esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Because dementia is difficult to treat, the best way is to detect the prodromal stage; this can identify those at high risk of dementia and help to delay its onset. It is a well-known fact that gait has a high correlation with cognitive function. Considering that dementia starts with cognitive decline, investigating the association between cognitive decline and gait may contribute to the detection of elderly individuals at high risk of dementia and even the prevention of dementia. The purpose of the present study was to investigate the gait cycle and biomechanics of elderly people with and without cognitive decline. A three-dimensional motion analysis system was used, and older adults over 65 participated in this study. K-MoCA was used to assess cognitive function and, according to the results of a cognition function assessment, they were classified into two groups. Spatiotemporal variables, subdivisions of the gait cycle, joint angle, joint moment, joint power, and support moment were investigated. Significant differences between both groups appeared in the subdivisions of the gait cycle, and parameters of gait biomechanics were established. These results provide insight into the mechanism dictating the gait of elderly individuals with cognitive decline.  
os 2021 26 August  
In other words, slow gait speed is considered to be an identifying factor for cognitive impairment, including MCI and dementia [2; 13; 14; 15]. Furthermore, it has been reported that motoric cognitive risk syndrome (MCR), a state in which both subjective cognitive complaints and slow gait speed are present, increases the risk of dementia [16; 17]. In addition, the co-occurrence of MCI and a slow gait has a high risk of dementia [6]. Although gait speed is useful in predicting cognitive decline and a high risk of dementia, it still has a limitation in that it lacks specificity [10].  
Investigating the gait cycle and dynamic parameters may contribute to overcoming the insufficient specificity of gait speed and, furthermore, could serve as additional markers for predicting cognitive decline. In addition, it can help to assist in walking during daily life for elderly people who suffer from cognitive decline and can also provide clinical evidence for cognitive function interventions through gait training/exercise based on several pathological and medical imaging studies [7; 18]. To our knowledge, there are no studies examining the gait cycle and biomechanical parameters of gait in elderly individuals with cognitive decline.  
Therefore, the purpose of this study was to investigate the gait cycle and gait biomechanics of the elderly with and without cognitive decline by using three-dimensional motion analysis.
## 4 Results and Discussion
### Parameters Analysis
#### 4.2.1 Case 1--Uniformly Distributed Suction  
Figure 7 depicts the \(FS\) of the tunnel face, considering uniform matric suction, where \(\varphi_{b}\) represents a constant. Figure 7a delineates the correlation between the \(FS\) and the burial depth ratio C/D for scenarios with D = 10 m, \(u_{a}-u_{w}=0\), 25, and 50 kPa. Figures 7b and 7c, respectively, present the \(FS\) for different values of C/D and D. Figure 7d demonstrates the influence of the internal friction angle of the soil on the \(FS\) at varying matric suction levels.  
As depicted in Figure 7, it is evident that matric suction has a favorable impact on tunnel face safety. Figure 7a,b indicate that as C/D increases, the \(FS\) initially improves before reaching a plateau. Once the burial depth ratio C/D surpasses a specific threshold, the horned failure mechanism will become completely submerged underground, and the stability of the tunnel face will no longer be influenced by variations in C/D. In contrast to the scenario where suction is not taken into account, the maximum difference, following the consideration of uniform suction of 50 kPa, can reach up to 33.4%. Figure 7c,d indicate that, under the condition of C/D = 1, as the suction and pressure increase, the \(FS\) exhibits nearly linear growth. Additionally, smaller values of D prove advantageous for the stability of the tunnel face, and changes in D have a relatively limited effect on the influence of the \(FS\).  
Figure 6: Comparison between the results of the kinematically limited analysis (KLA) and FLAC 3D numerical simulation method.
# Posture Recognition Using Ensemble Deep Models under Various Home Environments
## 4 Experimental Results
### Experimental Results  
The computer used in the experiment had the following specifications: Intel i7-6850K central processing unit at 3.60 GHz, Nvidia GeForce GTX 1080 Ti, 64 GB of random-access memory, and Windows 10 64-bit operating system. The CNN models used for posture recognition in this study were VGG16, VGG19 [19], ResNet50 [20], DenseNet201 [21], InceptionResNetV2 [22], and Xception [23], which are pre-trained CNNs. A pre-trained CNN can be used for transfer learning when new data need to be classified efficiently. Most of the parameters used to extract the feature maps were fixed, and only the final FC layers and Softmax were trained. A simple CNN (the conventional method) was added to the experiment for performance comparison. The structure of the simple CNN is depicted in Figure 16. The simple CNN was trained by updating all the parameters, because it was not a pre-trained model. Here, the number of postures was the number of classes. The number of defined postures was 10: \"standing normal\", \"standing bent\", \"sitting sofa\", \"sitting chair\", \"sitting floor\", \"sitting squat\", \"lying face\", \"lying back\", \"lying side\", and \"lying crouched\". If a class of \"other images\" (e.g., background) was added to the 10 posture classes, then the number of total classes was 11. The training, validation, and testing data sets for the 11 classes consisted of 44,874, 7913, and 6934 images, respectively. Additionally, the 10 posture classes were divided into four categories: standing, sitting, lying, and lying crouched. If a class for \"other images\" was added to these four posture classes, the number of total classes was five. The performance for the other numbers of classes was measured through simple mapping of the trained model with the data of 11 classes. The simple CNN was trained with a batch size of 128, 50 epochs, and the RMSProp optimizer [41]. The other pre-trained models were trained via transfer learning with a batch size of 128, 30 epochs, and the Adadelta optimizer [42]. The highest accuracies without data augmentation under transfer learning were 78.35% for VGG19 and type 2 preprocessing with the posture data of 11 classes, 69.89% for VGG19 and type 2 preprocessing with the posture data of 10 classes, 88.86% for VGG19 and type 2 preprocessing with the posture data of five classes, and 88.50% for ResNet50 and type 1 preprocessing with the posture data of four classes. Table 3 presents the classification performance of the CNNs without data augmentation under transfer learning. Figure 17 shows the confusion matrix for VGG19 with type 2 preprocessing, which exhibited the best performance for the posture data of 11 classes without data augmentation under transfer learning. Tables 4-6 present the accuracies of the CNNs for the posture data of 10, 5, and 4 classes, respectively, without data augmentation under transfer learning. Figure 15: Data configuration for training, validation, and testing data set.
## 5 Experimental Results and Analysis
### Experimental Environment and Parameters  
We conducts algorithm training on a computer configured with an Intel Core i7-10875H processor, a memory of 16 GB, and a hard disk space of 1T to test the proposed algorithm. We use the NetworkX tool to get the cloud network collaborative satellite network topology. As the parameter settings shown in Table 1, the types of physical nodes are categorized as satellite nodes, cloud server nodes, and edge nodes. Different types of nodes have differences in the amount of resources they possess. Experimental data of 1000 virtual networks and 1 physical network are generated through the topology generation tool. The generated physical network consists of 200 nodes and 800 links. Among them, there are 30 satellite nodes, 50 cloud server nodes, and 120 edge nodes. The CPU resource capacity of satellite nodes is between 40 and 80 units, and the bandwidth resource capacity is between 40 and 100 units. The CPU resource capacity of cloud server nodes is between 120 and 200 units, and the bandwidth resource capacity is between 100 and 200 units. The edge node CPU resource capacity is between 30 and 80 units, and the bandwidth resource capacity is between 30 and 100 units. The virtual network randomly contains 6-30 virtual nodes when generated. The probability of connection between nodes is 50%. Virtual node resources are generated according to QoS requirements. For nodes with low computational requirements, node resources are randomly generated within 3-18 units. For nodes with medium and high computational requirements, the node resources are randomly generated within 20-40 and 50-90 units, respectively. Virtual link bandwidth resources are also generated according to QoS requirements. For links with low, medium, and high bandwidth requirements, bandwidth resources are randomly generated within 5-15, 25-55, and 65-100 units, respectively.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Network** & **Parameter** & **Value** \\ \hline \hline \multirow{7}{*}{Physical Network} & Node number & 200 \\ \cline{2-3}  & Satellite Node number & 30 \\ \cline{2-3}  & Cloud server Node number & 50 \\ \cline{2-3}  & Edge Node number & 120 \\ \cline{2-3}  & Preset CPU of satellite nodes & 40–80 \\ \cline{2-3}  & Preset CPU of cloud server nodes & 120–200 \\ \cline{2-3}  & Preset CPU of edge nodes & 30–80 \\ \cline{2-3}  & Link number & 800 \\ \cline{2-3}  & Preset bandwidth of satellite links & 40–100 \\ \cline{2-3}  & Preset bandwidth of cloud server links & 100–200 \\ \cline{2-3}  & Preset bandwidth of edge links & 30–100 \\ \hline \multirow{7}{*}{VNRs} & Numbers of VNRs & 2000 \\ \cline{2-3}  & Training set & 1000 \\ \cline{2-3}  & Testing set & 1000 \\ \cline{2-3}  & Node number of each VNR & 6–30 \\ \cline{2-3}  & Node connection probability & 0.5 \\ \cline{2-3}  & CPU requirement of nodes & 10–60 \\ \cline{2-3}  & Bandwidth requirement of links & 10–60 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental environment parameters.
## 3 Materials and Methods
### Evaluation Metrics
#### 3.5.3 Root Mean Squared Error (RMSE)  
RMSE is the square root of MSE, which provides an interpretable error value in the same unit as the target variable. RMSE is sensitive to large errors and is commonly used in forecasting applications to measure model performance.  
Root Mean Squared Error is given by [37]:  
\[RMSE=\sqrt{\frac{1}{n}\ \sum_{i=1}^{n}(F_{i}\ -A_{i}\ )^{2}}\]  
where n is the number of observations, F\({}_{i}\) is the forecasted value for observation i, and A\({}_{i}\) is the actual value for observation i.
## 2 Related Works
### Open Issues and Challenges  
The solutions that have been described here show that interconnectivity challenges can be solved with success in environments where experience and knowledge have already been accumulated and there are technological solutions that can be applied to them. However, there is still as significant number of open issues and challenges that remain to be solved at mainly two different areas:  
1. _Lack of usability in the environment of the application domain_. The solutions that have been described previously work successfully in environments that, despite being constrained or present important issues regarding computational capabilities, are usually no underwater environments or imply autonomous maritime vehicles. Therefore, its optimization to the application domain represented in the manuscript is likely to be suboptimal. What is more, there seems not to be efforts to port those developments to this environment, as they were never targeted to be used for autonomous maritime vehicles. 2. _Lack of developments for data level transmissions_. The solutions that have been described either focus on the transmission of information in underwater conditions at a different (and usually lower) layer, or when data level transmissions are taken into account, they are done at the application layer, rather than having a software development for a distributed system that will contain the information related to session and presentation levels. 3. _Lack of data about what should be part of the information that is transferred from/to the entities that take part in a communication_. Whereas there is a high consensus about the different kinds of information transferred in other constrained environments, such as Wireless Sensor Networks or the Internet of Things, the establishment of accurate criteria that define the specific data that have to be used in underwater transmissions (coordinates, vehicle speed, water temperature, composition, etc.) is yet to be defined. 4. _Lack of security options_. The security infrastructure offered by the studied proposals is often precarious or nonexistent. Usually, not even the option to send messages encrypted is available, which poses a threat to the whole system, that might not be secure enough to have it used. A way to guarantee secure data interchanges between ends of the communications must be guaranteed by any new protocol designed for the purpose of interchanging information at the data level in underwater environments. The main advantages and disadvantages that have been found in each of them, as well as the references where the bulk of the information has been gathered, have been summarized in Table 1.
## 6 Conclusions  
This paper proposes the DSEC method to deal with the imbalanced data problem of TSSP. Firstly, the effects of the imbalanced data problem on the decision boundary and the classification performance of TSSP are analyzed in detail. Then, the three-step DSEC method is proposed to handle this problem. Finally, the effectiveness is demonstrated on the NPCC 140-bus system. The conclusions are drawn as follows:  
1. The imbalanced data problem can seriously deteriorate the classification performance of TSSP.
2. Compared with traditional machine learning methods, the proposed DSEC method can significantly improve the TUR and GM of TSSP, with the TSR value still being kept at a high level.
3. Compared with state-of-the-art data-level methods, the proposed DSEC method has higher TUR and GM values. Furthermore, the rapidity of the DSEC method fully meets the requirement of online application of TSSP.
4. The proposed DSEC method maintains a high GM value under different IRs. Moreover, the higher the IR value is, the greater the advantage of DSEC method over traditional machine learning methods will have.  
[PERSON] and [PERSON] developed the idea of this research and performed simulation verification; [PERSON] collected and processed the data; [PERSON] and [PERSON] wrote this paper; [PERSON] and [PERSON] checked and polished this paper.  
This work was supported by Science and Technology Project of State Grid Sichuan Electric Power Company (No. 52199718001V).  
The authors would like to appreciate [PERSON] from South Western University of Finance and Economics for her efforts in checking and polishing this paper. Also we would like to thank [PERSON] from Xihua University for his help in this paper.  
The authors declare no conflict of interest.  
Figure 7: Relationship between IR and IGM.
# Occluded Pedestrian Detection Techniques by Deformable Attention-Guided Network (DAGN)
## 4 Experimental Results
### Discussion  
The experimental results and ablation study are described in Section 4. We evaluated the method of three popular datasets for autonomous driving to demonstrate the detection performance of occluded pedestrians in traffic scenes. Inspired by DCN [10], the deformable convolution technique was used to sample from non-rigid locations. We addressed the main bottleneck of the wide variation in the appearance of the person when occluded, by designing the deformable convolution network with an attention module. As a result, most of the occluded instances with the indistinguishable background could be successfully detected. Extensive experimental results showed that our combination of effective techniques, such as DCAM, CIoU loss, DIoU-NMS, and ALTM, can produce significantly improved results in \"occluded\" and \"all\" cases, when compared to those of several state-of-the-art methods.  
In future work, we will consider applying our proposed pedestrian detector with lightweight network architectures to make the detection be real-time, while keeping the good detection performance. The real-time detector can be applied for the mobile and embedded vision applications, SSD [16] and YOLOV3 [17].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Reasonable** & **Small** & **Occluded** & **All** \\ \hline SSD [16] & 13.1 & 23.5 & 46.0 & 29.6 \\ Faster R-CNN [18] & 10.1 & 19.6 & 38.1 & 25.1 \\ YOLOv3 [17] & 9.7 & 18.6 & 40.1 & 24.2 \\ Cascade R-CNN [31] & 6.6 & 13.6 & 31.3 & 19.3 \\ DAGN (ours) & 5.9 & 14.2 & 26.3 & 17.5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparisons with the state-of-the-art methods in terms of log average miss rate on the ECP test set. Red color values denote the best results.
# Ground Deformation Associated with Deep Excavations in Beijing, China
## 3 Characteristic Analysis of Deformation
### Factors Influencing Maximum Surface Deformation
#### 3.3.4 Support System Stiffness  
System stiffness is defined by [PERSON] et al. [8], and it is expressed as follows:  
\[K=\frac{E_{w}I}{r_{w}h^{4}} \tag{1}\]  
where \(I=\frac{i^{3}}{12}\), \(E_{w}\) is the wall's Young's modulus, \(I\) is second moment of inertia of the wall section, \(h\) is the average vertical prop spacing of a multi-propped support system, \(t\) is the wall thickness, and \(r_{w}\) is the unit weight of water that is introduced as a normalizing factor. The system stiffness (\(K\)) is a dimensionless factor covering the wall bending stiffness (\(E_{w}I\)) and prop spacing (\(h\)), implying that the behavior of the retaining system is similar to a structural beam resting upon several supporting points.  
In accordance with the principle of equal stiffness, the stiffness of the retaining wall is equivalent to that of the underground diaphragm wall, and it is expressed as follows:  
\[\frac{1}{12}(D+s)t^{3}=\frac{1}{64}\pi D^{4} \tag{2}\]  
where \(t=0.838D\sqrt[3]{\frac{1}{1+\beta}}\), \(D\) is the diameter of the bored pile, and \(s\) is the net distance between piles.  
Figure 10: Effect of the embedded depth ratio (EDR) on maximum ground surface settlement.  
Figure 11 shows the relationship between the stiffness and dimensionless maximum surface settlement. The data in the figure exclude the intermediate working conditions. The stiffness of the supporting system is mainly between 7000 and 11,000, up to 22,000, and it can be increased by decreasing prop spacing or increasing the number of internal struts. This is the main reason why excavations that are supported by bored piles are deeper than those supported by other systems, and bored piles can be applied to all soil types with relatively small dimensionless maximum surface settlements. The maximum surface settlement decreases as stiffness increases. Until the support system has achieved a certain stiffness (>20,000), the effect on reducing foundation pit deformation is not obvious when stiffness is increased, but the construction cost is significantly increased.
# Distributed Learning for Dynamic Channel Access in Underwater Sensor Networks
## 1 Introduction
* Through performance evaluations, we demonstrate that the performance difference between the proposed algorithm and the centralized algorithms is not that large, even though if it is implemented in a distributed manner. Moreover, it is identified that the performance of the proposed algorithm is much better than that of the random algorithm.
## 2 Lotka-Volterra Based Modeling of Competing Relationships  
### Lotka-Volterra Model  
The Lotka-Volterra model is an important model used to describe the relationship between predators and prey in an ecosystem, which is an extension and expansion of the Logistic model and has been widely used in the analysis of competitive and cooperative relationships within an ecosystem or between enterprises. Because the model can describe the interactions of different variables under the same environmental capacity and can reflect the relationship between the variables more intuitively, it has been applied to many fields such as economics and epidemiology and also has some applications in the study of transportation systems [13]. The basic form of this model is that the two populations are represented by \(x\) and \(y\), and the growth rates of natural growth of population 1 and population 2 are represented. The predation of population 2 on population 1 is considered, and the chance of an encounter between population 1 and population 2 and the growth rate of population 2 after predation are represented, so that the equations for the change in the number of populations 1 and 2 can be shown in Equations (1) and (2):  
\[\frac{dx(t)}{dt}=a_{1}x(t)-b_{1}x(t)y(t) \tag{1}\]  
\[\frac{dy(t)}{dt}=-a_{2}y(t)+b_{2}x(t)y(t) \tag{2}\]  
The combination of the above equations is the initial form of the Lotka-Volterra model, taking into account the growth-retarding effect of each population itself and the mutualistic symbiosis, mutualistic bias, and many other relationships between populations. The general form of the Lotka-Volterra model can be obtained as Equation (3):  
\[\left\{\begin{array}{l}\frac{dx(t)}{dt}=x(t)(a_{1}+b_{1}x(t)+c_{1}y(t))\\ \frac{dy(t)}{dt}=y(t)(a_{2}+b_{2}y(t)+c_{2}x(t))\end{array}\right. \tag{3}\]  
where \(x(t)\) is the size of population 1; \(y(t)\) is the size of population 2; \(a_{1}\) and \(a_{2}\) are the endowment growth rates of population 1 and population 2; \(b_{1}\) and \(b_{2}\) are the coefficients of intraspecific interactions of population 1 and population 2 affected by their own blockage; and \(c_{1}\) and \(c_{2}\) are the coefficients of interactions of population 1 and population 2 affected by the interaction of the other population.  
From the above relationship, it can be seen that when \(\frac{dx(t)}{dt}=\frac{dy(t)}{dt}=0\), the competition between population 1 and population 2 reaches equilibrium. The positive and negative coefficients of the interaction coefficients reflect the various relationships between the populations, as shown in Table 1.
## 4 Feature Extraction  
This section describes the descriptors used in this work: Local Phase Quantization (LPQ) and a granulometry-based descriptor. The rationale behind this choice is the following. Firstly, we chose LPQ because this operator is supposed to achieve a good performance when the images may be affected by blur, which is a noise that frequently occurs in this type of image due to the nature of the collection process, as we can see in the bottom right corner of Figure 1. Next, we decided to evaluate a granulometry-based descriptor [4; 5], supposing that both could have a high level of complementarity.