esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Spatiotemporal Evolution Characteristics
### Diverse Changes in Spatial Influencing Factors
Band set statistics results demonstrated a notable overall upward trend in correlation, with primary indicator correlations being ranked as living \(>\) commerce \(>\) work \(>\) transportation in 2012 and transitioning to living \(>\) work \(>\) commerce \(>\) transportation by 2023. This shift underscores the dynamic temporal evolution and change rates of secondary indicators, which generally indicate an increase in correlations across the board, except for the commerce sector. This suggests that Paomo restaurants initially developed in a somewhat chaotic and spontaneous manner. Over time, however, the uniformity of commercial behaviors has emerged as an endogenous driving force for social development, establishing a robust foundation for the optimization of urban spatial structures. Traffic indicators revealed a strong and intensifying connection with main roads, yet there was a noticeable decline in the relationship to subway stations. This trend points to potential areas for improvement in making Paomo restaurants more accessible and appealing to younger demographics who frequently utilize public transit. Work-related indicators displayed a consistent upward trend across all secondary indicators, affirming the role of Paomo restaurants as a crucial dining option within primary work environments as urbanization advances. The point increase in proximity of Paomo restaurants to main roads by 2023 reflects both the strategic relocation of some restaurants to more accessible locations and the expansion of the road infrastructure around existing restaurants. The decline in proximity to subway stations might reflect a strategic avoidance of saturated markets, which could indeed be seen as a positive adaptation to the competitive landscape. Living factors consistently rose to prominence, underscoring the sustained popularity of Paomo restaurants and emphasizing the significant role that specialty catering plays in the layout of urban spatial structures. However, commercial factors presented a more complex scenario; while relationships with hotels and service facilities consistently rose, indicating high appreciation by both tourists and locals, the correlation with tourism facilities significantly decreased. This decline suggests a notable mismatch between this specialty cuisine and tourist destinations, which hinders tourists from directly engaging with Xi'an's authentic culinary culture. This discrepancy highlights a gap when compared to domestic first-tier cities and calls for enhanced focus from relevant authorities. Looking forward, urban planners and policymakers should carefully consider the spatial needs and evolving trends of specialty catering industries like Paomo restaurants. By guiding their sustainable development, it is possible to optimize urban commercial space structures and promote ongoing social and economic advancement. This strategic approach will not only cater to the immediate needs of local and visiting patrons but also contribute to the broader goal of urban renewal and cultural integration.
## 3 Results of Data Training and Discussion
### Logistic Regression Modeling
#### 3.1.3 Precision  
The precision in machine learning measures the proportion of correct positive predictions, highlighting the ability of the model to avoid false positives. It is calculated using Equation (5):  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{5}\]  
In our case:  
\[\text{Precision}=\frac{354}{354+37}=\frac{354}{391}\approx 0.9054 \tag{6}\]
## 2 Comprehensive Attribute Evaluation of Nodes
### Kinematic Posture of Nodes  
In Section 2.2, the forwarding ability of the node as the receiving party of the message is evaluated in the process of transmitting the data, whether it has enough device capacity for forwarding the message or not. In Section 2.3, the degree of willingness of the node to forward when it is the sending party of the message will be evaluated, and the node is more willing to use its resources to forward the message to the node that is related to it [13]. The special feature of the opportunistic network is the random movement of nodes, due to which the message cannot be directly transmitted to the destination node. The relay node is utilized to store and forward the message, and the message can be transmitted to the destination node hop by hop through the relay node. During the node's movement, it is more likely to forward the message to the nodes that are more convergent with it in terms of its movement posture or other nodes that are closer to the destination node in terms of their movement posture, and the degree of willingness to forward the message to these nodes is also stronger. In this paper, we use the dot product to calculate the direction similarity (DS) on the node movement posture, as shown in Equation (3).  
\[\text{DS}=||d_{i}||||d_{j}||cos(\theta), \tag{3}\]  
where \(d_{i}\) and \(d_{j}\) denote the direction vector of the node carrying the message and the direction vector of the node in the candidate relay set, respectively, and \(\theta\) denotes the angle between the motion directions of the two nodes, \(\theta\)e(0,\(\pi\)).  
When \(\theta\) is smaller, DS is larger, and nodes that are judged to be more similar in terms of movement posture have a higher degree of willingness for nodes to forward messages to them.
## 2 Localization of Microphones Based on Sound Source Array
### Localization of Microphones  
The implementation process of the microphone localization method based on sound source array was as follows. Given the position of the robot in the global coordinates, the position of the three sound sources and the reference microphone in the global coordinates could then be calculated. By obtaining TDOA, the distances between the microphone to be positioned (MTP) and the three sound sources were obtained through further calculation. Define \(\Delta T_{ij}(i=1,2\ldots N;\ j=1,2,3)\) as the time difference of receiving the \(j\)-th sound signal between the reference microphone and the \(i\)-th MTP, where \(N\) is the number of the MTP. Define the speed of sound propagation in the air as C, which is multiplied by the TDOA to obtain the difference value \(\Delta L_{ij}\) about the distance from the \(j\)-th sound source to the reference microphone and to the \(i\)-th MTP.  
\[\Delta L_{ij}=\Delta T_{ij}\cdot C \tag{6}\]  
\(L_{j}(j=1,2,3)\) is defined as the distance from the reference microphone to the \(j\)-th sound source. Based on Equation (6) and \(L_{j}(j=1,2,3)\), the distance between the \(i\)-th MTP and the \(j\)-th sound source can be calculated as follows  
\[d_{ij}=L_{j}+\Delta L_{ij}=L_{j}+\Delta T_{ij}\cdot C \tag{7}\]  
Define \((x,y)\) as the position of the \(i\)-th MTP, and \(\left(x_{j},y_{j}\right)(j=1,2,3)\) as the position of the three sound sources in the global coordinate system. According to the geometric relationship, the position of the \(i\)-th MTP in the environment is obtained by solving the intersections of three circles with three sound source positions as centers and the distance from the microphone to the corresponding sound source as radius.  
\[\left\{\begin{array}{l}\left(x-x_{1}\right)^{2}+\left(y-y_{1}\right)^{2}={d_ {i}}_{1}{}^{2}\\ \left(x-x_{2}\right)^{2}+\left(y-y_{2}\right)^{2}={d_{i}}_{2}{}^{2}\\ \left(x-x_{3}\right)^{2}+\left(y-y_{3}\right)^{2}={d_{i}}_{3}{}^{2}\end{array}\right. \tag{8}\]  
Here, \(d_{i1}\), \(d_{i2}\), and \(d_{i3}\) represent the distance from the \(i\)-th MTP to the three sound sources, respectively. A block diagram overview of the microphone localization processing chain is provided in Figure 4.  
Figure 3: Time delay of arrival (TDOA) estimating based on the generalized cross-correlation (GCC)-phase transformation (PHAT) methods. The sound signal waveform reached the peak when t was 0.01 s.
## 5 Delays within a Domain
### Example of Eliminating the Requirement for a Standard ACL  
An example of how a standard ACL could be optimized to eliminate the ACL in 2 out of 3 routers is seen in Figure 10.  
The basis for optimization is that if some addresses are denied access by subsequent routers then these can be moved to routers earlier in the route. Additionally the range of rules can be extended to be incorporated into earlier rules by modifying the mask. The effect would be the reduction of the overall delay across the domain. This has to be done with great consideration to ensure that the security policy is not violated. In this simple example with 3 routers the effect would be a reduction in the delay over the domain by around 65%.
## 1 Introduction
Furthermore, maintaining a safe distance between flying vehicles is essential to prevent potential collisions. This facilitates the recognition and exchange of location information for each UAV, which serves as the foundation for developing applications aimed at enhancing UAV safety through the utilization of shared location information with V2V communications [20; 21]. By implementing these mechanisms, the risks associated with UAS can be mitigated, ensuring safer and more efficient operations in various domains. In this paper, we develop a system for location awareness and sharing of UAVs named \"Drone Mapper\" [22]. To enable these functions, the system utilizes a V2V communication with low-power wide-area (LPWA) functionality operating in the 920 MHz band for global navigation satellite system (GNSS)-based location information sharing. The LPWA is an emerging wireless network technology designed for communication ability among low-power, especially battery-powered, devices over large geographical areas [23]. Since the AFCCS sensors and end devices in the UAS are mainly battery-based and power-limited communication devices, LPWA works as the solution to optimize the energy consumption of the end devices in the network [24; 25]. In addition, because the wireless technology adopted in the proposed system does not need cellular infrastructures, it can work under some tough conditions without good infrastructure such as countryside circumstances or places suffering severe disasters. Furthermore, building upon Drone Mapper, we also develop an autonomous flight coordination control system (AFCCS) to enhance UAV safety operations. The AFCCS provides two essential functionalities for multiple UAV applications: UAV collision avoid ance and UAV formation flying. To assess the effectiveness of our proposed method, we conduct field experiments specifically targeting these functionalities, and the experiments are designed to thoroughly evaluate the performance of our developed system and its associated methods. The rest of this paper is organized as follows. The system description for this study is provided in Section 2. The proposed methods for flight coordination control are introduced in Section 3. Then, we conduct field experiments to evaluate the proposed methods and system in Section 4. Finally, we provide some concluding remarks in Section 5.
# Study on Surface Damage Induced by High Heavy Layer Movement and Mining-Induced Earthquakes
## 2 Methodology
### Geological Overview and Monitoring Data of Mining-Induced Earthquakes  
#### 2.1.1 Geological Overview  
The 7202 working face in the Zaozhuang mining area is located in the No.7 mine district, which represents the seventh artificially delineated mining zone. Its northern boundary borders the 72 track dip roadway, a sloped passage designed for material transportation and ventilation. To the south, it is adjacent to the open-off cut, which is the initial coal seam boundary excavation, adjacent to the boundary of the coal seam. The eastern limit coincides with the coal field boundary, serving as the demarcation of mining rights, while the western side borders solid coal. The working face has an underground elevation in the range of approximately \(-751-\)\(-\)793 m. No mining activities are conducted in the surrounding areas. The working face takes a single longwall retreat mining method with full caving for goat management. The mining process utilizes fully mechanized caving processes. The schematic diagram of the working face is shown in Figure 2.  
The surface of the mining area is located approximately 1000 m east of the local village, with farmland covering the area. The ground elevation is +55.79 m. The cover depth of the 7202 working face is in the range of approximately 806.79-848.79 m.  
The 7202 working face is situated in the No.3 lower coal seam, with a maximum thickness of 7.90 m. The coal seam has a dip angle ranging from 5-\(11^{\circ}\), with an average of \(8^{\circ}\). The main roof of the working face consists of fine sandstone, with an average thickness of 11.68 m. The immediate floor is composed of mudstone, with an average thickness of 10.35 m. Above the coal seam lies a Jurassic heavy conglomerate layer, the length of which is approximately 80 m, characterized by its hard texture and high strength. This heavy conglomerate layer is located about 72 m above the coal seam. The lithology column of the working face is shown in Figure 3.  
Figure 1: Research route flowchart.  
When the Jurassic conglomerate heavy layer undergoes fracturing movements, inducing high-energy mining-induced earthquakes, the thin surface layers above the mining area of the 7202 working face results in the slow attenuation of seismic waves, often leading to noticeable ground vibrations at the surface.
## 4 Proposed Routing Protocol  
In this section, we proposed a model-free reinforcement-learning-based geographic routing protocol. The process of routing decisions is simplified as the Markov Decision Process. Our protocol mainly includes the following parts: sensing phase, feature extraction phase, learning phase, and routing decision phase. The routing protocol architecture is shown in Figure 2. The sensing phase is to acquire features. The deep-reinforcement-learning algorithm is used for training nodes in how to make routing decisions. Next, the DSEGR protocol is described in detail.
## Abbreviations  
\begin{tabular}{l l} \(r,\theta,Z\) & cylindrical coordinate system of the polymer layer \\ \(X,Y,Z\) & cartesian coordinate system of the bearing \\ \(C_{1},C_{2},C_{3}\) & coefficients of the curve fitting function \\ \(C_{r}\) & bearing radial clearance [m] \\ \(c_{XX},c_{YY}\) & direct damping coefficients of the bearing [Ns/m] \\ \(c_{XY},c_{YX}\) & cross-coupled damping coefficients of the bearing [Ns/m] \\ \(c_{\#}\) & effective damping of the bearing [Ns/m] \\ \(d_{r},d\theta,dZ\) & radial, angular, and axial increments [m] \\ \(E_{p}\) & Young's modulus of the polymer layer [N/m\({}^{2}\)] \\ \(e_{X},e_{Y}\) & journal position components [m] \\ \(F_{X},F_{Y}\) & sum of reaction forces of equivalent springs [N] \\ \(F_{X,R},F_{Y,R}\) & sum of the reaction forces of the equivalent springs [N] \\ \(F_{X,i},F_{Y,i}\) & reaction force of the equivalent spring i [N] \\ \(f_{X},f_{Y}\) & dynamic bearing reaction forces [N] \\ \(h\) & gas-film thickness [m] \\ \(i\) & equivalent spring number \\ \(j\) & imaginary unit, \(\sqrt{-1}\) \\ \(K_{GFPB}\) & equivalent structural stiffness of the GFPB [N/m] \\ \(k_{XX},k_{YY}\) & direct stiffness coefficients of the bearing [N/m] \\ \(k_{XY},k_{YX}\) & cross-coupled stiffness coefficients of the bearing [N/m] \\ \(k_{\#}\) & effective stiffness of the bearing [N/m] \\ \(k_{b}\) & equivalent stiffness per unit area of the bump foil [N/m\({}^{3}\)] \\ \(k_{p}\) & equivalent stiffness per unit area of the polymer layer [N/m\({}^{3}\)] \\ \(k_{GFPB}\) & equivalent structural stiffness per unit area of the GFPB [N/m\({}^{3}\)] \\ \(L\) & length of the polymer layer [m] \\ \(N_{b}\) & number of single bump foils \\ \(p\) & gas-film pressure field [N/m\({}^{2}\)] \\ \(p_{a}\) & ambient pressure [N/m\({}^{2}\)] \\ \(p_{in}\) & internal fluid-film pressure acting on the polymer layer [N/m\({}^{2}\)] \\ \(p_{avg}\) & average gas-film pressure over the bearing length [N/m\({}^{2}\)] \\ \(r_{inv},r_{out}\) & inner and outer radius of the polymer layer \\ \(r\) & radial position of an FE [m] \\ \(t\) & time [sec] \\ \(t_{p}\) & polymer layer thickness [m] \\ \(u\) & radial deformation of an FE [m] \\ \(W_{stmin}\) & strain energy of the polymer layer [Nm] \\ \(W_{potential}\) & potential energy of an equivalent spring [Nm] \\ \(\gamma\) & structural loss factor \\ \(\delta_{i}\) & deformation of the ith equivalent spring [m] \\ \(\epsilon\) & strain of an FE \\ \(\zeta_{0}\) & structural deflection of bearing [m] \\ \(\theta_{i}\) & angular location of ith equivalent springs [rad] \\ \(\mu\) & air viscosity [pa s] \\ \(v_{p}\) & Poisson's ratio of the polymer layer \\ \(\sigma\) & stress of a FE [N/m\({}^{2}\)] \\ \(\tau\) & the gap between journal and bearing surface [m] \\ \(\Omega\) & rotational speed [rad/s] \\ \end{tabular}
## 2 Design of a Structural Health Monitoring System for a Jacket Offshore Platform
### Assessment Subsystem  
The assessment subsystem examines the overall performance of the offshore platform using real-time data, which includes safety monitoring and early warning evaluation using static measurements, overall performance evaluation based on frequency variation and damage identification based on strain modal, which are described below.
## 2 Review of Cryogenic-Cooling Benefits
### Spectroscopic, Lasing, Linear and Nonlinear Optical Parameters
#### 2.7.3 Optically-Induced Laser Damage
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Crystal** & **Wavelength (nm)** & \(n_{0}\) & \(\beta_{1}\) & \(\beta_{2}\) & \(\beta_{3}\) & \(\beta_{4}\) & **Reference** \\  & & & **(fs/mm)** & **(fs\({}^{2}\)/mm)** & **(fs\({}^{3}\)/mm)** & **(fs\({}^{4}\)/mm)** & **Reference** \\ \hline Crystal SiO\({}_{2}\) (o) & 1030 & 1.5342 & 5162.8 & 25.8 & 46.2 & \(-\)53.6 & [91] \\ Crystal SiO\({}_{2}\) (e) & 800 & 1.5348 & 5200.6 & 38.1 & 50.4 & \(-\)43.9 & [91] \\ Crystal SiO\({}_{2}\) (o) & 800 & 1.5381 & 5181.9 & 45.8 & 32.1 & \(-\)12.0 & [91] \\ Fused Silica (SiO2) & 800 & 1.4533 & 4890.5 & 36.1 & 27.3 & \(-\)11.0 & [92] \\ Fused Silica (SiO\({}_{2}\)) & 1030 & 1.4500 & 4875.7 & 19.0 & 40.4 & \(-\)49.8 & [92] \\ \hline \end{tabular}
\end{table} 
Table 5: _Cont._It is a shortcoming of the present state-of-the-art that high average power effects have received far less attention than high peak power effects. Now that we are witnessing the advent of laser systems that have concurrent high and peak average powers, much attention will be focused in the near future on determining the LIDT's of laser crystals and optics under very extreme conditions. LIDT's at low temperature are scarce and in most cases have not been measured. Recently a publication reported a reduced damage threshold at cryogenic temperatures relative to the room temperature value [96]. This in contrast to a number of publications that have reported increased damage thresholds at cryogenic temperatures [97; 98; 99]. Increased laser damage threshold measurement efforts at cryogenic temperatures are needed to further our understanding of the design of cryogenic laser devices and to increase our knowledge of the scaling of the damage threshold with temperature.
# Matchability and Uncertainty-Aware Iterative Disparity Refinement for Stereo Matching
## 5 Conclusions  
The MUIR proposed in this paper utilizes the novel MUD module to improve the performance of RAFT-based models. The MUD module learns to decode the feature matchability mask and the disparity uncertainty concurrently from the disparity field's hidden state. This innovative approach reduces the model's susceptibility to mismatched pixels and alleviates the unnecessary reduction in disparity accuracy that occurs during joint training. Furthermore, building on the MUD module, we introduce the UFI and MGA modules to improve refinement efficiency. Comprehensive experimental evaluations on synthetic and real-world datasets show that our method reduces disparity error and occluded outlier proportion by up to 17.9% and 16.9%, respectively, compared to the baseline, demonstrating the improvement in disparity accuracy and robustness of MUIR. Future research may focus on incorporating global image features and scalable disparity encoding techniques to further optimize scale generalization.  
Conceptualization, [PERSON]; methodology, [PERSON]; formal analysis, [PERSON]; investigation, [PERSON] and Y.T.; resources, [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON], [PERSON] and Y.T.; supervision, Y.T. and H.G.; funding acquisition, H.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science & Technology Commission of Shanghai Municipality (STCSM), grant number 21010502900.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
We thank the anonymous reviewers for their constructive comments.  
Figure 8: Visual comparison between various models. The datasets are arranged from top to bottom in the following order: KITTI2012, KITTI2015, New Tsukuba, Middlebury and ETH3d. (**a**) The input left image; (**b**) the disparity ground truth. (**c**–**h**) are estimated disparity maps of: (**c**) RAFT-Stereo [12]; (**d**) our proposed MUIR-RAFT; (**e**) IGEV-Stereo [32]; (**f**) our proposed MUIR-IGEV; (**g**) EAI-Stereo [14]; (**h**) our proposed MUIR-EAI. The details are marked in the red box and best viewed when zoomed in.
## 2 IA for Two-Unit Series System with Three States
### Mathematical Model of IA  
For establishing the renewal model of IA for the two-unit series system with three states, the system must meet the following assumptions.  
**Assumption 1**.: _Each unit \(i,\ (i=1,2)\) has the working time \(X_{i}\), the delay-repair time \(W_{i}\) and the repair time \(Y_{i}\), which are subject to the following exponential functions:_  
\[X_{i} \sim F_{i}(t)=1-e^{-\lambda_{i}t}, \tag{1}\] \[W_{i} \sim W_{i}(t)=1-e^{-\mu_{i}t},\] (2) \[Y_{i} \sim G_{i}(t)=1-e^{-\
u_{i}t}, \tag{3}\]  
_where \(\lambda_{i}\) is the fault rate, \(\mu_{i}\) delay-repair rate and \(\
u_{i}\) repair rate._  
Note that the electronic units are always interfered by internal or external factors and easily lead to various types of failure, which always is the main reason for the failure of mechatronic system. The lifetime of the units generally follows exponential distribution [18]. Moreover, the maintenance time of system is generally under exponential, Weibull, logarithmic normal distribution [19]. For simplification, assume that repair time and delay-repair time are under exponential distribution.  
**Assumption 2**.: _We suppose that all units are new at the beginning. Besides, the unit will be new after maintenance._  
According to the assumptions above, the renewal process of the two-unit series system with three states is established by the failure time \(X_{1},\ X_{2}\), the delay-repair time \(W_{1},\ W_{2}\) and the repair time \(Y_{1},\ Y_{2}\) and is shown in Figure 1. \(\{\xi_{k}+\varphi_{k}+\chi_{k},\ k=1,2,...,n\}\) is the renewal process. \(\xi_{k}\) is the failure time of the series system, which means \(\xi_{k}=minX_{i}\). The delay-repair time \(\varphi_{k}\) and the repair time \(\chi_{k}\) rely on \(\xi_{k}\), which means that \(\varphi_{k}=W_{i},\ \chi_{k}=Y_{i}\) when \(\xi_{k}=X_{i},\ i=1,2\).  
Meanwhile we have  
\[P^{\prime}(W_{1}+Y_{1}\leq t) =P(W_{1}+Y_{1}=t)=\int_{0}^{t}u_{1}e^{-u_{1}(t-u)}v_{1}e^{-v_{1}u}du= \frac{\mu_{1}v_{1}}{v_{1}-\mu_{1}}(e^{-\mu_{1}t}-e^{-v_{1}t}), \tag{10}\] \[P^{\prime}(W_{2}+Y_{2}\leq t) =P(W_{2}+Y_{2}=t)=\int_{0}^{t}u_{2}e^{-u_{2}(t-u)}v_{2}e^{-v_{2}u}du= \frac{\mu_{2}v_{2}}{v_{2}-\mu_{2}}(e^{-\mu_{2}t}-e^{-v_{2}t}). \tag{11}\]