esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Accident Number Prediction
#### 3.2.2 Quarterly Forecast  
Taking the quarterly accident data as the time series, the stationarity is better after the second-order difference. So the ARMA (7, 3) model is established, and the prediction result is shown in Figure 6. The relative error of dynamic prediction is 3.49%, and the static forecast is 4.01%, which is not much different from the monthly forecast, and the accuracy of static prediction is doubled. The reason may be that the quarterly data is accumulated through the month, which plays the role of \"peak shaving and valley filling\" to a certain extent, and its data volatility is more stable than that of monthly data, which helps to improve the accuracy of forecasting.  
After the trend decomposition, set the ARIMA (4, 2, 3) process prediction trend component, and set the ARIMA (2, 0, 1) process prediction remainder. The results are shown in Table 5, the correlation coefficient between the true value of the quarterly accident number and the predicted value is 0.957, the degree of fit is 0.915, and the relative error is only 0.39%, indicating that the obtained results after quarterly forecast plus are more ideal than the monthly forecast. This also reflects the necessity of quarterly rectification, due to the short time of a month and the large differences in the status of the environment and festivals, the number of accidents during this period will be accidental. Divided by quarters, the regularity of the predictive results is enhanced, and the predictive error is reduced, so that the predictive results have better guiding value. At the end of each quarter, it is necessary to analyze the accident situation of the current quarter in a timely manner and summarize the experience and lessons, strengthen safety education and supervision for subsequent work, and continuously improve safety.  
Figure 6: Quarterly forecast results of housing municipal engineering production safety accidents in 2019.
## 5 Simulation Experiment
### Analysis of MDR  
As illustrated in Figure 7, when the attacker's channel gain is equal to the legitimate user's channel gain, the value of MDR is lower due to higher training samples \(\rho\) of the legal training sample \(\rho\). Conversely, for different values of the attacker and legitimate user's channel gain, \(\rho\) has the highest points \((9,0.2916)\) and \((9,0.2735)\), respectively. This is due to the fact that a smaller SINR results in smaller CERs. Thus, it has a direct impact on the signal's quantization accuracy and poses diverse effects on the subsequent calculation of MDR. It can be observed from the two curves in Figure 7 that with different values of \(\rho\), the highest value is presented at \(x=9\). This is due to the fact that the Q-learning algorithm slowly adjusts the threshold in order to find the optimal threshold. As the threshold value  
Figure 5: When \(k=-3\), FAR at \(\rho=20\) and \(\rho=10\) is tested as the number of experimental rounds increases, and the Q table is updated 20 times per round.  
rapidly changes during \(x=1\) to 9, the MDR changes accordingly. The slope of the MDR progressively becomes smaller and smaller when the threshold approaches the optimal threshold. However, the slope of MDR tends to stabilize after 400 training sessions. For \(\rho=20\), the MDR remains stable at about 0.2735%, which is 6.2% lower than that for \(\rho=10\).  
Moreover, the impersonation attack in the simulation environment is unknown at the beginning, and the chosen threshold at the receiver end is small. However, as the environment is explored and the number of training sessions increases, the test threshold gradually rises. According to Equation (17), MDR is increased. This is due to the acquired experience from past learning. In this manner, the increasing number of training gradually stabilizes the optimal threshold. Nonetheless, the channel vector varies from experiment to experiment. As a result, the MDR ranges from 28.5% to 29.0%. Figure 8 reveals that the SARSA algorithm outperforms Q-learning by 4%-5% in terms of MDR. This is because the SARSA algorithm is more vigilant than the Q-learning algorithm. There is also a 10% chance of selecting the detection threshold randomly, which means that some illegal signals may pass undetected. Consequently, the MDR of the SARSA algorithm is slightly higher than Q-learning.  
Figure 6: The comparison of FAR based on SARSA and Q-learning.  
Figure 7: When \(k=-3\), the MDR at \(\rho=20\) and \(\rho=10\) is tested separately. The Q table is updated 20 times per round as the number of experimental rounds increases.
# Inter-Unit Consistency and Validity of 10-Hz GNSS Units in Straight-Line Sprint Running
## 2 Materials and Methods  
### Participants  
This study was approved by the Nanyang Technological University Institutional Review Board (IRB-2020-09-033). Thirteen active participants (4 males, 9 females) were recruited via convenient sampling [age 21.6 (1.6) years, height 170.6 (7.7) cm, body mass 63.1 (10.1) kg]. To be eligible for this study, participants must have been training with a sports team at least twice a week and had minimally a year of experience in the specified sport. Additionally, they were required to be injury-free and pain-free at the time of the study.
# Cloud Computing Based on Computational Characteristics for Disaster Monitoring
## 2 Related Work
### Cloud Computing for Remote-Sensing Data Analysis
The complex performance of remote-sensing observation, analysis, and application algorithms allows the effective investigation of global issues such as climate change, natural disasters, and other environmental issues. However, algorithms require high-performance computing systems for implementation. Scientists have used cloud computing technology for remote-sensing and GIS applications to quickly respond to events, such as marine leak detection and localization [24], real-time feature extraction and detection of rivers, roads, and major highways [38], large-scale underwater image segmentation [39], image denoising processing [40], and accurate clustering of remote-sensing images [41]. The implementation of cloud computing programming for remote-sensing image processing based on cloud computing architecture is closely related to the distributed storage of data. To achieve optimal throughput, it is recommended to combine the knowledge of cloud computing architecture with the characteristics of remote-sensing image algorithms to carefully design and optimize distributed parallel processes. However, the optimization of cloud computing programs is difficult for developers in the field of remote sensing. The existing cloud computing parallel processing system satisfies the application requirements of most current cloud computing-based remote-sensing image-processing algorithms. However, the following issues should be addressed. First, there are various data formats and multi-source heterogeneity in remote-sensing image data. This issue has been addressed in previous studies [34]. Secondly, cloud computing programming for remote-sensing images is tightly coupled with image-processing algorithms, and often can only be used for a specific algorithm, i.e., parallel strategies are not reusable. Therefore, there is a need to develop a new parallel processing model and environment for remote-sensing image processing.
## 1 Introduction
The first commercial airplane to incorporate a GLA system using ailerons is the Tristar L-1011 from the 1980s [25] after the successful implementation of GLA technology on C-5A [26]. The effectiveness of GLA systems consisting ailerons and spoilers were tested on Airbus A300 [27] and firstly implemented on Airbus A320 [28]. The implementation of a GLA system on the Airbus A300 was shown to mitigate gust load significantly [25]. A baseline wing of a transonic transport aircraft integrated with a GLA system can lead to a reduction in the direct operating cost of nearly 6% and fuel savings of 9% [29]. A 15% wing root bending moment was relieved by the GLA system during unsteady wind encounters on A320 [28]. Figure 1: Relationship among flow control, load control and energy efficiency. Currently, for gust load alleviation, active control flaps are deflected to create forces and moments to attenuate gust loads. Ailerons, elevators or spoilers are normally used as the control surfaces for gust load alleviation. Fluidic actuators, such as blowing or suction, synthetic jets and oscillating jets, have been studied for many decades in the field of active aerodynamic flow control. Most of the studies focused on changing the momentum balance in the boundary layer to achieve aerodynamic improvement, such as lift augmentation, drag reduction and stall delay. Recently, there have been renewed interest in fluidic actuators for their potential application for modern aircraft flight control. Being able to fly and control aircraft without conventional control surfaces (namely flapless control) is one of the targets for future aircraft design with benefits including fewer moving parts, possibly less weight [31], less maintenance and enhanced stealth characteristics [32]. Apart from the steady load control capability, the dynamic responses of these fluidic actuators for unsteady load control, such as gust load, are the key factor for successively replacing the traditional flaps for flight control. This paper focuses on an overview of the research progress of flow control on gust load alleviation. Gust load definitions and the types of gusts aircraft may be subjected to are introduced. Current gust load alleviation techniques are discussed, followed by a focus on the overview of the studies on gust load alleviation by fluidic actuators including the surface jet blowing and circulation control via Coanda effect. The effects on gust load alleviation through traditional flaps and fluidic actuators are compared and discussed.
# A Survey of Precision Formation Relative State Measurement Technology for Distributed Spacecraft
## 3 Analysis of the Characteristics of Relative State Measurement Technology
### Visible Light Vision Measurement Technology
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Advantages** & **Disadvantages** \\ \hline Monocular vision & Simple structure and small size & Lack of in-depth information \\ Stereo Vision & Acquisition of 3D information & Limited range and a complex algorithm \\ Scanning LiDAR & Easier calibration & Time-consuming \\ Flash LiDAR & Large detection distance range & Limited resolution and low accuracy at close range \\ TOF Camera & Low power consumption and compact design & Limited detection distance \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Advantages and disadvantages of visual sensors. light conditions, but it also has the advantages of low power consumption and a small structure. Its use must be planned according to the characteristics of the task [39]. Due to the lack of image depth in monocular vision, laser-ranging can be used for auxiliary measurement in practical solutions [43; 46]. According to the existing trend analysis, the visual composite relative measurement method can fundamentally boost the advantages and may become a promising research direction in autonomous relative measurement sensors for spacecraft formations [47].
# Evaluation of Forward Models for GNSS Radio Occultation Data Processing and Assimilation
## 2 Experiments
### Data, Collocation, and Quality Control
#### 2.1.2 Spatial and Temporal Collocation  
Experiment 1 aims to compare the BA from FY3D retrievals and calculated using Equation (2) from EC4 Dvar atmospheric profiles. To improve the accuracy of the comparison, we must ensure that the refractivity profiles of EC4 Dvar and FY3D are collocated.  
In terms of temporal collocation, we only selected the occultation soundings whose time is less than one hour to EC4 Dvar's four main synoptic hours, namely 00, 06, 12, and 18 UTC; that is, \(|T_{FY3D}-T_{EC4 Dvar}|\leq 1\) h. 5914 out of 17791 FY3D GNOS soundings in September 2019 satisfied the temporal collocation.  
In terms of spatial collocation, we used the tangent point of occultation profiles as its location to search its nearest EC4 Dvar profile. The profile with 137 levels on grids nearest to the tangent point was selected. In such a way, we assume the EC4 Dvar and the FY3D profiles are collocated. However, because a grid resolution of \(2.8^{\circ}\times 2.8^{\circ}\) is too large, the collocated profiles of EC4 Dvar may be far be far FY3D. We fixed it in quality control as explained in the next subsection. Figure 15 in Appendix C shows that FY3D soundings in experiment 1 generally have an evenly spatial and temporal distribution, which makes the mean (statistical values) feasible in the subsequent analysis.
## 4 Proposed Learning-Based Optimization Scheme
### DNN Architecture  
The structure of the proposed neural network consists of a basic multilayer perceptron (MLP). An MLP is a fully connected neural network that consists of one input layer, multiple hidden layers and one output layer. We chose the channel parameter \(\left|h_{k}\right|\) for \(k=1,2,3,\dots,K\) as the input to the DNN and the optimal solution \((P^{*},\tau^{*})\) as the output of the DNN. Thus, neurons in the input layer are equal to the number of users (i.e., \(K\)) in the WPCN, while neurons in the output layer are equal to \(K+1\). However, neurons in hidden layers and the number of hidden layers are hyperparameters in the DNN, and it is necessary to adjust them to obtain the best accuracy in the optimal solution. The output of every \(l\)th layer in the hidden layer can be defined as shown in Equation (1):  
\[q_{l}=g(w_{l}q_{l-1}+b_{l})\]  
where \(w_{l}\) and \(b_{l}\) are the weights and biases of the \(l\)th layer, respectively. Learning is the problem of finding the weights, \(\mathbf{w}\), within some feasible set that will lead to the optimal value of \((P,\tau)\). The initialization of the weights of neurons is very important, because the careful initialization of the network can speed up the learning process [20]. We used a truncated normal distributed random variable divided by the square root of the fan-in for each respective layer to initialize the weights of neurons. This method is the recommended initialization for a rectified linear unit (ReLU) activation function in order to avoid the gradient vanishing problem while optimizing the loss function.  
Here, \(g(.)\) indicates an activation function that applies to the output of every hidden layer. Activation functions add non-linear properties of the mapping function of input and learning variables. A DNN mainly performs the sum of products of inputs and their respective weights and executes activation function \(g(.)\), which is generated for every hidden layer output. An activation function mainly helps the network to learn non-linear complex mapping functions for the input and output. In the proposed scheme, we use the ReLU activation function for the output of every hidden layer.  
The ReLU in deep networks is easier to optimize, converges faster and is faster to compute [10, 14]. ReLU activation function outputs \(g(x)=max(0,x)\), _i.e._, _if_ \(x<0,\ g(x)=0\)_otherwise_\(g(x)=x\).  
Thus, the DNN architecture is a function \(f(.)\), parameterized by \(\mathbf{w}\), that learns the mapping relationship between \((P,\tau)\) and \(|h_{k}|\), which is defined as  
\[(P,\tau)=f\left(|h_{k}|;\mathbf{w}\right) \tag{11}\]
## 3 Experimental Results and Discussion
### Tension Distribution of Geogrid  
Figure 6 shows the tension distribution of geogrid, and the measurement points are presented in Figure 1c. As can be seen, the geogrid's maximum tension in both the transverse and longitudinal directions is found in the loading area. The tension increases with distance from the load center and at all loading levels. The tension in the transverse direction is greater than the tension in the longitudinal direction. The tension in the center of the geogrid increases significantly with the increase in load. However, in the A-7 and B-7 curves at the edge of the subgrade, it is not obvious that the deformation of the geogrid spreads from the center to the surrounding area. The tension at the axis in two directions is the largest, and the growth rate of tension at the axis is also greater than other parts as the load increases.  
The negative value in Figure 6 is due to the measurement point being located in the position of the soil between the piles. When the upper load is transmitted, the soil between the piles settles downward and the geogrid sinks downward, so the tension measurement point on the upper surface has a negative value. The geogrid above the pile cap has a  
Figure 5: Pile–soil stress ratio.  
Figure 6: Tension distribution of geogrid: (**a**) A-1–A-7; (**b**) B-1–B-7; (**c**) C-1–C-3.  
positive strain value due to the upward protruding arch of the geogrid above the pile cap in response to the pile's piercing action. It can be seen that the tension of the geogrid above the pile cap is generally greater than the soil between the piles, which indicates that the tension of the geogrid is related to the relative position of itself and the pile, and it also verifies the soil arch effect from the side. As the ground settles, the geogrid is stretched, and a portion of the geosynthetic's strength is therefore mobilized. Consequently, the geogrid acts as a tensioned membrane, and the resulting hoop tension will reduce the net pressure on the ground. The tensioned membrane effect (Figure 7) of the GRPS embankment is also playing a role.
## 3 Results  
### The Results of Butterworth Bandpass Filtering of the MMG Signal  
Taking the results of the raw signals of the biceps brachii during the curling exercise as an example, Figure 14 illustrate the results of Butterworth Bandpass Filtering of the MMG signal. There was a bandpass filter circuit on the signal amplification module of the sensor. The passband of the bandpass filter is 10-50 Hz [33]. After being processed by the Butterworth bandpass filter, the low-frequency and high-frequency noise in the original MMG signal are removed, which is well shown in the spectrum in Figure 14b. The Marginal Hilbert Spectrum is a two-dimensional representation derived from the Hilbert spectrum; it shows the total energy contribution of each frequency value. The Marginal Hilbert Spectrum helps us understand the energy distribution of different frequencies in a signal [34,35].
# Line Matching Based on Viewpoint-Invariance for Stereo Wide-Baseline Aerial Images
## 3 Line Matching for Image Pair on the Rectified Image Space  
The perspective viewpoint-invariant method proposed in this paper aims to eliminate the problem of large viewing angle distortion of two images under a wide baseline. As mentioned in the introduction, the effect of line matching is mainly related to a line segment's neighborhood information. Therefore, this paper selects one of the most outstanding line matching methods, which is open source and based on a line-juncture-line descriptor that is referred to as line-juncture-line (LJL) [21]. LJL only uses the line segments extracted from the images, and constructs the feature descriptor according to the information of their neighborhoods to realize the matching of two images. It uses two lines as a group to conduct matching, which intensifies the stability under different circumstances. A brief introduction of the LJL line matching method can be found below.  
The general idea of LJL is shown in Figure 4: Firstly, the line segments are extracted from stereo images using the line segment detector (LSD) method [26], then two adjacent line segments are selected in the first image. The intersection point of two line segments is found at a juncture point, and the two line segments and juncture are constructed into one LJL structure. Descriptors in the LJL structure neighborhood are constructed according to the gradient information. Then, the LJL structure in the second image is constructed in the same way. Making the LJL structures of the first image as a reference, the LJL structures in the second image try to match them one by one.
# Traffic-Predictive Routing Strategy for Satellite Networks
## 5 Route Maintenance
### Remote Maintenance Process  
Determine the route repair method based on the location of the break. Select the corresponding node for local repair based on the stability of the upstream and downstream nodes. If the stability of the upstream node at the break is greater than that of the downstream node, the upstream node initiates the route local repair process. Otherwise, the downstream node initiates the route local repair process.  
(1) The upstream node initiates the local repair process:  
As shown in Figure 4, the link between node \(V_{3}\) and node \(V_{4}\) is broken; hence, data packets will not traverse the route from \(V_{3}\) to \(V_{4}\). When the stability of node \(V_{3}\) is higher than that of node \(V_{4}\), the upstream node \(V_{3}\) initiates a local repair. At this moment, node \(V_{3}\) is searching for the next two-hop nodes in its routing table, specifically \(V_{5}\). As long as a route from \(V_{5}\) to \(V_{3}\) is identified, the route can be restored. Node \(V_{3}\) sends an RREQ packet to \(V_{5}\) through its neighbor node \(V_{6}\). After receiving the RREQ packet, node \(V_{5}\) replies with an RREP packet to \(V_{3}\). If node \(V_{3}\) receives the RREP, it means that the local route repair is completed. The repaired path is: \(V_{3}-V_{6}-V_{7}-V_{5}\). The repair path in Figure 4 is just one example; the route repair may also involve a route through node \(V_{4}\).  
(2) The downstream node initiates the local repair process:  
As shown in Figure 5, the link between node \(V_{3}\) and node \(V_{4}\) is broken; hence, data packets will not traverse the route from \(V_{3}\) to \(V_{4}\). When the stability of node \(V_{4}\) is higher than that of node \(V_{3}\), the downstream node \(V_{4}\) initiates a local repair. At this moment, node \(V_{4}\) is searching for the previous two-hop nodes in its routing table, specifically \(V_{2}\). As long as a route from \(V_{4}\) to \(V_{2}\) is found, the route can be restored. \(V_{4}\) sends an RREQ request packet to node \(V_{2}\) through neighbor node \(V_{8}\). Node \(V_{2}\) sends a response packet RREP back to node \(V_{4}\). If \(V_{4}\) receives the RREP packet, it means that the local route repair is completed. The repaired path is: \(V_{2}-V_{9}-V_{8}-V_{4}\). The repair path in Figure 5 is just one instance; the route repair may also involve a route through node \(V_{3}\).  
Figure 4: Example diagram of local repair on upstream nodes.