esa-sceva/satcom-chunk-collection huggingface dataset:
# A Theoretical Study on the Resilience Evaluation Method of Operational Road Tunnel Systems
## 6 Conclusions  
This paper provides a summary of the existing research on the resilience of road tunnels. We analyze the basic elements of resilience. It can be divided into four keywords: system function, external disturbances, resistant ability, and recovery ability. The relationship between the four basic elements is described in this paper, and we propose the basic concept of road tunnel system resilience. Based on this concept, we propose the tunnel function system composition and the classification of external disturbances faced by road tunnels. The road tunnel resilience triangle is proposed to describe the relationship between road tunnel systems and external disturbances emphatically. The theoretical analysis model of road tunnel resilience is built to describe the change in road tunnel system function over time due to external disturbances. The ideal resilience curve of road tunnel systems can be used to exhibit a high resilience level, which includes slow speed of functional damage, low functional damage, zero response, and fast speed of functional recovery. Five basic properties of road tunnel resilience are proposed to describe the resilience level. Furthermore, we propose a resilience evaluation method for road tunnels based on the functional network. This method can show the functional transfer direction through the functional nodes. We can analyze the important functional nodes and paths to find vulnerable links that road tunnel systems are subjected to by external disturbances. Based on the vulnerable links, it can provide a theoretical foundation for capital allocation plans and risk mitigation strategies.  
Conceptualization, C.X. and H.H.; methodology, C.X. and H.H.; software, H.H.; validation, C.X., H.H. and H.W.; formal analysis, H.H.; investigation, H.W.; resources, H.W.; data curation, H.H. and H.W.; writing--original draft preparation, C.X. and H.H.; writing--review and editing, H.H.; visualization, C.X.; supervision, C.X.; project administration, C.X.; funding acquisition, C.X. All authors have read and agreed to the published version of the manuscript.  
Figure 8: Resilience evaluation diagram of road tunnel considering different operating environments.  
**Funding:** This research was funded by the pilot tasks for building a strong transportation country of Research Institute of Highway Ministry of Transport: QG2021-1-3-3).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The relevant data are all included in the paper.  
**Conflicts of Interest:** Author [PERSON] was employed by the company Research Institute of Highway Ministry of Transport. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
###### Abstract  
Agricultural drought is triggered by a depletion of moisture content in the soil, which hinders photosynthesis and thus increases carbon dioxide (CO\({}_{2}\)) concentrations in the atmosphere. The aim of this study is to analyze the relationship between soil moisture (SM) and vegetation activity toward quantifying CO\({}_{2}\) concentration in the atmosphere. To this end, the MODerate resolution imaging spectroradiometer (MODIS), an optical multispectral sensor, was used to evaluate two regions in South Korea for validation. Vegetation activity was analyzed through MOD13A1 vegetation indices products, and MODIS gross primary productivity (GPP) product was used to calculate the CO\({}_{2}\) flux based on its relationship with respiration. In the case of SM, it was calculated through the method of applying apparent thermal inertia (ATI) in combination with land surface temperature and albedo. To validate the SM and CO\({}_{2}\) flux, flux tower data was used which are the observed measurement values for the extreme drought period of 2014 and 2015 in South Korea. These two variables were analyzed for temporal variation on flux tower data as daily time scale, and the relationship with vegetation index (VI) was synthesized and analyzed on a monthly scale. The highest correlation between SM and VI (correlation coefficient (r) = 0.82) was observed at a time lag of one month, and that between VI and CO\({}_{2}\) (r = 0.81) at half month. This regional study suggests a potential capability of MODIS-based SM, VI, and CO\({}_{2}\) flux, which can be applied to an assessment of the global view of the agricultural drought by using available satellite remote sensing products.  
Keywords:agricultural drought; soil moisture; vegetation activity; carbon dioxide flux; remote sensing; optical multispectral sensor +
Footnote †: journal: Remote Sensing, 2020, 12, 1359; doi:10.3390/rs12091359
## 2 Pre-Splitting Blasting Seam Formation Mechanism and Damage Theory
### Judment Standard of Rock Mass Damage Under Blasting Load  
Engineering practice has demonstrated that numerous types of damage (such as joints, micro-cracks, etc.) are randomly distributed in rock masses. Under the action of blasting loads, new cracks are generated in the rock mass near the blasting zone. At the same time, due to the effect of stress waves, existing joints and cracks continue to propagate, nucleate, and coalesce, forming main cracks. According to Huygens' principle, when sound waves reach a structural interface, they will undergo reflection, scattering, diffraction, and other effects. Therefore, these micro-cracks and macroscopic fractures extend the propagation path and reduce the sound wave velocity. Moreover, the reduction in sound velocity is closely related to the number and width of the cracks [27].  
When pre-splitting blasting is used on the excavation contour line, according to the specifications and the requirements for the width of the pre-split cracks and the irregularity of the rock surface after excavation, an important criterion reflecting good blasting quality is the residual rate of the half-holes. For intact rock, this rate should be above 80%, for relatively intact and poorly intact rock, it should be no less than 50%, and for fragmented and highly fragmented rock, it should be no less than 20%. In reality, even if the specifications or production regulations are met, the blasting load still causes varying degrees of damage to the retained rock mass.  
Reference [23] established a relationship between the rock mass damage variable, integrity coefficient, and sound wave velocity reduction rate based on the acoustic wave method. By integrating these, we can obtain the following:  
\[D=1-\frac{E}{E_{0}}=1-\left(\frac{v}{v_{0}}\right)^{2}=1-K=1-\left(1-\eta \right)^{2} \tag{1}\]  
In the equation, \(E_{0}\) and \(E\) represent the elastic moduli of the rock mass before and after blasting, respectively. \(v_{p2}\) is the sound wave velocity in the rock mass before blasting, and \(v_{p1}\) is the sound wave velocity after blasting.  
According to the \"Technical Specification for Construction of Rock Foundation Excavation for Hydraulic Structures\" [28] issued in China, when the change rate \(\eta\leq 10\%\), the rock mass is undamaged or slightly damaged; when \(10\%<\eta\leq 15\%\), the rock mass is slightly damaged; and when \(\eta>15\%\), the rock mass is damaged. Based on Equation (1), the threshold value for the influence of blasting on the surrounding rock can be calculated, i.e., when the change rate of the longitudinal wave velocity of the rock mass is 10%, the corresponding rock mass damage threshold is \(D_{\sigma}\) = 0.19.
# Game-Theory-Based Task Offloading and Resource Scheduling in Cloud-Edge Collaborative Systems
## 3 Problem Description  
### System Model  
The Cloud-Edge collaboration system is shown in Figure 1, which contains one cloud computing server, \(M\) multi-threaded edge servers and a set of edge servers, which is \(M=\{1,2,\ldots m\ldots M\}\), and each edge server is equipped with a base station that can handle the computing tasks of multiple end-users simultaneously. \(N\) end users, the set of users, is denoted as \(N=\{1,2,\ldots n\ldots N\}\), and each user has a non-detachable computationally intensive task \(\tau_{n}\) waiting to be processed. The characteristics of \(\tau_{n}\) are represented as the parameter tuple \(\tau_{n}\triangleq(D_{n},C_{n},T_{n})\), \(D_{n}\) is the input data required by the task, \(C_{n}\) is the total computation required by the task (i.e., the number of CPU cycles) and \(T_{n}\) is the maximum tolerable latency of the task. The computing tasks of mobile users can be processed locally or offloaded to the edge servers for processing. For some special computing tasks that require larger resource consumption and longer task completion cycles, such as data backup for large enterprises, the tasks can be directly migrated to cloud servers for processing and are not considered in the model of this paper. The set of offloading policies is denoted as \(S=\left\{s_{n}\middle|s_{n}\in\left\{s_{n}^{l},s_{n}^{m}\right\}\right\}\), \(n\in N,m\in M\right\}\); \(s_{n}^{l},s_{n}^{m}\) denote local computation and edge server computation, respectively; and \(s_{n}^{l}=1\) if local execution is selected, and \(s_{n}^{m}=1\) if edge server execution is selected.  
The cloud is responsible for the scheduling of task offloading, making offloading decisions and matching the best edge server for task offloading. The mobile user uploads the data set \(I_{n}\), carrying the computational task information of user \(n\) to the cloud center, which collects the entire data set of computational tasks, polls the edge server for resources, retrieves them algorithmically and feeds the obtained resource allocation and offloading policy to the edge server and the end-user. To facilitate experiments and obtain useful data,  
Figure 1: Cloud-Edge collaboration system model.  
we consider quasi-static scenes or simple moving scenes [28; 29]. In mobile scenarios, the unloading task needs to be completed before the device is moved to another location. We set the computing power of terminal devices and edge servers to be stable during task processing, and it may change in different time periods.
# Design and Research of a Flexible Foot for a Multi-Foot Bionic Robot
## 5 Prototype Experiments and Results Analysis
### Omnidirectional Force Calibration
#### 5.2.3 Influence and Evaluation of Flexible Foot on Exercise Effect  
The performance of the same robot was compared when walking with rigid feet and walking with flexible feet. The experimental data curve of the recorded attitude angle curve is shown in Figure 16.  
As can be seen from Figure 16, when the robot walked with a rigid foot and a flexible foot, the trend of the attitude angle was about the same. When walking with a flexible foot, the amplitude of the attitude angle was significantly smaller than the amplitude when walking with a rigid foot.  
Figure 16: Attitude angle curve of robot walking. (**a**) change of roll angle; (**b**) change of pitch angle; (**c**) change of yaw angle.  
After statistical calculation, the minimum peak values of deflection angle, pitch angle, and roll angle of the flexible foot were 37.4%, 29.8%, and 7.8% less than those of the rigid foot, respectively. The results show that the trunk variation of the robot with the flexible foot was significantly smaller than that of the robot with the rigid foot, which was beneficial for the stability of the robot motion.  
Experiments were designed to study the foot-tip state of the robot walking in a tripod gait. The voltage values of the rigid foot and flexible foot were recorded separately. According to the calibration results in Section 3, the voltage values were converted into foot force. Two decimal numbers were reserved for data processing, as shown in Figure 17.  
It can be seen clearly from the graph that the peak force on the foot tip of the robot with the flexible foot was obviously smaller than that of the rigid foot. The average peak pressure of the rigid foot was about 3.47 N, whereas that of the flexible foot was 1.39 N, which was 59% less than that of the rigid foot. The experimental results show that the flexible foot designed in this paper had a very significant effect in cushioning ground impact.  
Through different simulation experiments, this section has clearly concluded that when the robot was in motion, the error between the acquired value of the foot tip force and the actual value reached 3.1%. Using the flexible foot, the peak value of the foot tip pressure was reduced by 59% and the maximum smoothness was increased by 37.4% compared with the rigid contact, indicating that the flexible foot structure improved the stability of the robot's motion process.
## 2 Related Work
### Path Planning
#### 2.2.2 Path Planning in High-Density Storage System  
In the field of warehousing logistics and their management, scholars have explored diverse methodologies to identify optimal solutions. [PERSON] et al. [30] proposed a bilevel optimization framework integrating job shop scheduling and collision-free route planning for high-density manufacturing systems. [PERSON] et al. [31] introduced a path-net graph-based routing and scheduling algorithm for automated storage and retrieval systems (AS/RS), balancing path costs with trunk rail and pallet lifter utilization. [PERSON] et al. [32] proposed a hierarchical framework that integrates centralized task assignment with a multi-agent reinforcement learning (MARL)-based distributed trajectory planning module to significantly improve task allocation efficiency and collision resolution in dynamic interactive scenarios. [PERSON] et al. [33] studied the scheduling optimization problem of the traditional AS/RS systems and proposed a storage location allocation method based on access frequencies. Although their work effectively reduces the average access time, its applicability is limited to single crane systems and cannot meet high concurrent requirements. [PERSON] et al. [34] proposed a modular 4D shuttle system that can operate simultaneously across multiple dimensions and greatly improve the system's throughput. However, the task scheduling complexity of this system increases significantly when presenting challenges in multi-vehicle conflict detection. [PERSON] et al. [35] analyzed the impact of vehicle scheduling strategies on system performance in multi-vehicle systems and proposed a distributed dispatching policy. Although decentralized control on an HDS system can improve the system's robustness, path conflicts are inevitable under high-density scenarios, which affects the overall shipping efficiency. [PERSON] et al. [36] explored the application of AI methods in an HDS system by using a deep reinforcement learning algorithm to optimize shipping path selection, demonstrating strong adaptability in dynamic environments. However, the computational cost during the training phase is relatively high, limiting the real-timeresponse of this method. All of the work mentioned above shows that although solutions exist, they are not tailored to topological representation or might be overly complex for HDS systems, rendering them impractical.  
In summary, three major challenges exist in current path planning for HDS systems: (1) lack of universal map representation methods that can effectively describe facility structures and traffic constraints while recording accurate spatial information and geometric properties; (2) high computational complexity and high solution sensitivity when handling complex path-planning problems; and (3) limited application feasibility due to insufficient consideration of transportation kinematic constraints in HDS systems. Therefore, efficient path planning algorithmic approaches for HDS systems are urgently needed, and our mesh-tree-based LH algorithm is a potential solution.
## 3 Open-Circuit Fault Diagnosis Strategy  
The proposed method is presented in Figure 3, and its core architecture consists of fault detection and location. Firstly, the IASMO is constructed through the hybrid logic dynamic model. Current characteristic values are extracted separately from the estimated currents and actual inverter outputs. These values construct fault detection variables and adaptive thresholds, enabling real-time fault detection via dynamic thresholding. Secondly, the initial location variables narrow down the fault range to the positive/negative level switches. Then, the fault location variables facilitate accurate identification of the faulty switches, achieving precise diagnosis in T\({}^{2}\)3L inverters.
# Distributed-Satellite-Clusters-Based Spectrum Sensing with Two-Stage Phase Alignment
## 6 Conclusions  
In this paper, we present a general framework for cooperative spectrum sensing exploiting the DSC system to enhance the sensing performance of weak signals. Based on this framework, a two-stage phase alignment method is conceived to eliminate the dynamics and randomness of phase deviations respectively, so as to improve the strength of the sensing signal. Especially in the second stage, a two-bit feedback algorithm suitable for a satellite high dynamic environment is proposed. We present a closed-form expression for the PDF of the sum of i.i.d. SR RVs. Based on the new PDF expression, we derived the analytical expressions of \(P_{d}\) for the considered SS_DSC system. Simulation results were provided to confirm the validity of the analytical results, which shows that the proposed scheme can significantly improve the \(P_{d}\) especially for sensing weak signals, whilst achieving a faster convergence.  
Conceptualization, Y.W. and T.H.; Data curation, Y.W.; Formal analysis, G.Z.; Investigation, Y.W. and G.Z.; Methodology, Y.W. and T.H.; Project administration, G.Z.; Resources, Y.W.; Software, Y.W.; Supervision, [PERSON] and G.Z.; Validation, X.D.; Visualization, Y.W.; Writing--original draft, Y.W.; Writing--review and editing, X.D. All authors have read and agreed to the published version of the manuscript.  
This work presented was partially supported by the National Science Foundation of China (No. 91738201,U21A20450 and 62171234), the National Key Laboratory of Science and Technology on Space Microwave (No. 6142411422118), the Jiangsu Province Basic Research Project (No. BK20192002), and the postgraduate research and practice innovation program of jiangsu province under Grant KYCX20_0708.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 10: The impact of time synchronization accuracy on cooperative SNR where \(\gamma=0\) dB.
###### Abstract
Additionally, the training of our proposed framework, DMRMs, is simpler than other previously introduced deep learning methods [3; 4; 9; 11; 12; 13], since it utilizes a pre-trained network along with a simple joint learning technique. In our proposed method, motion flow maps are integrated with a 3D deep neural network in order to capture deep motion flow information such as relative motion information of objects (spatial and time-dependent features of motion flow) and local variation of motion flow, which is further fused with spatial-time-dependent features for the joint learning of all features. In a recent study [10], the author has extracted the motion flow using the convolutional kernel method. The difference between our method and this study [10] is that we have utilized a 3D deep neural network to extract the relative motion and variational motion flow features from the dense motion flow maps, which helps our framework maximize the utilization of motion flow. In contrast, in the study [10], the author has extracted only motion flows using a convolutional kernel, and no relative motion information is extracted. Usually, motion flow maps are passed separately in the network, as in studies [9; 11], showing a relationship gap between appearance and motion features. Our proposed framework, DMRMs, seeks to address this gap without neglecting the context-dependency that is critical to anomalous event detection due to the subjective definition of anomalous events. Therefore, our work addresses multiple challenges of the task and helps mitigate the false alarm rate. The primary contributions of this study are summarized as follows:  
1. A pre-trained 3D ResNet is used, not only to extract global, context-based features of moving objects but also to extract the deep local motion-related information from their motion flow maps. The network is trained to learn the context-dependency or the relationship between spatial-time-dependent features and deep motion features using joint learning. A new objective function is proposed for DMIL using MRMs. Anomalous events are detected with their class (type) and with the localization of moving objects using motion flow maps. 3. The proposed framework has been tested on the two most recent and challenging datasets-UCF-Crime [14] and ShanghaiTech [5]. The results show that our method improves by 6.5% AUC on the UCF-Crime dataset compared with the state-of-the-method [14] and gives 68.5% AUC on the ShanghaiTech. The ablation study demonstrates the effectiveness of our proposed framework. The remainder of this paper is structured as follows. Section 2 presents a brief analysis of existing anomalous event detection algorithms. Section 3 presents the details of our proposed anomalous event detection algorithm. Experimentation and ablation studies are mentioned in Section 4. At last, Section 5 concludes the paper and provides future research directions.