k-1)\\ Y(k)=g[W_{3}X(k)+b_{2}]\end{array}\right. \tag{3}\]  
where \(f\) and \(g\) are the hidden layer and output neurons' transfer functions, respectively, and \(W_{1}\), \(W_{2}\), and \(W_{3}\) are the hidden layer's connection weights to the input layer, the context layer's connection weights to the hidden layer, and the hidden layer's connection weights to the output layer, respectively. \(U(k-1)\) and \(Y(k)\) are the input vectors of the input layer and the output vector of the output layer, respectively; \(b_{1}\) and \(b_{2}\) are the weights of the input layer and hidden layer respectively; and \(X(k)\) and \(X_{c}(k)\) are the outputs of the hidden layer and the context layer, respectively.
# Dynamic Linked Data: A SPARQL Event Processing Architecture
## 5 Application Design Pattern
### Software Framework and Application Design Pattern  
Recalling Figure 1, a SEPA application is composed by agents (i.e., producers, consumers and aggregators), the interaction of which is mediated by vocabularies and rules defined through shared ontologies. While producers and consumers should be kept as simple as possible to be reused and shared by different applications, aggregators implement the application logic. Aggregators link the functionalities provided by producers and consumers in order to achieve the desired behaviors. To this end, they subscribe to events created by producers and create new events that may trigger actions of consumers or other aggregators. In general, the application logic implemented by an aggregator can be combinatorial (i.e., no context memory is needed) or sequential (i.e., the context evolution is stored into the aggregator internal context memory). Within SEPA, an aggregator plays the role played by an event processing agent within the event processing network presented in [69].  
The advantages of the proposed design guidelines are two-fold. First, since producers and consumers are implemented independently from a specific use case, they can be shared between different applications (i.e., by modifying existing or implementing new aggregators, the overall system functionality can be modified or extended indefinitely). This, of course, leads to cost savings also when new systems are deployed. Second, since the processing performed by consumers and producers is very simple, they may be implemented in resource-restricted devices.  
SEPA provides the software framework shown in Figure 7. Interoperability at the information level is granted by RDF/RDFS/OWL ontologies: as a best practice, application developers are recommended to adopt standard and recognized ontologies. The framework offers an API in several programming languages and at different levels of abstraction, to allow developers to choose the desired one (also with reference to the class of the device as defined in RFC7228 ([[https://tools.ietf.org/rfc/rfc7228.txt](https://tools.ietf.org/rfc/rfc7228.txt)]([https://tools.ietf.org/rfc/rfc7228.txt](https://tools.ietf.org/rfc/rfc7228.txt))): low-level APIs implement the SPARQL 1.1 Secure Event Protocol (see Section 3), while higher level APIs provide an application design pattern that can be followed to enhance reuse, modularization and interoperability (see APCI, Aggregator Producer Consumer API, in Figure 7). At the application level, SEPA promotes the reuse of software modules through the APCI: along with the adopted ontologies, an application can be characterized by the set of updates and subscribes issued by its agents. Within the APCI, each agent is linked to a specific primitive: a producer to an update, a consumer to a subscribe and an aggregator to a pair subscribe-update.
## 1 Introduction
[30] considered a compressible flow of Jeffrey fluid through a porous medium and concluded that the average velocity of a viscoelastic fluid flow increases in the presence of a magnetic field through the porous medium. The flow of a second-grade fluid under the low Reynolds number approximation has been recently addressed by [PERSON] et al. [31], who concluded that viscoelastic effects are responsible for a drastic change in the flow field. [PERSON] et al. [32] analysed the magnetically actuated mucociliary pumping in a bronchial tube due to ciliary motion. They noticed that the ciliary beating frequency can be controlled by the application of a magnetic field. However, the MHD flow of a second-grade fluid induced by ciliary motion in a porous medium with inertial forces has not been studied yet. This is of particular interest for the diagnosis of respiratory diseases by magnetic resonance imaging [32]. Therefore, the present study addresses the role of a magnetic field and inertial forces on second-grade fluid flow through a porous medium surrounded by ciliated walls. In the present work, the flow of a second-grade fluid due to metachoronal motion of cilia is investigated, and the influence of moderate Reynolds number, magnetic field and porous medium on the velocity, pressure and stream functions is studied in detail. The momentum equations include inertial effects, which were always ignored in former studies [31; 33]. The presence of a magnetic field and inertial forces reduces the momentum equations into a system of fourth-order non-linear partial differential equations with moving boundary conditions. Obtaining the solution of the highly nonlinear partial differential equations with nonhomogeneous boundary conditions is very challenging for researchers. Therefore, one attempts to solve this system by the analytical technique HPM [33] together with the software Mathematica. The resulting expressions for the pressure gradient, stream function and velocity profile are portrayed, and the influences of the Hartmann number (magnetic field), Reynolds number (inertial force), porosity parameter (porous medium) and viscoelastic parameter (second-grade fluid) are discussed in detail. Antiplectic and symplectic metachronal wave patterns are observed, and the results show that the antiplectic wave pattern produces high speed compared with the symplectic wave pattern. To the best of the authors' knowledge, such a comprehensive analysis has never been proposed so far in the literature.
## 5 Conclusions and Future Work  
In this paper, we presented a dual stack single radio architecture that can be used in mobile sinks, specifically an Unmanned Aerial Vehicle (UAV), to address the problem of data collection in static, ground, wireless sensor nodes. We discussed how a low cost, dual stack protocol can be used by a short range UAV (or any other mobile device) to collect data from a WSN. Moreover, the viability of using a single radio transmitter for simultaneously collecting data from the WSN and communicating with the Sink node or Base Station was shown. Our architecture design allows the UAV to collect data from heterogeneous ground sensor devices and send telemetry or collected data back to the Sink using the same communication hardware and without compromising the data rate. This prototype design has clearly established the feasibility of using a single communication hardware for mobile nodes for data gathering in sensor networks.  
This paper also detailed the proposed communication protocol, which is capable of dealing with two different requirements: a best effort communication between the UAV and the WSN and a more reliable communication to be used between the UAV and Sink. This paper presented the successful use of the proposed architecture within an environmental monitoring application scenario. It can also be observed that the proposed dual stack architecture is not only valid for the UAV, it is also generic enough to be used for ground mobile nodes as well.  
In this work, we did not focus on how sensor nodes communicate with the CH, although in a real application this needs to be configured in a way that sensor-to-CH communication does not collide with CH-to-UAV communication. Different sleep/wake-up pattern can be used for such purposes. It is also possible to exploit cooperation (information sharing) between UAV and ground nodes to avoid messages collisions. Similarly, it is also possible for static nodes, particularly the CH, to learn and predict the mobility pattern of UAV to further enhance UAV detection. This will not only decrease message collisions, but also enhance network lifetime and allow more accurate sleep/wake-up patterns for sensor nodes. This along with effect of single radio transmitter on the lifetime of network needs to be further addressed.  
Future work also includes removing the UAV remote pilot by designing and implementing a dynamic speed control for the UAV, in which speed is adaptively and autonomously adjusted according to the application needs. Similarly, a dynamic Flush Back Period is also under consideration so as to use available bandwidth wisely. Finally, additional practical experimentation alternating best-effort and reliable transmissions should be performed.